Construction and Application of a Knowledge Graph

remote sensing

Communication Construction and Application of a Knowledge Graph

Xuejie Hao 1 , Zheng Ji 2 , Xiuhong Li 1,*, Lizeyan Yin 3, Lu Liu 1, Meiying Sun 1, Qiang Liu 1 and Rongjin Yang 4

1 College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China; [email protected] (X.H.); [email protected] (L.L.); [email protected] (M.S.); [email protected] (Q.L.) 2 School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China; [email protected] 3 Institute of Computing, Modeling and Their Applications, Clermont-Auvergne University, 63000 Clermont-Ferrand, France; [email protected] 4 Chinese Research Academy of Environmental Sciences, No. 8, Da Yang Fang, An Wai, Chao Yang District, Beijing 100012, China; [email protected] * Correspondence: [email protected]; Tel.: +86-136-2116-6693

Abstract: With the development and improvement of modern surveying and remote-sensing technology, data in the ﬁelds of surveying and remote sensing have grown rapidly. Due to the characteristics of large-scale, heterogeneous and diverse surveys and the loose organization of surveying and remote-sensing data, effectively obtaining information and knowledge from data can be difﬁcult. Therefore, this paper proposes a method of using ontology for heterogeneous data integration. Based on the heterogeneous, decentralized, and dynamic updates of large surveying and remote-sensing data, this paper constructs a knowledge graph for surveying and remote-sensing applications. First,

data are extracted. Second, using the ontology editing tool Protégé, a knowledge graph mode level is constructed. Then, using a relational database, data are stored, and a D2RQ tool maps the data

Citation: Hao, X.; Ji, Z.; Li, X.; Yin, L.; from the mode level’s ontology to the data layer. Then, using the D2RQ tool, a SPARQL protocol Liu, L.; Sun, M.; Liu, Q.; Yang, R. and resource description framework query language (SPARQL) endpoint service is used to describe Construction and Application of a functions such as query and reasoning of the knowledge graph. The graph database is then used Knowledge Graph. Remote Sens. 2021, to display the knowledge graph. Finally, the knowledge graph is used to describe the correlation 13, 2511. https://doi.org/10.3390/ between the ﬁelds of surveying and remote sensing. rs13132511 Keywords: knowledge graph; surveying; remote sensing; knowledge visualization Academic Editor: Frédérique Seyler

Received: 5 June 2021 Accepted: 24 June 2021 1. Introduction Published: 26 June 2021 In 2012, Google officially proposed the concept of the knowledge graph, which aims to assist intelligent search engines [1]. After the knowledge graph was formally proposed, Publisher’s Note: MDPI stays neutral it was quickly popularized in academia and industry and was widely used in intelligent with regard to jurisdictional claims in published maps and institutional affil- search, personalized recommendation, intelligence analysis, anti-fraud and other fields. iations. Essentially, a knowledge graph is a semantic network and knowledge base with a directed graph structure that describes entities (concepts) and their relationships in the physical world in symbolic form. The knowledge graph is represented in the form of triples (Entity1- Relation-Entity2), where the nodes of the graph represent entities or concepts, and the edges represent the relationships between entities or concepts [2]. Copyright: © 2021 by the authors. Knowledge graphs are a new method of knowledge representation. In essence, the Licensee MDPI, Basel, Switzerland. semantic web is an early form of the knowledge graph, which is an abstract concept This article is an open access article distributed under the terms and that describes entities and relationships between entities in the objective world and is conditions of the Creative Commons also a networked knowledge base composed of entities, properties, and relationships. A Attribution (CC BY) license (https:// knowledge graph is a collection of concepts, entities, and their relationships in the abstract creativecommons.org/licenses/by/ physical world [3]. The knowledge graph has changed the traditional method of informa- 4.0/). tion retrieval. On the one hand, knowledge graphs describe the semantic and attribute

Remote Sens. 2021, 13, 2511. https://doi.org/10.3390/rs13132511 https://www.mdpi.com/journal/remotesensing Remote Sens. 2021, 13, 2511 2 of 19

relationship between concepts to reason about concepts through fuzzy string matching. Conversely, knowledge graphs display the structured knowledge of classification and arrangement to users through the grid graphic information display interface. Concurrently, knowledge graphs solve the problem of manual filtering of useless information, which has practical significance for an intelligent society [4]. Knowledge graphs can be divided into general knowledge graphs and domain knowledge graphs. Knowledge graphs used in surveying and remote sensing are domain knowledge graphs. To date, few studies have investigated knowledge graphs for surveying and remote sensing. Wang and others proposed a framework for remote-sensing interpretation of knowledge graphs [5]. Xie and others designed a framework for the construction of a large knowledge graph in the field of remote-sensing satellites [6]. Geoscience knowledge graphs are also used and have been studied in detail. Xu and others proposed the concept, framework, theory, and characteristics of geoscience graphs based on geoscience graphs and geoscience information graphs. Jiang proposed the process of constructing knowledge graphs and explored the key technology of geographic knowledge graphs [7]. Lu and others systematically reviewed the research progress on topics related to geographic knowledge graphs and analysed the key issues of current geoscience knowledge graph construction [8]. However, these studies all discuss the construction of knowledge graphs in theory and do not provide any real examples of constructing knowledge graphs. Based on previous research experience, this paper describes an example of constructing knowledge graphs for surveying and remote sensing. The construction of this knowledge graph can provide services for users of surveying and remote sensing, surveying and remote-sensing experts, and developers of surveying and remote-sensing software. Users can visualize knowledge through the knowledge graph and discover the relationship between knowledge more easily. Searches conducted through the knowledge graph improve the user’s search efficiency. Remote-sensing experts gain insights and discover new rules in the field of surveying and remote sensing through the inference function of the knowledge graph. Software developers can integrate the knowledge graph into the remote-sensing product e-commerce platform, which can not only improve search efficiency, but also accurately recommend products for users. The professional field of surveying and remote sensing is the ladder of social progress. With the development of social intelligence, it is significant to study professional development technology for intelligent surveying and remote sensing.

2. The Theoretical Basis for the Construction of Knowledge Graphs The system framework of a knowledge graph in the fields of surveying and remote sensing primarily refers to its construction mode structure, which describes the process of constructing a knowledge graph in the fields of surveying and remote sensing (Figure1 ). The process of knowledge graphing in the field of surveying remote sensing can be divided into two parts: mode level construction and data layer construction. The mode level is built on the data layer, and the factual expression in the data layer is standardized through the ontology library. The ontology is the conceptual template of the structured knowledge base. The knowledge base constructed through the ontology library has the advantages of a strong hierarchical structure and low redundancy. The data layer is composed of a series of knowledge entities or concepts. Knowledge is stored in units of facts, and the data layer expresses knowledge in the form of triples (Entity 1-Relation-Entity 2) or (Entity-Attribute-Attribute Value). The logical structure of the knowledge graph in the field of surveying and remote sensing is shown in Figure2. Remote Sens. 2021, 13, 2511 3 of 19 Remote Sens. 2021, 13, 2511 3 of 19 Remote Sens. 2021, 13, 2511 3 of 19

Figure 1.1. The framework of the knowledge graphgraph inin thethe ﬁeldsfields ofof surveyingsurveying andand remoteremote sensing.sensing. Figure 1. The framework of the knowledge graph in the fields of surveying and remote sensing.

Figure 2. The logical structure of the knowledge graph in the fields of surveying and remote sens- FigureFigure 2. TheThe logical logical structure of thethe knowledgeknowledge graphgraph ininthe the fields fields of of surveying surveying and and remote remote sensing. sensing. ing. 2.1. Design of Disciplinary Knowledge Graph Mode in the Field of Surveying and Remote Sensing 2.1. Design of Disciplinary Knowledge Graph Mode in the Field of Surveying and Remote Sens- 2.1. DesignThe mode of Disciplinary level construction Knowledge process Graph begins Mode fromin the theField application of Surveying domain and Remote of the knowl-Sens- ing ingedge graph, determines the knowledge scope of the ontology, and then constructs the The mode level construction process begins from the application domain of the ontology.The mode The construction level construction of the ontology process is begi the keyns from link inthe the application construction domain of the patternof the knowledge graph, determines the knowledge scope of the ontology, and then constructs knowledgelayer. The ontology graph, determines is an explicit the description knowledge of scope the shared of the conceptual ontology, modeand then and constructs is used to the ontology. The construction of the ontology is the key link in the construction of the theadd ontology. semantics The tosemantic construction web of pages the andontology describe is the the key relationship link in the between construction concepts of the [9]. pattern layer. The ontology is an explicit description of the shared conceptual mode and patternThe ontology layer. The is the ontology pattern is layer, an explicit conceptual description mode, of and the logical shared basis conceptual of the knowledge mode and is used to add semantics to semantic web pages and describe the relationship between isgraph. used Theto add mode semantics level of theto knowledgesemantic web graph pages in the and field describe of surveying the relationship and remote between sensing concepts [11]. The ontology is the pattern layer, conceptual mode, and logical basis of the conceptsuses ontology [11]. The to realize ontology the storageis the pattern and management layer, conceptual of knowledge. mode, and When logical constructing basis of the an knowledge graph. The mode level of the knowledge graph in the field of surveying and knowledgeontology, we graph. mainly The construct mode level a collection of the knowledge of concepts graph from in the the subject field of words surveying of remote- and remote sensing uses ontology to realize the storage and management of knowledge. When remotesensing sensing teaching uses materials. ontology The to relationshiprealize the storage is mainly and amanagement hierarchical relationshipof knowledge. between When theconstructing upper and an lower ontology, positions. we mainly Entity construct filling is a mainlycollection obtained of concepts from from structured the subject data constructing an ontology, we mainly construct a collection of concepts from the subject sources,words of and remote-sensing the ontology teaching database materials. is filled in Th a bottom-upe relationship manner. is mainly a hierarchical re- words of remote-sensing teaching materials. The relationship is mainly a hierarchical re- lationshipA domain between ontology the upper is a specializedand lower positions. ontology Entity that describes filling is conceptsmainly obtained in a specific from lationship between the upper and lower positions. Entity filling is mainly obtained from domainstructured (such data as sources, remote and sensing, the ontology meteorology, database environment, is filled in a etc.) bottom-up and the manner. relationship structured data sources, and the ontology database is filled in a bottom-up manner. betweenA domain concepts. ontology The domainis a specialized ontology ontology of surveying that describes and remote concepts sensing, in a specific as a kind do- A domain ontology is a specialized ontology that describes concepts in a specific do- ofmain unique (such ontology, as remote can sensing, clearly meteorology, describe the relationship environment, between etc.) and concepts the relationship belonging be- to main (such as remote sensing, meteorology, environment, etc.) and the relationship be- andtween concepts concepts. in thisThe domain.domain ontology The basic of principles surveying of and ontology remote construction sensing, asare a kind clarity, of tween concepts. The domain ontology of surveying and remote sensing, as a kind of objectivity,unique ontology, consistency, can clearly minimum describe coding the relationship deviation, and between minimum concepts ontology belonging constraints. to and unique ontology, can clearly describe the relationship between concepts belonging to and

Remote Sens. 2021, 13, 2511 4 of 19

Ontology construction uses a seven-step method. The steps of this method are as follows: (1) determine the professional field and category of the ontology; (2) examine the possibility of reusing existing ontology; (3) list the important terms in the ontology; (4) define the hierarchical relationship between classes; (5) define the properties of the class; (6) define the constraints between the properties; and (7) create an instance [10].

2.2. The Data Layer of the Knowledge Graph in the Field of Surveying and Remote Sensing 2.2.1. Data Layer Relational Structure Design In this article, the data extraction method is used to construct the data layer. When extracting related entities, the relationship between the entities in the field of surveying and remote sensing is first defined. The relationship between entities is mainly the upper– lower relationship and the non-upper–lower relationship. Research on non-subordinate relationships in the knowledge graphs focuses on two aspects: entity attributes and entity relationships. The non-subordinate relationship of entity attributes is mainly used for triples: entity-attribute-attribute value, where the attribute depends on the corresponding entity. Each attribute will have its corresponding attribute value. In the definition of entity relationships, there is always a direct or indirect relationship between entities. Through the relationship analysis between entities, the general relationship between the entities used is defined. The relationship between entities mainly includes the relationship of belonging, containing, etc. These relationships are common relationships among entities in the field of surveying and remote sensing.

2.2.2. Data Layer Construction The data layer construction process is based on unstructured data and uses manual, automatic, or semiautomatic techniques to extract knowledge from the data and store it in the database. Data acquisition is a key step in the construction of the data layer. The core of knowledge acquisition is how to automatically obtain knowledge elements of structured information, such as entities, relationships, and properties, from unstructured and semi-structured data sources. Typically, automatic or semi-automatic machine learning technology is used to extract entities, relationships, attributes, and other information about the knowledge graph from open multisource data [11]. Knowledge acquisition includes the extraction of entities, relationships, properties, etc. Entity acquisition is the process of automatically identifying called entities (knowledge points, type names, etc.) from text data sets. Relation extraction is the process of discovering semantic relationships between entities from data sources using methods such as machine learning. Attribute extraction is the process of extracting attribute information about entities from data sources. The difference between attribute extraction and relation extraction is not only to identify the attribute name of the entity but also to identify the attribute value of the entity. Therefore, most studies are based on rules for extraction. [12].

3. Construction of Knowledge Graphs Construction of knowledge graphs is the core content of this article. It includes ﬁve parts: data acquisition and storage, ontology construction and storage, ontology and database mapping, query and reasoning of knowledge graphs, and visualizing the knowledge graph on Neo4j.

3.1. Data Acquisition and Storage 3.1.1. Data Acquisition The data used in this article are from surveying and remote sensing. The data source is a textbook related to the ﬁeld of surveying [13] and remote sensing [14]. DeepDive (http: //deepdive.stanford.edu/, accessed on 6 May 2021) was used to extract semi-structured table data from unstructured text data [15]. DeepDive can extract structured data from unstructured data and perform a series of data processing steps to build a knowledge base and extract relationships. It is very good at handling data sources in different formats. Remote Sens. 2021, 13, 2511 5 of 19

(http://deepdive.stanford.edu/, accessed on 6 May 2021)was used to extract semi-struc- Remote Sens. 2021, 13, 2511 5 of 19 tured table data from unstructured text data [17]. DeepDive can extract structured data from unstructured data and perform a series of data processing steps to build a knowledge base and extract relationships. It is very good at handling data sources in dif- DeepDiveferent formats. has good DeepDive database has support, good databa supportsse support, PubMed supports and other Pu databasebMed and data other sources, data- andbase is data a data sources, source thatand hasis a been data processed source that in natural has been language. processed DeepDive in natural has established language. aDeepDive framework has to established standardize a the framework construction to st processandardize of the the knowledge construction base process system of and the allowsknowledge users base to design system their and own allows extractors users to and design markers their according own extractors to the knowledgeand markers base ac- thatcording needs to tothe be knowledge constructed. base The that relationship needs to be extraction constructed. process The ofrelationship DeepDive extraction is shown inprocess Figure of3. DeepDive The DeepDive is shown based in domain Figure text3. The knowledge DeepDive extraction based domain method text includes knowledge the followingextraction steps method [16]: includes the following steps [18]: (1)(1) DataData processing. processing. First, First, the the original original corpus corpus will will be beloaded. loaded. The The natural natural language language pro- cessingprocessing (NLP) (NLP) tag tagis added. is added. A Aset set of of cand candidateidate relationships relationships an andd the sparse featurefeature representationrepresentation of of each each candidat candidatee relationship relationship are are extracted. extracted. (2)(2) RemoteRemote supervision supervision of of data data and and rules, rules, and and th thenen various various strategies strategies will will be beused used to su- to pervisesupervise the the data data set set so sothat that we we can can use use machine machine learning learning to to learn the weightweight ofof thethe mode. mode. (3) Learning and inference: mode specification. Then, the advanced configuration of the (3) Learning and inference: mode specification. Then, the advanced configuration of the mode will be specified. mode will be specified. (4) Error analysis and debugging. Finally, we will show how to use DeepDive’s tags, (4) Error analysis and debugging. Finally, we will show how to use DeepDive's tags, er- error analysis and debugging tools. ror analysis and debugging tools.

FigureFigure 3.3. TheThe relationshiprelationship extractionextraction processprocess ofof DeepDiveDeepDive [15[17].].

TheThe extractionextraction processprocess ofof domaindomain knowledgeknowledge basedbased onon DeepDive:DeepDive: (1)(1) Experiment preparation (preparation(preparation beforebefore knowledgeknowledge extraction).extraction). First,First, DeepDiveDeepDive stores the involvedinvolved input,input, intermediateintermediate and outputoutput datadata inin aa relationalrelational database.database. DeepDive supports manymany databases,databases, suchsuch asas Postgres,Postgres, GreenplumGreenplum andand MySQL.MySQL. TheThe database used in this experiment isis Postgres.Postgres. (2)(2) Data processing. This part is divided into four steps: ①1 Loading Loadingthe the original originalinput input data. First, we convert the targettarget datadata intointo anan electronicelectronic texttext format.format. OnlyOnly thethe twotwo fieldsfields of document document id id and and document document conten contentt are are reserved. reserved. We Westore store the cleaned the cleaned data datain a comma-separated in a comma-separated values values (CSV) (CSV) format format file. Then file. Then import import the original the original text textdata datainto the into associated the associated database. database. We set We the set data the format data format of the of document the document storage storage in the inapp. the ddlog app. file. ddlog There file. are There two text are fields two textin the fields articles in the table, articles articles table, (id text, articles content (id text,text). contentThen, we text). put Then,the compressed we put the file compressed of the original file ofdata the into original the specified data into input the specified input folder and run the “DeepDive compile” command. Then, we execute the “DeepDive do articles” command, which imports the original data into the articles table of the associated database. At this time, we can query the imported raw data by executing the query command. 2 Adding NLP markups. We use the CoreNLP Remote Sens. 2021, 13, 2511 6 of 19

natural language processing system to add annotations to the original data. The steps of NLP are: first, the input original article is divided into sentences. The sentence is divided into words, and the part-of-speech tags, standard forms, dependency analysis and entity recognition tags of the words in the sentence are obtained. After NLP, some commonly used entities (person names, place names, etc.) can be marked. It is also necessary to ensure further entity identification of the data processed by NLP. The input are the data in the sentences table. The output are the marked data. Finally, we import the final marked data into the sentences_new table in the database. 3 Extracting candidate relation mentions. DeepDive proposes corresponding input and output interfaces, allowing users to design their entity or relationship extractors. Generally speaking, the SQL(Structured Query Language) statement is used as the input interface to extract data from the database; The output is the corresponding table in the database. We perform entity extraction on the data after entity recognition, and then establish the corresponding database table structure. 4 Extracting features for each candidate. First, we extract the feature description and store the feature in the func_feature table. The purpose is to use certain attributes or characteristics to represent each candidate pair. There is a library DDlib that can automatically generate features in DeepDive, which defines features that are not dependent on the domain. There are also many dictionaries in the DDlib library. These dictionaries contain words related to the correct classification of descriptions and relationships and are usually combined with domains and specific applications. We declare the extract_func_features function in app.ddlog. The input of this function includes the information of the two entities in the entity_mention table and the NLP results in the sentence where the two entities are located. The output is the two entities and their characteristics. (3) Distant supervision with data and rules. We will use remote supervision to provide noisy label sets for candidate relationships to train machine learning models. Gener- ally speaking, we divide the description method into two basic categories: mapping from secondary data for distant supervision and using heuristic rules for distant supervision [17]. However, we will use a simple majority voting method to solve the problem of multiple labels in each example. This method can be implemented in ddlog. In this method, first, we sum the labels (all −1, 0, or 1). Then, we simply threshold and add these labels to the decision variable table has_spouse. In addition, we also need to make sure that all the spouse candidates who are not marked with rules are not included in this table. Once again, we execute all the above. (4) Learning and inference: model specification. We need to specify the actual model that DeepDive will perform learning and inference. DeepDive will learn the parameters of the model (the weights of features and the potential connections between variables). Then we perform statistical inferences on the learned model to determine that the probability of each variable of interest is true. 1 Specifying prediction variables. In our experiment, we have a variable to predict the mention of each spouse candidate. In other words, we want DeepDive to predict the value of a Boolean variable for each mentioned spouse candidate to indicate whether the value is correct. DeepDive cannot only predict the value of these variables but also predict the marginal probability, that is, DeepDive’s confidence in each prediction. 2 Specifying features. We need to define the following: each has_spouse variable will be connected to the elements of the corresponding spouse_candidate row; We hope that DeepDive understands the weights of these elements from the data we remotely monitor; those weights of the element should be the same for the specific function of all instances. 3 Specifying connections between variables. We can use learning weights or given weights to specify the dependencies between predictors. In the experiment, we specify two such rules, which have fixed (given) weights. First, we define the asymmetric connection, that is, if the model considers that a person mentions p1 and another person mentions p2 as a spouse relationship in the sentence, then it should also consider the opposite. Remote Sens. 2021, 13, 2511 7 of 19

weights to specify the dependencies between predictors. In the experiment, we specify two such rules, which have fixed (given) weights. First, we define the asymmetric connection, that is, if the model considers that a person mentions p1 and another Remote Sens. 2021, 13person, 2511 mentions p2 as a spouse relationship in the sentence, then it should also con- 7 of 19 sider the opposite. The model should be strongly biased towards everyone mentioning a sign of marriage. Instead, we use negative weights for this operation. ④ Fi- nally, we want to Theperform model learning should be and strongly inference biased using towards theeveryone specified mentioning model. This a sign will of marriage. build a model basedInstead, on the we data use negativein the data weightsbase, learn for this the operation. weights, 4inferFinally, the weexpected want to perform or marginal probabilitieslearning andof the inference variables using in the the specified model model.and then This load will it build back a modelinto the based on the database. In this way,data inwe the can database, see the learn probability the weights, of the infer has_spouse the expected variable or marginal inferred probabilities of by DeepDive. the variables in the model and then load it back into the database. In this way, we can (5) Error analysis andsee debugging. the probability To ofaccurately the has_spouse analyze variable the inferredexperimental by DeepDive. results, we first declare a(5) scoreError or a analysisuser-defined and debugging. query sentence To accurately and define analyze the part the experimentalof the labeled results, we first declare a score or a user-defined query sentence and define the part of the data used for training. DeepDive uses this score to estimate the accuracy of the ex- labeled data used for training. DeepDive uses this score to estimate the accuracy periment. We declareof the these experiment. definitions We in declare deepdive.conf these definitions and define in deepdive.conf deepdive.calibra- and define deep- tion.holdout_fractiondive.calibration.holdout_fraction as 0.25. The test set is 75% as of 0.25. the Thelabeled test setdata, is 75% and of the the test labeled set data, and is used to verify thethe correctness test set is used of to the verify experimental the correctness results. of the Approximately experimental results. 1400 Approximately labeled data and approximately1400 labeled data 1000 and data approximately were used 1000for testing. data were The used graph for testing.on the left The graph on in Figure 4 is the correctthe left inrate Figure graph.4 is Unde the correctr ideal rate conditions, graph. Under the red ideal curve conditions, should the be red curve close to the blue calibrationshould be close line. to However, the blue calibrationthis is not line. the case. However, It may this be is caused not the by case. It may the sparseness andbe noise caused of by the the training sparseness data and of noisethe test of thedata. training The middle data of graph the test in data. The middle graph in Figure4 is the predicted number graph of the test set. The forecasted Figure 4 is the predicted number graph of the test set. The forecasted quantity map quantity map usually presents a “U” shape. The graph on the right in Figure4 is usually presents thea “U” predicted shape. probability The graph quantity on the graphright ofin theFigure entire 4 datais the set. predicted Among them, the probability quantityprediction graph dataof the falling entire in thedata 0.5–0.6 set. Among probability them, interval the prediction indicates that data there are still falling in the 0.5–0.6some probability hidden types interval of instances, indicates and the that features there of are DeepDive still some are insufficient hidden for these types of instances,instances. and the Thefeatures predicted of Deep data whoseDive are probability insufficient does for not fallthese at (0,instances. 0.1) or (0.9, 1.0) are The predicted datathe whose data to probability be extracted. does An importantnot fall at indicator (0, 0.1) or to improve(0.9, 1.0) the are quality the data of the system to be extracted. Anis toimportant re-speculate indicator the above to improve data and attributethe quality it to of the the probability system is interval to re- (0, 0.1) or speculate the above(0.9, data 1.0). and attribute it to the probability interval (0, 0.1) or (0.9, 1.0).

FigureFigure 4. The 4. test The set test correct set correct rate calibration rate calibration plot (Left plot), test (Left) set, predictiontest set prediction plot (Middle plot), (Middle), prediction prediction plot for all data sets (Rightplot ).for all data sets (Right).

3.1.2. Data Storage3.1.2. Data Storage The data of the knowledgeThe data of graph the knowledge is usually graph expressed is usually in expressedthe form inof thetriples, form repre- of triples, repre- senting the relationshipsenting between the relationship entities between or between entities entities or between and attribute entities and values. attribute In values.this In this article, the relationalarticle, database the relational MySQL database and graph MySQL database and graph Neo4j database are Neo4jused. areThe used. purpose The purpose of of using MySQL usingis to MySQLrealize isthe to mapping realize the mappingbetween betweendata and data ontology and ontology and andthen then to touse use existing existing tools to realizetools to data realize query data and query reason anding. reasoning. The purpose The purpose of using of Neo4j using Neo4jis to update is to update and and search data. Neo4jsearch data.can directly Neo4j can display directly the display query the results query resultsin the inform the formof graphs of graphs to re- to realize the function of knowledge visualization. Neo4j has local storage and data processing functions alize the function of knowledge visualization. Neo4j has local storage and data processing that are different from general databases, which can ensure high readability and integrity functions that are ofdifferent data. from general databases, which can ensure high readability and integrity of data. The extracted data are processed and stored in three tables, called “knowledge”, The extracted“knowledge_to_genre”, data are processed and “genre”,stored in which three are tables, imported called into “knowledge”, a MySQL database. The “knowledge_to_genre”,E-R(Entity and Relationship) “genre”, which diagram are imported is shown below into a (Figure MySQL5). database. The E- R(Entity Relationship) diagram is shown below (Figure 5).

RemoteRemote Sens. Sens. 20212021, 13, 13, ,2511 2511 8 of 19 8 of 19

FigureFigure 5. 5.E-R E-R diagram diagram of of data data stored stored in the in database.the database.

InIn the the entity entity table table called called “knowledge”, “knowledge”, each each knowledge knowledge point ispoint numbered, is numbered, the at- the at- tributetribute “knowledge_id” “knowledge_id” is created, is created, and the and attribute the at istribute used as is the used primary as the key. primary In the entity key. In the table called “genre”, each category is numbered, the attribute “genre_id” is created, and the entity table called “genre”, each category is numbered, the attribute “genre_id” is created, attribute is used as the primary key. In the relation table called “knowledge_to_genre”, the propertiesand the “genre_id”attribute andis “knowledge_id”used as the wereprimary set as thekey. foreign In keysthe of therelation entity tablestable called “genre”“knowledge_to_genre”, and “knowledge” tothe create properties the relationship “genre_id” between and genre“knowledge_id” and knowledge. were For set as the example,foreign keys the knowledge of the entity “collinear tables equation”“genre” an belongsd “knowledge” to the field ofto “surveying”; create the relationship “genre” be- istween a “class”. genre In and the “genre” knowledge. class, thereFor example, are two entities: the knowledge “surveying” “collinear and “remote equation” sensing”; belongs to “genre”the field means of “surveying”; the genre to which “genre” the knowledgeis a “class”. node In belongs.the “genre” A knowledge class, there node are belongs two entities: to“surveying” either the “surveying” and “remote genre sensing”; or the “remote-sensing” “genre” means genre. the genre to which the knowledge 3.2.node Ontology belongs. Construction A knowledge and Storage node belongs to either the “surveying” genre or the “remote- 3.2.1.sensing” Ontology genre. Construction There are two ways to construct ontologies: top-down and bottom-up. The ontology construction3.2. Ontology of Construction the open domain and Storage knowledge graph typically uses a bottom-up method to3.2.1. automatically Ontology extractConstruction concepts, types of concept, and relationships between concepts from theThere knowledge are two graph. ways Theto construct open world ontologies: is too complex top-down to be considered and bottom-up. in a top-down The ontology manner. As the world changes, the corresponding concepts are still growing. Most domain construction of the open domain knowledge graph typically uses a bottom-up method to knowledge graph ontology construction uses a top-down approach. On the one hand, the conceptautomatically and scope extract of the concepts, domain types knowledge of concept, graph and are fixed relationships or controllable between compared concepts from tothe the knowledge open domain graph. knowledge The open graph; world on the is other too complex hand, the to domain be considered knowledge in graph a top-down mustmanner. yield As high-accuracy the world changes, results. Currently, the corresponding domain knowledge concepts graphs are still are widelygrowing. used Most do- inmain voice knowledge assistants [18 graph]. These ontology domain knowledgeconstruction graphs uses can a meettop-down most user approach. needs while On the one ensuringhand, the accuracy. concept and scope of the domain knowledge graph are fixed or controllable comparedThis article to the uses open a top-down domain approach knowledge to construct graph; ontologies, on the and other the creationhand, the tool domain usesknowledge Protégé (graphhttps://protege.stanford.edu must yield high-accuracy, accessed results. on 6Currently, May 2021), domain an ontology knowledge editing graphs tool [19]. The specific creation process is described as follows. First, we create the ontology are widely used in voice assistants [20]. These domain knowledge graphs can meet most class and two classes of knowledge and genre. Note that all classes are subclasses of “Thing”,user needs and while all classes ensuring must be accuracy. mutually exclusive; an instance can only be one of the two classes.This Second, article the uses relationship a top-down between approach the classes to construct (i.e., the object ontologies, properties) and is the created. creation tool Thisuses articleProtégé created (https://protege.stanford.edu, the object attribute “belong_to” accessed (We classify on 6 May the knowledge 2021), an extractedontology editing fromtool [21]. the “Surveying”The specific textbookcreation intoprocess the “Surveying”is described class.as follows. We define First, the we relationshipcreate the ontology betweenclass and this two knowledge classes andof knowledge the class “surveying” and genre. as “belong_to”.Note that all Similarly, classeswe are classify subclasses of the“Thing”, knowledge and extractedall classes from must the be “Remote mutually Sensing” exclusive; textbook an asinstance “Remote can Sensing” only be class. one of the Wetwo also classes. define Second, the relationship the relationship between this between knowledge the classes and the (i.e., class the “Remote object Sensing” properties) as is cre- “belong_to”) to indicate that a certain knowledge point is in a certain field (type). Therefore, ated. This article created the object attribute “belong_to” (We classify the knowledge ex- its attribute “domain” is defined as the class “knowledge”, and its attribute “range” is thetracted class from “genre”. the “Surveying” “Domain” indicates textbook which into the class “Surveying” the attribute class. belongs We to. define “Range” the relation- representsship between the valuethis knowledge range of the and attribute, the class which “surveying” defines the as inverse “belong_to”. of this attribute Similarly, as we clas- “belong_to”.sify the knowledge Thus, ontology extracted describes from reasoning the “Remot rulese forSensing” knowledge textbook reasoning. as “Remote The class, Sensing” objectclass. properties,We also define and data the propertiesrelationship are shownbetween below this (Figure knowledge6). For and example, the class in resource “Remote Sens- descriptioning” as “belong_to”.) framework(RDF) to indicate data, the that knowledge a certain pointknowledge “electromagnetic point is in waves” a certain belongs field (type). toTherefore, the class ofits “remote attribute sensing”. “domain” When is inquiring, defined as the the knowledge class “knowledge”, point “electromagnetic and its attribute wave”“range” can is alsothe beclass found “genre”. in the class“Domain” “remote indicates sensing”. which Finally, class class the properties attribute (data belongs to. “Range” represents the value range of the attribute, which defines the inverse of this attribute as “belong_to”. Thus, ontology describes reasoning rules for knowledge reasoning. The class, object properties, and data properties are shown below (Figure 6). For example, in resource description framework (RDF) data, the knowledge point “electromagnetic waves” belongs to the class of “remote sensing”. When inquiring, the knowledge

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point “electromagneticpoint “electromagnetic wave” can also wave” be canfound also in be the found class in “remotethe class sensing”.“remote sensing”. Finally, Finally, class propertiesproperties)class (data properties properties) are created (data are properties) and created are similar andare created toare object similar and properties. are to similarobject Concurrently, properties.to object properties. ProtConcur-égé alsoConcur- has rently, Protégéarently, also visual has Protégé display a visual also function displayhas a to visual show function display the structure to function show of the theto show ontology.structure the structure Theof the ontology ontology. of the structure ontology. is The ontology structureshownThe ontology below is shown (Figurestructure below7). is shown (Figure below 7). (Figure 7).

Figure 6. Construction of ontology class (left), object properties (middle), and data properties Figure 6.6.Construction Construction(right). of of ontology ontology class class (left (left),), object object properties properties (middle (middle),), and data and properties data properties (right). (right).

FigureFigure 7.7. VisualVisual displaydisplay ofof ontologyontology structure.structure.

3.2.2. Ontology Storage Figure 7. Visual 3.2.2.display Ontology of ontology Storage structure. The ontology storage mode in this article is the Relational Database Management The ontology storage mode in this article is the Relational Database Management System (RDBMS). The principle of RDBMS is to map the ontology to one or more tables, 3.2.2. OntologySystem Storage (RDBMS). The principle of RDBMS is to map the ontology to one or more tables, and then divide it into several modes according to the mapping mode, such as horizon- and then divide it into several modes according to the mapping mode, such as horizontal The ontologytal storage, storage decomposition mode in this storage, article verticalis the Relational storage and Database hybrid storage. Management As the storage storage, decomposition storage, vertical storage and hybrid storage. As the storage mech- System (RDBMS).mechanism The principle and data of management RDBMS is capabilitiesto map the of ontology relational to databases one or aremore relatively tables, mature, anism and data management capabilities of relational databases are relatively mature, re- and then dividerelational it into several database modes storage according is widely usedto the in mapping many storage mode, methods. such as horizontal lational database storage is widely used in many storage methods. This article uses the RDF and web ontology language (OWL) to describe ontolo- storage, decompositionThis articlestorage, uses vertical the RDF storage and web and ontology hybrid storage.language As (OWL) the storage to describe mech- ontologies gies [20]. RDF is used to represent any resource information and describes resources anism and data[22]. management RDF is used capabilities to represent of any relational resource databases information are and relatively describes mature, resources re- through lational databasethrough storage the is mode widely of attribute-attribute used in many storage value [21 methods.]. OWL is used to express the relationship betweenthe mode classes, of attribute-attribute the constraints of value the set [23]. cardinality, OWL is theused equality to express relationship, the relationship the attribute be- This articletype,tween uses and classes, the the RDF characteristics the and constraints web ontology of of the the attribute set language cardinality, [22]. Compared(OWL) the equality to withdescribe relationship, other ontologies ontology the attribute descrip- [22]. RDF is usedtiontype, to languages, andrepresent the characteristics OWL any hasresource better of the descriptioninformation attribute ability[24]. and Compared anddescribes more with description resources other ontology vocabulary.through descrip- The the mode of attribute-attributedescriptiontion languages, vocabulary OWL value has expands better[23]. OWLdescription the reasoning is used ability and to queryexpressand more capabilities the description relationship of the vocabulary. ontology. be- The tween classes, thedescription constraintsThe ontology vocabulary of isthe constructed setexpands cardinality, the and reasoning stored the equality using and query the relationship, ontology capabilities editing the of theattribute tool ontology. Prot égé . It type, and the characteristicscannotThe only ontology construct of the is constructedattribute and operate [24]. and the Compared stored ontology usin but gwith the also otherontology visually ontology editing display descrip- tool the generatedProtégé. It cannot only construct and operate the ontology but also visually display the generated tion languages,ontology, OWL has including better description the display of ability the hierarchical and more relationship description between vocabulary. ontology The concepts, ontology, including the display of the hierarchical relationship between ontology con- description vocabularyas well as theexpands visual displaythe reasoning of ontology and conceptual query capabilities entities, entity of the relationships, ontology. and entity cepts, as well as the visual display of ontology conceptual entities, entity relationships, The ontologyattributes. is constructed When the ontologyand stored is createdusing usingthe ontology the ontology editing description tool Protégé. language It OWL andand Protentityég éattributes., semiautomatic When constructionthe ontology can is created be achieved. using Protthe éontologygé can also description reason about lan- cannot only construct and operate the ontology but also visually display the generated theguage ontology OWL basedand Protégé, on the hierarchicalsemiautomatic relationship construction of the can ontology, be achieved. with the Protégé help ofcan Jena’s also ontology, includingqueryreason mode, theabout display andthe ontology can of also the based realizehierarchical on the the editing hierarch relationship operationical relationship between of the ontology of ontology the ontology, in the con- RDF with and the cepts, as well OWLashelp the of languages visual Jena’s querydisplay [23]. mode, of ontology and can alsoconceptual realize the entities, editing entity operation relationships, of the ontology in and entity attributes.the RDF When and OWL the languagesontology [25].is created using the ontology description language OWL and3.3. Protégé, Ontology semiautomatic and Database Mapping construction can be achieved. Protégé can also reason about the3.3. ontology OntologyTwo standards basedand Database on to convertthe Mapping hierarch the structuredical relationship data of of the the relational ontology, database with the into RDF help of Jena’s formatqueryTwo datamode, standards have and been can to developedconvert also realize the by structured thethe RDB2RDF editing data operationof studio the relational of W3C. of the Thedatabase ontology process into isin RDF applied for- the RDF and OWLbymat the datalanguages D2RQ have tool been [25]. [24 developed]. by the RDB2RDF studio of W3C. The process is applied by the D2RQ tool [26]. 3.3. Ontology and Database Mapping Two standards to convert the structured data of the relational database into RDF format data have been developed by the RDB2RDF studio of W3C. The process is applied by the D2RQ tool [26].

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The first standard is direct mapping, which is defined by the rule that tables in the database are classes in the associated ontology. For example, there are 3 tables for the data The first standard is direct mapping, which is defined by the rule that tables in the stored in MySQL. After mapping, the ontology has 2 classes instead of the 3 classes pre- database are classes in the associated ontology. For example, there are 3 tables for the dataviously stored defined. in MySQL. The Aftercolumns mapping, of tables the ontologyare attributes, has 2 classesand the instead rows ofare the instances. 3 classes The con- previouslytent in each defined. cell of tables The columns is text. ofIf a tables certain are column attributes, in the and cell the is rows a foreign are instances. key, then it will Thenot contentbe able into eachmap cell the ofdata tables in the is text. database If a certain to the column defined in ontology. the cell is In aforeign this case, key, the short- thencomings it will of not direct be able mapping to map theare datamarked. in the In database response to theto this defined defect, ontology. the RDB2RDF In this studio case,proposed the shortcomings R2RML and of allows direct mappingusers to flexibly are marked. edit and In response set mapping to this rules. defect, This the mapping RDB2RDFalso provides studio the proposed ability R2RML to view and existing allows usersrelational to flexibly data edit in the and RDF set mapping data mode, rules. which is Thisrepresented mapping by also mapping provides the the structure ability to selected view existing by the relational customer data and in the the target RDF data vocabulary. mode, which is represented by mapping the structure selected by the customer and the R2RML mapping is an RDF graph and is recorded in Turtle syntax. R2RML supports dif- target vocabulary. R2RML mapping is an RDF graph and is recorded in Turtle syntax. R2RMLferent supportstypes of differentmapping types implementations. of mapping implementations. The processor The can processor provide can virtual provide SPARQL virtualprotocol SPARQL and resource protocol anddescription resource framewor descriptionk frameworkquery language query language(SPARQL) (SPARQL) endpoints, gen- endpoints,erate RDF generate dumps, RDF or provide dumps, orlink provide data interf link dataaces interfaces on the mapped on the mapped relational relational data [27]. The dataspecific [25]. mapping The specific diagram mapping is diagramshown below is shown (Figure below 8). (Figure 8).

FigureFigure 8. 8.Schematic Schematic diagram diagram of ontology of ontology to database to database mapping. mapping.

3.4.3.4. Query Query and and Reasoning Reasoning This article describes how to use D2RQ to setup a SPARQL service endpoint and use This article describes how to use D2RQ to setup a SPARQL service endpoint and use query operations in the browser. A SPARQL endpoint provides a service that is compliant withquery the operations SPARQL protocol. in the browser. Through theA SPARQL default or en defineddpoint mapping provides file, a service RDF data that can is be compliant queriedwith the in SPARQL the rules ofprotocol. RDF. In otherThrough words, the todefault complete or defined the final mapping query, D2RQ file, converts RDF data can be thequeried SPARQL in querythe rules into of an RDF. SQL statementIn other basedwords, on to the complete mapping the file final and then query, returns D2RQ the converts resultthe SPARQL to the user query [26]. into an SQL statement based on the mapping file and then returns the resultThe to following the user steps[28]. are executed: (1) start the D2R server; (2) enter the browser to start theThe SPARQL following endpoint; steps are and executed: (3) enter the(1) SPARQLstart the statement D2R server; to execute (2) enter the the query. browser to Concurrently,start the SPARQL operations endpoint; can also and be queried(3) enter by th writinge SPARQL Python statement scripts. The to third-partyexecute the query. library SPARQL Wrapper of Python can easily interact with endpoints. Concurrently, operations can also be queried by writing Python scripts. The third-party Using D2RQ to open the endpoint service has two disadvantages: it does not support publishinglibrary SPARQL RDF data Wrapper directly toof thePython network can througheasily interact the endpoint with andendpoints. does not support the inference.Using D2RQ To solve to theseopen problems,the endpoint certain service components has two of disadvantages: Fuseki, Jena, and it thedoes tuple not support databasepublishing (TDB) RDF in Apache data directly Jena are to investigated the network experimentally. through the Fuseki endpoint is a SPARQL and does server not support providedthe inference. by Apache To solve Jena, whichthese problems, is primarily certain run as acomponents web application of Fuseki, or as an Jena, embedded and the tuple server.database Jena (TDB) provides in resourceApache descriptionJena are investigated framework schemaexperimentally. (RDFS). In Fuseki the case is of a aSPARQL singleserver machine, provided storage by Apache layer technology Jena, which can provide is primarily high RDF run storage as a web performance application [27]. or as an Next,embedded we must server. define Jena reasoning provides rules resource and conduct description knowledge framework reasoning. schema (RDFS). In the case of a single machine, storage layer technology can provide high RDF storage performance [29]. Next, we must define reasoning rules and conduct knowledge reasoning.

3.5. Visualizing the Knowledge Graph on Neo4j Based on the literature [2], the knowledge graph has basic query and reasoning functions; however, the visualization function of the knowledge graph has not been reflected.

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3.5. Visualizing the Knowledge Graph on Neo4j Remote Sens. 2021Based, 13, 2511 on the literature [2], the knowledge graph has basic query and reasoning11 of 19 functions; however, the visualization function of the knowledge graph has not been reflected. Therefore, the graph database Neo4j is used to visualize the knowledge graph [30]. Neo4j provides3.5. Visualizinggood thevisualizations Knowledge Graph of on Neo4jknowledge graphs. The specific implementation processBased and visualization on the literature are [2], shown the knowledge below. graph The process has basic of query generating and reasoning the func- knowledge graph istions; as follows. however, the visualization function of the knowledge graph has not been reflected. First, table dataTherefore, are converted the graph into database the form Neo4jat is of used CSV. to visualizeThese files the knowledge are then graphmoved [28 to]. Neo4j provides good visualizations of knowledge graphs. The specific implementation process the import directoryand of Neo4j visualization because are shownNeo4j below.defaults The to process open of files generating in the theimport knowledge directory. graph is as After starting the database,follows. the Cypher language [31] is used to import data, and the construction mode of theFirst, node table is data created are converted (k: knowledge into the format {name: of CSV. “absolute These files orientation”}). are then moved to the This statement createsimport a node directory with of a Neo4j knowledge because Neo4jlabel, defaults and this to opennode files has in a the name import attribute, directory. After starting the database, the Cypher language [29] is used to import data, and the construction an attribute value ofmode “absolute of the node orientation”, is created (k: and knowledge a variable {name: name “absolute k. The orientation”}). Cypher sentence This statement for importing structuredcreates data a node entity with data a knowledge into the label, database and this is nodeCode has 1. aThe name result attribute, of Code an attribute 1 is shown in Figure 9.value of “absolute orientation”, and a variable name k. The Cypher sentence for importing structured data entity data into the database is Code 1. The result of Code 1 is shown in CodeFigure 1 Entity9. Data Imported Based on Cypher Language

LOAD CSV WITH HEADERSCode FROM 1 Entity “file Data:///knowledge.csv” Imported Based on Cypher AS line Language MERGE (k:knowledge{id:line.knowledge_iLOAD CSV WITH HEADERSd, name:line.knowledge_name, FROM “ﬁle:///knowledge.csv” AS line MERGE (k:knowledge{id:line.knowledge_id,genre:line.knowledge_genre}) name:line.knowledge_name, genre:line.knowledge_genre})

Figure 9. PartFigure of the 9. Partvisualization of the visualization shows showsthe result the result of importing of importing entities entities into thethe database. database.

The name of the imported file is “knowledge.csv”, and the label is “knowledge”. The name of theAttributes imported and attribute file is values“knowledge.csv”, correspond to row and and the column label data is in“knowledge”. the table. The imports Attributes and attributeof nodes values and relationships correspond are to similar. row and The Cyphercolumn sentence data in for the importing table. structuredThe imports of nodes andrelational relationships data into the are database simi islar. Code The 2. TheCypher result ofsentence Code 1 is shownfor importing in Figure 10 . structured relational data into the database is Code 2. The result of Code 1 is shown in Code 2 Relational Data Imported Based on Cypher Language Figure 10. LOAD CSV WITH HEADERS FROM “file:///knowledge_to_genre.csv” AS line MATCH (from:knowledge{id:line.knowledge_id}),(to:genre{id:line.genre_id}) Code 2 RelationalMERGE Data (from)-[r:belong_to{Relation:line.knowledge_to_genre}]->(to)Imported Based on Cypher Language LOAD CSV WITH HEADERSThe name FROM of the “file:///kno imported filewledge_to_genre.csv” is “knowledge_to_genre”. AS The line partial MATCH result of the (from:knowledge{id:line.knowledge_id}),(to:genre{id:line.genre_id})imported visualization is shown below (Figure 11). In this picture, the two red nodes, MERGE (from)-[r:belong_to{Relation“Surveying” and “Remote Sensing”,:line.knowledge_to_genre}]->(to) represent the class of knowledge, and the blue node represents knowledge.

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Figure 10. Part of the visualization shows the result of importing relationships into the database.

The name of the imported file is “knowledge_to_genre”. The partial result of the imported visualization is shown below (Figure 11). In this picture, the two red nodes, “Sur- veying” and “Remote Sensing”, represent the class of knowledge, and the blue node rep- FigureresentsFigure 10. 10. knowledge.Part Part of of the the visualization visualization shows shows the result the result of importing of importing relationships relationships into the database. into the database.

FigureFigure 11. 11.Part Part of theof the visualization visualization of the knowledgeof the knowledge graph in thegraph field in of the surveying field of and surveying remote sensing. and remote sensing. The database contains 1024 nodes, which belong to the two classes of nodes “knowledge” and “genre”. The nodes in the knowledge graph are the primary components of the knowledge graph, primarily the knowledge of surveying and remote sensing. The Figure 11. Part of the visualization of the knowledge graph in the field of surveying and remote sensing.

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Remote Sens. 2021, 13, 2511 13 of 19 The database contains 1024 nodes, which belong to the two classes of nodes “knowledge” and “genre”. The nodes in the knowledge graph are the primary components of the knowledge graph, primarily the knowledge of surveying and remote sensing. libraryThe library also containsalso contains 1295 relationships,1295 relationship whichs, which represent represent the relationship the relationship between between nodes innodes the class in the “knowledge” class “knowledge” and nodes and innodes the class in the “genre”. class “genre”. For example, For example, “belong_to” “belong_to” is the belongingis the belonging relationship relationship between between knowledge knowledge and type. and The type. library The library also contains also contains attribute at- information,tribute information, such as such the “genre” as the “genre” and “name” and “name” of the “knowledge” of the “knowledge” node. Certainnode. Certain basic informationbasic information for creating for creating the database the database is shown is shown below below (Figure (Figure 12). 12).

FigureFigure 12.12.Part Part ofof thethe informationinformation of of the the knowledge knowledge graph graph knowledge knowledge base base in in the the ﬁeld field of of surveying survey- anding remoteand remote sensing. sensing.

4.4. ApplicationApplication AnalysisAnalysis of of the the Knowledge Knowledge Graph Graph ApplicationApplication analysis analysis of of knowledge knowledge graph graph is is an an important important means means to verifyto verify the the value value of knowledgeof knowledge graph. graph. This This article article veriﬁes verifies the applicationthe application value value of the of knowledge the knowledge graph graph from thefrom two the application two application scenarios scenarios of “domain of “domain relevance relevance analysis” analysis” and “knowledge and “knowledge reasoning rea- insoning the ﬁeld in the of surveyingfield of surveying and remote and sensing”remote sensing” in the smart in the campus smart campus platform. platform.

4.1.4.1. DomainDomain RelevanceRelevance AnalysisAnalysis In many fields, there are various degrees of correlation. For example, in education, In many fields, there are various degrees of correlation. For example, in education, there is a common phenomenon in which pieces of knowledge are related between different there is a common phenomenon in which pieces of knowledge are related between differ- disciplines of the same major. Zhou and others proposed a method for constructing a ent disciplines of the same major. Zhou and others proposed a method for constructing a scientific knowledge graph based on the degree of interdisciplinary association, which scientific knowledge graph based on the degree of interdisciplinary association, which aims to help students quickly understand the relationship between disciplines [30]. The aims to help students quickly understand the relationship between disciplines [32]. The relevance analysis based on the knowledge graph can assist students in choosing courses relevance analysis based on the knowledge graph can assist students in choosing courses and improve teaching quality. and improve teaching quality. In recent years, with the development of science and technology, the “smart campus In recent years, with the development of science and technology, the “smart campus platform” has risen rapidly. Therefore, in the field of surveying and remote sensing, the platform” has risen rapidly. Therefore, in the field of surveying and remote sensing, the knowledge graph can be leveraged in the context of the smart campus platform. The same knowledgeknowledge isgraph shown can to be exist leveraged in multiple in the domains,context of therebythe smart highlighting campus platform. the association The same betweenknowledge fields is shown (Figure to13 ).exist The in association multiple do betweenmains, domainsthereby highlighting is related to thethe numberassociation of commonbetween knowledgefields (Figure between 13). The domains. association The morebetween the numberdomains of is commonrelated to knowledge, the number the of highercommon the knowledge association between between domains. fields. In The this more research, the number if one knowledge of common belongs knowledge, to both the “surveying”higher the association and “remote between sensing”, fields. then In the this knowledge research, is if the one common knowledge knowledge belongs between to both the“surveying” two fields. and The “remote more common sensing”, knowledge then the knowledge between two is the fields, common the higher knowledge degree be- of associationtween the two between fields. fields. The more common knowledge between two fields, the higher degree of association between fields. The domain is the class in ontology. In this research, the domains of surveying and remote sensing respectively correspond to the classes of “Surveying” and “Remote Sens- ing” in the knowledge graph. Among all the knowledge, some knowledge belongs to the class of “Surveying “, and some knowledge belongs to the class of “Remote Sensing”.

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Some knowledge belongs to both the class of “Surveying” and the class of “Remote Sens- ing”. These are the common knowledge of the two fields. Then this common knowledge determines the degree of association between the two fields (Figure 13). The picture (Figure 13) contains nodes of “Surveying” and “Remote Sensing”, as well as all the knowledge belonging to the field of remote sensing and surveying, which also contains the relationship “belong_to” between classes nodes and knowledge. The knowledge graph contains 1022 knowledge points. There are 273 knowledge points shared by the two disciplines. Its repetition rate is 26.7%. According to the degree of relevance, it can be used as a reference when selecting courses. If you were a student in a field unrelated to surveying and remote sensing and sought to have a general and comprehensive understanding of the field, then in order to save time and cost, you could just choose the two subjects of photogrammetry and remote sensing principles and applications. Ac- cordingly, professional students in this field can quickly help such students understand Remote Sens. 2021, 13, 2511the degree of coverage of knowledge content between subjects, help them14 of 19quickly understand a nearby subject that has already studied a subject, and improve learning efficiency.

FigureFigure 13. Display 13. Display of of relevance relevance between between courses. courses.

The domain is the class in ontology. In this research, the domains of surveying and 4.2. Knowledgeremote sensing Reasoning respectively in the correspond Field of to Surveying the classes of and “Surveying” Remote andSensing “Remote Sensing” Knowledgein the knowledge reasoning graph. Among is allprimarily the knowledge, based some knowledgeon known belongs knowledge to the class ofto infer new “Surveying”, and some knowledge belongs to the class of “Remote Sensing”. Some knowl- knowledgeedge belongs or distinguish to both the classincorrect of “Surveying” knowledge. and the classThe ofreasoning “Remote Sensing”. function These of are knowledge is a prominentthe common feature knowledge of the knowledge of the two fields. graph. Then thisCompared common knowledge to traditional determines knowledge the reasoning, knowledgedegree of association reasoning between based the twoon fieldsknowledge (Figure 13 graphs). is more flexible, and its methods The picture (Figure 13) contains nodes of “Surveying” and “Remote Sensing”, as are morewell diverse. as all the knowledgeThere are belonging many tomethods; the field of however, remote sensing the and method surveying, used which in this article is primarilyalso containsbased on the relationshipthe reasoning “belong_to” of description between classes logic nodes and and knowledge.thus primarily The introduces knowledgeknowledge reasoning graph contains based 1022 on knowledgethis method. points. There are 273 knowledge points shared by the two disciplines. Its repetition rate is 26.7%. According to the degree of relevance, Theit candescription be used as alogic reference system when is selecting primarily courses. divided If you into were four a student parts: in a(1) field three basic elements (concepts,unrelated to surveying relationships and remote and sensing entities); and sought (2) to the have axiom a general set, and to comprehensive which the concept belongs; (3)understanding the assertion of the set field, of then the in entity; order to saveand time (4) andthe cost, reasoning you could mechanism. just choose the two Reasoning tools subjects of photogrammetry and remote sensing principles and applications. Accordingly, based onprofessional supporting students OWL in this DL field (description can quickly help logic) such students language understand include the degree FaCT++, of Racer, and pellet [33].coverage In addition of knowledge to contentexisting between tools, subjects, knowledge help them can quickly be reasoned understand by a nearby writing rules. In subjectthis knowledge that has already graph, studied the a subject, reasoning and improve of knowledge learning efficiency. is described by writing rules based on4.2. the Knowledge reasoning Reasoning method in the Field of of description Surveying and Remotelogic. Sensing This article primarily involves two Knowledge reasoning is primarily based on known knowledge to infer new knowledge or distinguish incorrect knowledge. The reasoning function of knowledge is a promi- nent feature of the knowledge graph. Compared to traditional knowledge reasoning,

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knowledge reasoning based on knowledge graphs is more flexible, and its methods are more diverse. There are many methods; however, the method used in this article is primarily based on the reasoning of description logic and thus primarily introduces knowledge reasoning based on this method. The description logic system is primarily divided into four parts: (1) three basic elements (concepts, relationships and entities); (2) the axiom set, to which the concept belongs; (3) the assertion set of the entity; and (4) the reasoning mechanism. Reasoning tools based on supporting OWL DL (description logic) language include FaCT++, Racer, and pellet [31]. In addition to existing tools, knowledge can be reasoned by writing rules. In this knowledge graph, the reasoning of knowledge is described by writing rules based on the reasoning method of description logic. This article primarily involves two types of reasoning: entity and relational. Entity reasoning infers unknown entities based on existing entities and relationships (e.g., known entity-relation-unknown entity). Relational reasoning infers the relationship between one or more entities by editing rules under the condition of existing entities (e.g., known entity-unknown relationship-known entity). Generally, missing knowledge is inherent to knowledge graphs, that is, the incom- pleteness of entities or relationships. We can apply the knowledge graph in the field of surveying and remote sensing to complement the knowledge graph in the field of surveying and remote sensing. Knowledge graph completion is an important way to acquire knowledge. The goal of knowledge graph completion is to find these missing items of knowledge and add them to the knowledge graph so that the knowledge graph tends to be complete [32]. A knowledge graph is named G, and its basic components include the entity set E = e1, e2, ... ei (i is the number of entities), relation set R = r1, r2 ... rj (j is the number of relations) and corresponding triple set T = {(em, rk, en)} (em, en ∈ E, rk ∈ R). Since the number of entities E and relationships R in the knowledge graph are limited, there may be some entities and relationships that are not in G. According to the content we want to complete, we can divide knowledge completion into three subtasks: 1 Given a partial triplet T1 = (?, rk, en), we predict the head entity. 2 Given a partial triplet T2 = (em, rk,?), we predict the tail entity. 3 Given a partial triplet T3 = (em, ?, en), we predict the relationship between entities. According to whether the entities and relationships belong to the original knowledge graph, we can divide the knowledge graph completion into static knowledge graph completion and dynamic knowledge graph completion. The entities and relationships in the completion of the static knowledge graph are all in the original knowledge graph. The entities and relationships in the completion of the dynamic knowledge graph are not in the original knowledge graph. Through the completion of the knowledge graph, the collection of entities and relationships of the original knowledge graph can be expanded. Knowledge graph completion is the most widely used field of knowledge reasoning. The original intention of a large number of knowledge graph reasoning algorithms is to be applied to knowledge graph completion, such as the Markov logic network ( MLN), translating relation embeddings (TransR), capsule network-based embedding (CapsE), and relational graph neural network with hierarchical attention (RGHAT). All the methods mentioned above can determine whether there is a certain relationship between any entities by reasoning in the vector space, and then realize the completion of the knowledge graph. Integrating the knowledge graph in the field of surveying and remote sensing into the smart campus platform can help students discover new entities or relationships between entities. For example, relational reasoning is based on the Jena tool, as shown in code 3 (based on the SPARQL language). This code defines a rule named “rule”, which means that if there is an entity that belongs to “Remote Sensing”, then this entity belongs to “Geography”, which is (Remote Sensing-belong_to-Geography). For example: knowing (Aberration-belong_to-Remote Sensing), according to this code you can obtain the result: (Aberration-belong_to-Geography). The visual display of the whole reasoning process is shown in Figure 14. Remote Sens. 2021, 13, 2511 16 of 19

Code 3 Inference Rules Based on SPARQL Language @prefix: . @prefix owl: . @prefix rdf: . @prefix xsd: . @prefix rdfs: . Remote Sens. 2021, 13, 2511 16 of 19 [rule:(?k:belong_to ?g)(?g:hasname ?n)(?n:genre_name ‘Remote Sensing’)->(?k rdf:type:Geography)] [ruleInverse:(?k:belong_to ?g)->(?g:hasKnowledge ?k)]

Figure 14.14.Visual Visual display display of theof the inference inference process. process.

5. Summary and and Prospect Prospect InIn the the field field of of surveying surveying and and remote remote sensing, sensin rapidg, rapid acquisition, acquisition, efficient efficient processing processing and effective application of remote-sensing data are the core tasks. However, in the face of and effective application of remote-sensing data are the core tasks. However, in the face the massive amount of data accumulated, as well as the heterogeneous, decentralized, and dynamicof the massive update amount characteristics of data of accumulated, the massive amount as well of data,as the it heterogeneous, is difficult to realize decentralized, the semanticand dynamic integration, update interoperability characteristics and of sharingthe massive platform amount construction of data, of it massiveis difficult data to realize applicationthe semantic services. integration, Since the interoperability knowledge graph and is a kindsharing of semantic platform network, construction hierarchical of massive interconnectiondata application and services. semantic Since processing the knowledge capabilities graph can is be a realizedkind of semantic in the form network, of a hier- graphicalarchical interconnection structure, and the and system semantic and relevance processing of knowledge capabilities can can be be displayed realized more in the form intuitively.of a graphical The knowledgestructure, graphand the provides system a better and organizationrelevance of and knowledge management can method be displayed formore isolated intuitively. information The andknowledge knowledge. graph It describes provides the a real-worldbetter organization entities, concepts, and management and the relationships between entities and concepts in a structured and semantic form and method for isolated information and knowledge. It describes the real-world entities, con- organizes information into a form that is easier for people to understand. The purpose is to expresscepts, and the objectivethe relationships world as abetween well-structured entities knowledge and concepts expression. in a structured It is hailed and as asemantic boosterform and for theorganizes next generation information of artificial into a intelligence. form that Theis easier contributions for people of this to articleunderstand. are The aspurpose follows: is to express the objective world as a well-structured knowledge expression. It is (1)hailedTo as quickly a booster obtain for effective the next data generation from massive of artificial amounts ofinte heterogeneous,lligence. The decentral- contributions of this articleized, and are dynamically as follows: updated data. This article proposes a method for constructing a subject knowledge graph in the field of surveying and remote sensing. (1) To quickly obtain effective data from massive amounts of heterogeneous, decentral- (2) To verify the application value of the knowledge graph in the field of surveying and ized,remote and sensing. dynamically This article updated verifies data. its functions This article from proposes the query, a visualizationmethod for constructing and areasoning subject knowledge of the knowledge graph graph. in the field of surveying and remote sensing. (2) ToThe verify challenges the application and countermeasures value of encounteredthe knowledge in the graph research in the are field as follows: of surveying and remote sensing. This article verifies its functions from the query, visualization and (1) The connection between the mode layer and the data layer. The knowledge graph reasoningin the field of of the surveying knowledge and remote graph. sensing is mainly divided into two parts: mode The challenges and countermeasures encountered in the research are as follows: (1) The connection between the mode layer and the data layer. The knowledge graph in the field of surveying and remote sensing is mainly divided into two parts: mode level construction and a data layer. The mode level is the foundation of the data layer. Through the factual expression of the ontology library standard data layer, ontology is the conceptual template of a structured knowledge base. The data layer is composed of a series of knowledge entities or concepts. Knowledge is stored in units of facts. The data layer expresses knowledge in the form of triples (entity 1-relation-entity 2) or (entity-attribute-attribute value). Realizing the association between ontology and data at two levels is a major challenge in constructing a knowledge graph. This article uses the D2RQ tool to realize mapping from ontology to the database. The D2RQ tool converts the structured data of the relational database into data in RDF format. This mapping also provides the ability to view existing relational data that exist in the RDF data model, which is represented by mapping the structure selected by the customer

Remote Sens. 2021, 13, 2511 17 of 19

level construction and a data layer. The mode level is the foundation of the data layer. Through the factual expression of the ontology library standard data layer, ontology is the conceptual template of a structured knowledge base. The data layer is composed of a series of knowledge entities or concepts. Knowledge is stored in units of facts. The data layer expresses knowledge in the form of triples (entity 1-relation-entity 2) or (entity-attribute-attribute value). Realizing the association between ontology and data at two levels is a major challenge in constructing a knowledge graph. This article uses the D2RQ tool to realize mapping from ontology to the database. The D2RQ tool converts the structured data of the relational database into data in RDF format. This mapping also provides the ability to view existing relational data that exist in the RDF data model, which is represented by mapping the structure selected by the customer and the target vocabulary. The R2RML mapping itself is an RDF graph and is recorded in Turtle syntax. R2RML supports different types of mapping implementation. The processor can provide virtual SPARQL endpoints on the mapped relational data, or generate RDF dumps, provide a link data interface. (2) Application and practice of the domain knowledge graph. This article gives an example of the application of integrating the knowledge graph in the field of surveying and remote sensing into the smart campus platform. The knowledge visualization application of the domain knowledge graph on the smart campus platform can assist teachers and students in selecting courses. The domain knowledge graph is applied to knowledge reasoning on the smart campus platform, which can help teachers and students discover and reason about new knowledge, as well as new relationships between knowledge. Knowledge in the field of surveying and remote sensing is diverse and complex, and knowledge graphs can be studied in more detail in the following areas. Data in these fields of study are typically images, and the recognition and acquisition of knowledge in image data are understudied; in particular, the knowledge graph of image data lacks a time dimension. Thus, future research should investigate how to add the time dimension to the knowledge graph and how to extend the time dimension to the application of the knowledge graph. If the above problems can be broken through, then the knowledge graph in the field of surveying and remote sensing will have greater potential application value: (1) The discovery of new rules of surveying and remote sensing: The continuous increase in surveying and remote-sensing data and the continuous improvement of digital management and utilization technology have provided great convenience for scientific researchers to carry out research work. The surveying and remote-sensing knowledge graph provides support for the insight and discovery of regular knowledge of surveying and remote-sensing resources by associating a large amount of surveying and remote sensing knowledge into a network structure. Researchers can discover various knowledge and rules hidden behind the development process through the analysis of surveying and remote-sensing data to provide relevant scientific research personnel and scientific research policy makers with scientific research directions and a policy-making basis. (2) Application of machine learning methods in the analysis of knowledge graphs in the field of surveying and remote sensing: From the development process of the combination of machine learning and knowledge graphs (the knowledge graph as a complex network; traditional machine learning methods to conduct graph mining and analysis on the knowledge graph; further application of deep learning methods and graph neural network methods in knowledge graphs), the value and function of the knowledge graphs have been further embodied. The surveying and remote- sensing domain knowledge graph is a special domain knowledge graph, and the analysis method in the general knowledge graph is used to mine and analyse the graph, but it cannot make full use of the structure and characteristics of the surveying and remote-sensing domain knowledge graph. For the specific structural features and entity attributes in the knowledge graph of surveying and remote sensing, it Remote Sens. 2021, 13, 2511 18 of 19

is necessary to design specific machine learning methods or deep neural network structures. At the same time, for different application scenarios, different objective functions are usually designed to learn the parameters of the algorithm. Therefore, the study of machine learning and deep learning mining methods for the knowledge graph in the field of surveying and remote sensing is helpful to the further analysis and application of the knowledge graph in the field of surveying and remote sensing. (3) Construction of the service platform of the knowledge graph in the field of surveying and remote sensing: In the construction of the knowledge graph in the field of surveying and remote sensing, the work efficiency is affected due to the problem of scattered tools. The next research plan is to build a knowledge graph service platform in the field of surveying and remote sensing to realize the integration of tools and services. At the data source level, it integrates all kinds of open and available data in the field of surveying and remote sensing, as well as data unique to each demand side. Through the provided functions of surveying and remote sensing data acquisition, data storage, ontology construction, graph construction and update, a knowledge graph of the field of surveying and remote sensing that can be updated in time can be constructed. In terms of services, through the provision of knowledge service algorithms and models such as knowledge queries, knowledge visualization, and knowledge reasoning, a service platform provides targeted knowledge services for different roles.

Author Contributions: Conceptualization, X.H. and Z.J.; methodology, X.H.; validation, X.L., Q.L. and R.Y.; formal analysis, Z.J.; investigation, L.L.; resources, X.H.; data curation, X.L.; writing— original draft preparation, X.H.; writing—review and editing, L.L., L.Y.; visualization, M.S.; supervision, X.L.; project administration, Z.J.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript. Funding: This research was supported by the National Key Research and Development Project of China (No. 2016YFC0502106), National Science and Technology Major Project of China (No. 2018ZX07111002) and the National Natural Science Foundation of China(No. 41476161). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The storage URL of the structured raw data to construct the knowledge graph is: https://github.com/hao1661282457/Knowledge-graphs.git (accessed on 25 June 2021). Acknowledgments: We thank AJE (https://www.aje.cn/ (accessed on 25 June 2021)), for editing the English text of this manuscript. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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