DOCSLIB.ORG

Natural Language Questions for the Web of Data

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Natural Language Questions for the Web of Data Natural Language Questions for the Web of Data Mohamed Yahya1, Klaus Berberich1, Shady Elbassuoni2 Maya Ramanath3, Volker Tresp4, Gerhard Weikum1 1 Max Planck Institute for Informatics, Germany 2 Qatar Computing Research Institute 3 Dept. of CSE, IIT-Delhi, India 4 Siemens AG, Corporate Technology, Munich, Germany {myahya,kberberi,weikum}@mpi-inf.mpg.de [email protected] [email protected] [email protected] Abstract and discover relevant information. As linked data is in RDF format, the standard approach would be The Linked Data initiative comprises struc- to run structured queries in triple-pattern-based lan- tured databases in the Semantic-Web data model RDF. Exploring this heterogeneous guages like SPARQL, but only expert programmers data by structured query languages is tedious are able to precisely specify their information needs and error-prone even for skilled users. To ease and cope with the high heterogeneity of the data the task, this paper presents a methodology (and absence or very high complexity of schema in- for translating natural language questions into formation). For less initiated users the only option structured SPARQL queries over linked-data to query this rich data is by keyword search (e.g., sources. via services like sig.ma (Tummarello et al., 2010)). Our method is based on an integer linear pro- None of these approaches is satisfactory. Instead, the gram to solve several disambiguation tasks by far most convenient approach would be to search jointly: the segmentation of questions into in knowledge bases and the Web of linked data by phrases; the mapping of phrases to semantic entities, classes, and relations; and the con- means of natural-language questions. struction of SPARQL triple patterns. Our so- As an example, consider a quiz question like lution harnesses the rich type system provided “Which female actor played in Casablanca and is by knowledge bases in the web of linked data, married to a writer who was born in Rome?”. to constrain our semantic-coherence objective function. We present experiments on both the The answer could be found by querying sev- question translation and the resulting query eral linked data sources together, like the IMDB- answering. style LinkedMDB movie database and the DB- pedia knowledge base, exploiting that there are 1 Introduction entity-level sameAs links between these collections. One can think of different formulations of the 1.1 Motivation example question, such as “Which actress from Recently, very large, structured, and semantically Casablanca is married to a writer from Rome?”. A rich knowledge bases have become available. Ex- possible SPARQL formulation, assuming a user fa- amples are Yago (Suchanek et al., 2007), DBpe- miliar with the schema of the underlying knowl- dia (Auer et al., 2007), and Freebase (Bollacker et edge base(s), could consist of the following six al., 2008). DBpedia forms the nucleus of the Web of triple patterns (joined by shared-variable bind- Linked Data (Heath and Bizer, 2011), which inter- ings): ?x hasGender female, ?x isa actor, ?x connects hundreds of RDF data sources with a total actedIn Casablanca (film), ?x marriedTo ?w, of 30 billion subject-property-object (SPO) triples. ?w isa writer, ?w bornIn Rome. This complex The diversity of linked-data sources and their high query, which involves multiple joins, would yield heterogeneity make it difficult for humans to search good results, but it is difficult for the user to come 379 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 379–390, Jeju Island, Korea, 12–14 July 2012. c 2012 Association for Computational Linguistics up with the precise choices for relations, classes, and good query (with triple patterns that can actually entities. This would require familiarity with the con- be joined) or at a meaningless and non-executable tents of the knowledge base, which no average user query (with disconnected triple patterns). This gen- is expected to have. Our goal is to automatically cre- eralized disambiguation problem is much more chal- ate such structured queries by mapping the user’s lenging than the more focused task of named entity question into this representation. Keyword search is disambiguation (NED). It is also different from gen- usually not a viable alternative when the information eral word sense disambiguation (WSD), which fo- need involves joining multiple triples to construct cuses on the meaning of individual words (e.g., map- the final result, notwithstanding good attempts like ping them to WordNet synsets). that of Pound et al. (2010). In the example, the obvi- ous keyword query “female actress Casablanca mar- 1.3 Contribution ried writer born Rome” lacks a clear specification of In our approach, we introduce new elements towards the relations among the different entities. making translation of questions into SPARQL triple patterns more expressive and robust. Most impor- 1.2 Problem tantly, we solve the disambiguation and mapping Given a natural language question qNL and a knowl- tasks jointly, by encoding them into a comprehen- edge base KB, our goal is to translate qNL into a sive integer linear program (ILP): the segmentation formal query qFL that captures the information need of questions into meaningful phrases, the mapping expressed by qNL. of phrases to semantic entities, classes, and rela- We focus on input questions that put the em- tions, and the construction of SPARQL triple pat- phasis on entities, classes, and relations between terns. The ILP harnesses the richness of large knowl- them. We do not consider aggregations (counting, edge bases like Yago2 (Hoffart et al., 2011b), which max/min, etc.) and negations. As a result, we gener- has information not only about entities and relations, ate structured queries of the form known as conjunc- but also about surface names and textual patterns tive queries or select-project-join queries in database by which web sources refer to them. For example, terminology. Our target language is SPARQL 1.0, Yago2 knows that “Casablanca” can refer to the city where the above focus leads to queries that consist of or the film, and “played in” is a pattern that can de- multiple triple patterns, that is, conjunctions of SPO note the actedIn relation. In addition, we can lever- search conditions. We do not use any pre-existing age the rich type system of semantic classes. For ex- query templates, but generate queries from scratch ample, knowing that Casablanca is a film, for trans- as they involve a variable number of joins with a- lating “played in” we can focus on relations with a priori unknown join structure. type signature whose range includes films, as op- A major challenge is in the ambiguity of posed to sports teams, for example. Such informa- the phrases occurring in a natural-language ques- tion is encoded in judiciously designed constraints tion. Phrases can denote entities (e.g., the city for the ILP. Although we intensively harness Yago2, of Casablanca or the movie Casablanca), classes our approach does not depend on a specific choice of (e.g., actresses, movies, married people), or rela- knowledge base or language resource for type infor- tions/properties (e.g., marriedTo between people, mation and phrase/name dictionaries. Other knowl- played between people and movies). A priori, we do edge bases such as DBpedia can be easily plugged not know if a phrase should be mapped to an entity, in. a class, or a relation. In fact, some phrases may de- Based on these ideas, we have developed a frame- note any of these three kinds of targets. For example, work and system, called DEANNA (DEep Answers a phrase like “wrote score for” in a question about for maNy Naturally Asked questions), that com- film music composers, could map to the composer- prises a full suite of components for question de- film relation wroteSoundtrackForFilm, to the class composition, mapping constituents into the seman- of movieSoundtracks (a subclass of music pieces), tic concept space, generating alternative candidate or to an entity like the movie “The Score”. Depend- mappings, and computing a coherent mapping of all ing on the choice, we may arrive at a structurally constituents into a set of SPARQL triple patterns that 380 can be directly executed on one or more linked data ‘Who’, ‘played in’, ‘movie’ and ‘Casablanca’. sources. 2. Phrase mapping to semantic items. This in- 2 Background cludes finding that the phrase ‘played in’ can ei- ther refer to the semantic relation actedIn or to We use the Yago2 knowledge base, with its rich playedForTeam and that the phrase ‘Casablanca’ type system, as a semantic backbone. Yago2 is com- can potentially refer to Casablanca (film) or posed of instances of binary relations derived from Casablanca, Morocco. This step merely constructs Wikipedia and WordNet. The instances, called facts, a candidate space for the mapping. The actual dis- provide both ontological information and instance ambiguation is addressed by step 4, discussed below. data. Figure 1 shows sample facts from Yago2. Each 3. Q-unit generation. Intuitively, a q-unit is a triple fact is composed of semantic items that can be di- composed of phrases. Their generation and role will vided into relations, entities, and classes. Entities be discussed in detail in the next section. and classes together are referred to as concepts. 4. Joint disambiguation, where the ambiguities in the phrase-to-semantic-item mapping are resolved. Subject Predicate Object film subclassOf production This entails resolving the ambiguity in phrase bor- Casablanca (film) type film ders, and above all, choosing the best fitting can- “Casablanca” means Casablanca (film) “Casablanca” means Casablanca, Morocco didates from the semantic space of entities, classes, Ingrid Bergman actedIn Casablanca (film) and relations. Here, we determine for our running Figure 1: Sample knowledge base example that ‘played in’ refers to the semantic re- lation actedIn and not to playedForTeam and the Examples of relations are type, subclassOf, and phrase ‘Casablanca’ refers to Casablanca (film) actedIn.
Load more

Recommended publications
  • A Question Answering System for Chemistry
    A Question Answering System for Chemistry Preprint Cambridge Centre for Computational Chemical Engineering ISSN 1473 – 4273 A Question Answering System for Chemistry Xiaochi Zhou1, Daniel Nurkowski4, Sebastian Mosbach1;2, Jethro Akroyd1;2, Markus Kraft1;2;3 released: January 25, 2021 1 Department of Chemical Engineering 2 CARES and Biotechnology Cambridge Centre for Advanced University of Cambridge Research and Education in Singapore Philippa Fawcett Drive 1 Create Way Cambridge, CB3 0AS CREATE Tower, #05-05 United Kingdom Singapore, 138602 3 School of Chemical 4 CMCL Innovations and Biomedical Engineering Sheraton House Nanyang Technological University Castle Park, Castle Street 62 Nanyang Drive Cambridge, CB3 0AX Singapore, 637459 United Kingdom Preprint No. 266 Keywords: Question Answering system, Natural language processing, Knowledge Graph Edited by Computational Modelling Group Department of Chemical Engineering and Biotechnology University of Cambridge Philippa Fawcett Drive Cambridge, CB3 0AS United Kingdom CoMo E-Mail: [email protected] GROUP World Wide Web: https://como.ceb.cam.ac.uk/ Abstract This paper describes the implementation and evaluation of a proof-of-concept Ques- tion Answering system for accessing chemical data from knowledge graphs which offer data from chemical kinetics to chemical and physical properties of species. We trained a question type classification model and an entity extraction model to interpret chemistry questions of interest. The system has a novel design which applies a topic model to identify the question-to-ontology affiliation. The topic model helps the sys- tem to provide more accurate answers. A new method that automatically generates training questions from ontologies is also implemented. The question set generated for training contains 80085 questions under 8 types.
    [Show full text]
  • Discussion #1 | University of Texas at Austin | August 13, 2018 Facilitated By: Itza A
    Introduction to Linked Data Discussion #1 | University of Texas at Austin | August 13, 2018 Facilitated by: Itza A. Carbajal, LLILAS Benson Latin American Metadata Librarian Hello! My name is Itza A. Carbajal I will be facilitating this discussion series on Linked Data I am the Latin American Metadata Librarian for a Post Custodial project at LLILAS Benson Have questions? Email me at: [email protected] Like twitter? You can find me at: @archiviststan Housekeeping ◎ Discussions are meant to highlight collective wisdom of the group ◎ Attendance to all discussion meetings not required, but recommended ◎ Take home practices are not required, but encouraged to further an individual’s understanding ◎ Readings are not required, but should be considered as a method for self education or for sharing with others Link to LIVE syllabus: http://bit.ly/SYLLABUSUTLD Link to reading materials: http://bit.ly/READUTLD 1. Discussion #1 Topics Topics to Discuss ◎ Semantic Web ◎ Linked data principles ◎ RDF and Triple statements Related Topics not covered in discussions ◎ Linked Open Data ◎ Examples of linked open data sets ◎ Linked Data platform 2. First, the Semantic Web Semantic Web (project) ◎ Derives from the concept of semantics defined as the study of meanings ◎ Extension to the current World Wide Web ◎ Also known as Web 3.0 where the web can now “read-write-execute” ◎ Changes the current web of information to a web of data including data inside the web and outside ◎ Core functions include semantic markup ○ Semantic markup - data interchange formats accessible to humans and machine ◎ Focuses on adding meaning/context to information giving machines and humans the ability to communicate and cooperate ◎ Relies on machine-readable metadata to expresses that meaning/context ◎ No formal definition, still ongoing and constantly maturing 3.
    [Show full text]
  • Constructing Reference Semantic Predictions from Biomedical Knowledge Sources
    Constructing Reference Semantic Predictions from Biomedical Knowledge Sources Demeke Ayele1, J. P. Chevallet2, Million Meshesha1, Getnet Kassie1 (1) Addis Ababa University, Addis Ababa, Ethiopia (2) University of Grenoble, Grenoble, France {demekeayele, meshe8, getnetmk}@gmail.com, jean- [email protected] ABSTRACT Semantic tuples are core component of text mining and knowledge extraction systems in biomedicine. The practical success of these systems significantly depends on the correctness and quality of the extracted semantic tuples. The quality and correctness of the semantic predictions can be measured against a benchmark semantic structure. In this article, we presented an approach for constructing a reference semantic tuple structure based on the existing biomedical knowledge sources in which the evaluation is based on the UMLS knowledge sources. In the evaluation, 7400 semantic triples are extracted from UMLS knowledge sources and the semantic predictions are constructed using the proposed approach. In the semantic triples, 87 concepts are found redundantly classified and 207 pair of semantic triples showed hierarchically inconsistent. 128 are found to be non-taxonomically inconsistent. The quality of the semantic triple is also judged using expert evaluators. The Cohen's kappa coefficient is used to measure the degree of agreement between two evaluators and the result is promising (0.9). Construire des prévisions de référence sémantique à partir de sources de connaissances biomédicales Les "tuples sémantiques" forment un élément essentiel à la fouille de texte et aux systèmes d'extraction de connaissances dans le domaine biomédical. Le succès en pratique des systèmes exploitant ces informations sémantique, dépend fortement de l'exactitude et de la qualité des tuples sémantiques.
    [Show full text]
  • Distributed Semantic Sensor Networks How to Use Semantics and Knowledge Distribution to Integrate Sensor Data of Disparate Data Sources
    2007 Distributed Semantic Sensor Networks How to use semantics and knowledge distribution to integrate sensor data of disparate data sources. In this research project we focused on sensor networks and how semantic web technologies and knowledge sharing can make integration of distributed sensor data possible. To achieve this we have first identified the main challenges to address to allow for data integration. A distributed semantic sensor network framework is proposed that uses semantic web techniques and knowledge sharing to address these challenges. We propose that knowledge sharing in a sensor network is a key aspect of data integration in a dynamic environment, since it allows the network to handle changes in the environment. This project is innovative in that it proposes new ways to handle semantic integration in a distributed environment, by using question federation and data conversion on semantically annotated data and questions. It contributes to current research in that our framework enables data integration of distributed sensor data. Masters Student M.J. van der Veen, Department of Artificial Intelligence, Rijksuniversiteit Groningen, Groningen Supervisors Bart Verheij, Department of Artificial Intelligence, Rijksuniversiteit Groningen Arnoud de Jong, TNO ICT & Telecommunicatie, Groningen 1 People A masters student at the Department of Artificial Intelligence at the Rijksuniversiteit Groningen in The Netherlands. This thesis is the final work in his masters studies. His main interests besides writing this thesis is doing sports, coaching, travelling and making music . Masters Student: Maarten van der Veen A tenured lecturer/researcher (in Dutch: universitair docent) at the University of Groningen, Department of Artificial Intelligence and a member of the ALICE institute.
    [Show full text]
  • Applying the Semantic Web to Computational Chemistry
    Applying the Semantic Web to Computational Chemistry Neil S. Ostlund, Mirek Sopek Chemical Semantics Inc. , Gainesville, Florida, USA {ostlund, sopek}@chemicalsemantics.com Abstract. Chemical Semantics is a new start-up devoted to bringing the Semantic Web to computational chemistry, with a future goal to cover chemical results of other kinds, including experimental. We have created a prototype test bed that enables computational chemists to publish the results of their computations (for example: structure, energies and wave functions of Ab Initio calculations) on servers (powered by semantic triple stores) holding RDF data. In addition, scientists will be able to search across results produced by themselves and other researchers using SPARQL queries and faceted search built on top of it. The semantics of computational chemistry oriented RDF data is defined by an ontology identified as “GC” (Gainesville Core). Gainesville Core defines concepts such as: theoretical models used in computations, molecular systems (as collections of atoms and molecules), molecular arrangements which describe system evolution in time or in phase space and so on. The poster demonstrates the project during its early stage, including a description and demonstration of the prototype portal created by Chemical Semantics. This paper is the concise description of the poster content. Keywords: computational chemistry, semantic web, linked data, Gainesville Core, ontology Chemical Semantics portal main targets The Chemical Semantic portal, now in its prototype version, has the following long term goals: 1. Interoperable PUBLISHING of Computational Chemistry calculations using Linked Data principles 2. Enhanced search across all published results with basic reasoning enabled by the new ontology 3. FEDERATION of published data with existing web-based chemical datasets 4.
    [Show full text]
  • Relext: Relation Extraction Using Deep Learning Approaches for Cybersecurity Knowledge Graph Improvement
    RelExt: Relation Extraction using Deep Learning approaches for Cybersecurity Knowledge Graph Improvement Aditya Pingle, Aritran Piplai, Sudip Mittal, Anupam Joshi James Holt, and Richard Zak University of Maryland, Baltimore County, Baltimore, MD 21250, USA Laboratory for Physical Sciences fadiping1, apiplai1, smittal1, [email protected] fholt, [email protected] Abstract—Security Analysts that work in a ‘Security Opera- cybersecurity reports, CVE [21], CWE [22], National Vul- tions Center’ (SoC) play a major role in ensuring the security nerability Datasets [28], and any cybersecurity text publicly of the organization. The amount of background knowledge they available. Various SIEM systems, fetch, process, and store have about the evolving and new attacks makes a significant difference in their ability to detect attacks. Open source threat much of the cyber threat intelligence available through OSINT intelligence sources, like text descriptions about cyber-attacks, sources. Knowledge Graphs (KG) specifically to store cyber can be stored in a structured fashion in a cybersecurity knowl- threat intelligence has been used by many cybersecurity in- edge graph. A cybersecurity knowledge graph can be paramount formatics systems. These cybersecurity knowledge graphs can in aiding a security analyst to detect cyber threats because it not only store but are also be able to retrieve data and answer stores a vast range of cyber threat information in the form of semantic triples which can be queried. A semantic triple complex queries asked by the security analysts. contains two cybersecurity entities with a relationship between The dependence of various cybersecurity informatics sys- them. In this work, we propose a system to create semantic tems on various knowledge representation schemes makes triples over cybersecurity text, using deep learning approaches it imperative that we develop systems that improve these to extract possible relationships.
    [Show full text]
  • An Approach for Knowledge Graph Construction from Spanish Texts
    ISSN 1870-4069 An Approach for Knowledge Graph Construction from Spanish Texts Andrea G. Garcia Perez1, Ana B. Rios Alvarado1, Tania Y. Guerrero Melendez1, Edgar Tello Leal1, Jose L. Martinez Rodriguez2 1 Autonomous University of Tamaulipas, Mexico 2 CINVESTAV Unidad Tamaulipas, Mexico [email protected], farios, tyguerre, [email protected], [email protected] Abstract. Knowledge Graphs (KGs) represent valuable data sources for the Educational and Learning field, allowing computers and peo- ple to process and interpret information in an easier way. In addition KGs are useful to get information by software systems on any data re- source (person, place, organization, among others). KGs are represented through triples composed of entities and semantic relations commonly obtained from textual resources. However, exploiting triples from text is complicated due to linguistic variations, where Spanish has been slightly approached. This paper presents a method for constructing KGs from textual resources in Spanish. The method is composed of four stages: 1) obtaining of documents, 2) identification of entities and their relations, 3) association of entities to linked data resources, and 4) the construction of a schema for the KGs. The experiments were performed over a set of doc- uments in a general and computer science domain. Our method showed encouraging results for the precision in the identification of entities and semantic relationships for the KGs constructed from unstructured texts. Keywords: knowledge graph, construction of graph from texts. 1 Introduction In recent years the Web has become a global repository that represents a source of knowledge in a wide range of domains and languages, where the information is shared and stored in different formats.
    [Show full text]
  • Ontology Middleware for Integration of Iot Healthcare Information Systems in EHR Systems
    Article Ontology Middleware for Integration of IoT Healthcare Information Systems in EHR Systems Abdullah Alamri Faculty of Computing and Information Technology, University of Jeddah, Jeddah 23218, Saudi Arabia; [email protected] Received: 14 September 2018; Accepted: 4 October 2018; Published: 8 October 2018 Abstract: Healthcare sectors have been at the forefront of the adoption and use of IoT technologies for efficient healthcare diagnosis and treatment. Because healthcare IoT sensor technology obtains health-related data from patients, it needs to be integrated with the electronic healthcare records (EHR) system. Most EHR systems have not been designed for integration with IoT technology; they have been designed to be more patient-centric management systems. The use of the IoT in EHR remains a long-term goal. Configuring IoT in EHR can enhance patient healthcare, enabling health providers to monitor their patients outside of the clinic. To assist physicians to access data resources efficiently, a data model that is semantic and flexible is needed to connect EHR data and IoT data that may help to provide true interoperability and integration. This research proposes a semantic middleware that exploits ontology to support the semantic integration and functional collaborations between IoT healthcare Information Systems and EHR systems. Keywords: IoT; EHR; healthcare IT; ontology; semantic integration; middleware 1. Introduction Technological advancements are realizing the vision of interconnected objects (things) as smart objects which can sense each other according to the environment and deliver information to a variety of innovative applications and services [1]. These sensing objects constitute the Internet of Things (IoT) that can offer enormous potential for high-quality automation in many different industry sectors.
    [Show full text]
  • Heron Visualisation Engine. Visualisation and Dissemination of Semantic Cultural Heritage Data
    Archeologia e Calcolatori 28.2, 2017, 603-607 HERON VISUALISATION ENGINE. VISUALISATION AND DISSEMINATION OF SEMANTIC CULTURAL HERITAGE DATA Over the past two decades, there has been a proliferation of software to create great 3D models of archaeological sites and objects, and there has been plenty of thought and discussion on data models for finds. The results of those exertions have been made public through institutional websites and specific portals, but now, a further step is necessary: the cultural heritage data and (meta)data need to be taken into the semantic web. The Heritage Online Visualisation Engine (Heron VE) was born to do this. It provides tools for documenting, visualising and disseminating the se- mantic relations between sites, objects, documentation and narratives. Heron VE is scalable: it can be used to tell the story of a particular archaeological complex, but it can also illustrate relations between sites and objects which are widely separated in both time and space. With Heron VE, dissemination of (meta)data can take many different forms: Heron VE contains modules for presenting and reporting on data, but it can also be used to provide data only. Heron VE was born in the early 1990s, when the author was attending the University in Utrecht. Working with both relational databases and SGML (e.g. TEI, the Text Encoding Initiative), the author developed a taste for flexible data models based on standards. A significant step towards Heron VE was his work on Digitale Kanalen – het water van Amsterdam (Museumserver.nl 2001), a website recounting the story of water – highlighting, amongst other things, beer, shipping and flood defenses – in the history of Amsterdam.
    [Show full text]
  • Out of Cite, out of Mind: the Current State of Practice, Policy, and Technology for the Citation of Data
    Data Science Journal, Volume 12, 13 September 2013 OUT OF CITE, OUT OF MIND: THE CURRENT STATE OF PRACTICE, POLICY, AND TECHNOLOGY FOR THE CITATION OF DATA CODATA-ICSTI Task Group on Data Citation Standards and Practices Edited by Yvonne M. Socha Data Science Journal, Volume 12, 13 September 2013 OUT OF CITE, OUT OF MIND: THE CURRENT STATE OF PRACTICE, POLICY, AND TECHNOLOGY FOR THE CITATION OF DATA CODATA-ICSTI Task Group on Data Citation Standards and Practices Edited by Yvonne M. Socha PREFACE The growth in the capacity of the research community to collect and distribute data presents huge opportunities. It is already transforming old methods of scientific research and permitting the creation of new ones. However, the exploitation of these opportunities depends upon more than computing power, storage, and network connectivity. Among the promises of our growing universe of online digital data are the ability to integrate data into new forms of scholarly publishing to allow peer-examination and review of conclusions or analysis of experimental and observational data and the ability for subsequent researchers to make new analyses of the same data, including their combination with other data sets and uses that may have been unanticipated by the original producer or collector. The use of published digital data, like the use of digitally published literature, depends upon the ability to identify, authenticate, locate, access, and interpret them. Data citations provide necessary support for these functions, as well as other functions such as attribution of credit and establishment of provenance. References to data, however, present challenges not encountered in references to literature.
    [Show full text]
  • Exploration of Large-Scale SPARQL Query Collections: Finding Structure and Regularity for Optimizing Database Systems
    Exploration of Large-Scale SPARQL Query Collections: Finding Structure and Regularity for Optimizing Database Systems Von der Universit¨atBayreuth zur Erlangung des Grades eines Doktors der Naturwissenschaften (Dr. rer. nat.) genehmigte Abhandlung von Thomas Timm aus Herne 1. Gutachter: Prof. Dr. Wim Martens 2. Gutachter: Prof. Dr. George Fletcher Tag der Einreichung: 20. August 2019 Tag des Kolloquiums: 19. Februar 2020 Erforschung von riesigen SPARQL Anfragesammlungen: Erkennung von Struktur und Regularit¨at zur Optimierung von Datenbanksystemen Deutsche Kurzfassung Nachdem das Word Wide Web erfolgreich in das Leben von Menschen uberall¨ Einzug gehalten hatte, bereitete es den Weg fur¨ das Semantic Web. W¨ahrend das Word Wide Web zur menschlichen Nutzung konzipiert worden inst, soll das Semantic Web Maschinen die Verarbeitung von Daten erleichtern. Zu diesem Zweck werden Daten als Ontologie statt mit klassischen relationalen Datenbanken modelliert. Die vorliegende Arbeit befasst sich mit der Erforschung von riesigen Sammlungen von Anfragen fur¨ semantische Datenbanken. Konkret wurden uber¨ eine halbe Milliarde Anfragen untersucht. Die vom World Wide Web Consortium (W3C) entwickelte Spezifikation Resource Description Frame- work (RDF) hat sich als Standard zur Abbildung von semantischen Daten etabliert. Als zugeh¨orige Sprache fur¨ Anfragen ist das SPARQL Protocol and RDF Query Language (SPARQL, ein rekursives Akronym) vom W3C ent- wickelt worden. Es gibt mehrere große ¨offentliche Datenbanken die semantische Daten zur Verfugung¨ stellen. Die Nutzung dieser Endpunkte ergibt sich aus deren Protokollierung. Diese Aufzeichnungen liefern Anfragesammlungen, die Auf- schluss uber¨ die tats¨achliche Nutzung der Daten und der Funktionen von SPARQL geben k¨onnen. Untersucht wurden zwei prim¨are Quellen fur¨ An- fragen: Eine gemischte Sammlung, die haupts¨achlich von USEWOD stammt, und ¨offentlich verfugbare¨ Anfragesammlungen von Wikidata.
    [Show full text]
  • Semantics-Preserving RDB2RDF Data Transformation Using Hierarchical Direct Mapping
    applied sciences Article Semantics-Preserving RDB2RDF Data Transformation Using Hierarchical Direct Mapping Hee-Gook Jun 1 and Dong-Hyuk Im 2,* 1 Openub, Seoul 06097, Korea; [email protected] 2 School of Information Convergence, Kwangwoon University, Seoul 01890, Korea * Correspondence: [email protected] Received: 22 September 2020; Accepted: 8 October 2020; Published: 12 October 2020 Abstract: Direct mapping is an automatic transformation method used to generate resource description framework (RDF) data from relational data. In the field of direct mapping, semantics preservation is critical to ensure that the mapping method outputs RDF data without information loss or incorrect semantic data generation. However, existing direct-mapping methods have problems that prevent semantics preservation in specific cases. For this reason, a mapping method is developed to perform a semantics-preserving transformation of relational databases (RDB) into RDF data without semantic information loss and to reduce the volume of incorrect RDF data. This research reviews cases that do not generate semantics-preserving results, and the corresponding problems into categories are arranged. This paper defines lemmas that represent the features of RDF data transformation to resolve those problems. Based on the lemmas, this work develops a hierarchical direct-mapping method to strictly abide by the definition of semantics preservation and to prevent semantic information loss, reducing the volume of incorrect RDF data generated. Experiments demonstrate the capability of the proposed method to perform semantics-preserving RDB2RDF data transformation, generating semantically accurate results. This work impacts future studies, which should involve the development of synchronization methods to achieve RDF data consistency when original RDB data are modified.
    [Show full text]