Semantic Networks for Engineering Design: a Survey
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
A Comparative Evaluation of Geospatial Semantic Web Frameworks for Cultural Heritage
heritage Article A Comparative Evaluation of Geospatial Semantic Web Frameworks for Cultural Heritage Ikrom Nishanbaev 1,* , Erik Champion 1,2,3 and David A. McMeekin 4,5 1 School of Media, Creative Arts, and Social Inquiry, Curtin University, Perth, WA 6845, Australia; [email protected] 2 Honorary Research Professor, CDHR, Sir Roland Wilson Building, 120 McCoy Circuit, Acton 2601, Australia 3 Honorary Research Fellow, School of Social Sciences, FABLE, University of Western Australia, 35 Stirling Highway, Perth, WA 6907, Australia 4 School of Earth and Planetary Sciences, Curtin University, Perth, WA 6845, Australia; [email protected] 5 School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth, WA 6845, Australia * Correspondence: [email protected] Received: 14 July 2020; Accepted: 4 August 2020; Published: 12 August 2020 Abstract: Recently, many Resource Description Framework (RDF) data generation tools have been developed to convert geospatial and non-geospatial data into RDF data. Furthermore, there are several interlinking frameworks that find semantically equivalent geospatial resources in related RDF data sources. However, many existing Linked Open Data sources are currently sparsely interlinked. Also, many RDF generation and interlinking frameworks require a solid knowledge of Semantic Web and Geospatial Semantic Web concepts to successfully deploy them. This article comparatively evaluates features and functionality of the current state-of-the-art geospatial RDF generation tools and interlinking frameworks. This evaluation is specifically performed for cultural heritage researchers and professionals who have limited expertise in computer programming. Hence, a set of criteria has been defined to facilitate the selection of tools and frameworks. -
Probabilistic Topic Modelling with Semantic Graph
Probabilistic Topic Modelling with Semantic Graph B Long Chen( ), Joemon M. Jose, Haitao Yu, Fajie Yuan, and Huaizhi Zhang School of Computing Science, University of Glasgow, Sir Alwyns Building, Glasgow, UK [email protected] Abstract. In this paper we propose a novel framework, topic model with semantic graph (TMSG), which couples topic model with the rich knowledge from DBpedia. To begin with, we extract the disambiguated entities from the document collection using a document entity linking system, i.e., DBpedia Spotlight, from which two types of entity graphs are created from DBpedia to capture local and global contextual knowl- edge, respectively. Given the semantic graph representation of the docu- ments, we propagate the inherent topic-document distribution with the disambiguated entities of the semantic graphs. Experiments conducted on two real-world datasets show that TMSG can significantly outperform the state-of-the-art techniques, namely, author-topic Model (ATM) and topic model with biased propagation (TMBP). Keywords: Topic model · Semantic graph · DBpedia 1 Introduction Topic models, such as Probabilistic Latent Semantic Analysis (PLSA) [7]and Latent Dirichlet Analysis (LDA) [2], have been remarkably successful in ana- lyzing textual content. Specifically, each document in a document collection is represented as random mixtures over latent topics, where each topic is character- ized by a distribution over words. Such a paradigm is widely applied in various areas of text mining. In view of the fact that the information used by these mod- els are limited to document collection itself, some recent progress have been made on incorporating external resources, such as time [8], geographic location [12], and authorship [15], into topic models. -
Semantic Memory: a Review of Methods, Models, and Current Challenges
Psychonomic Bulletin & Review https://doi.org/10.3758/s13423-020-01792-x Semantic memory: A review of methods, models, and current challenges Abhilasha A. Kumar1 # The Psychonomic Society, Inc. 2020 Abstract Adult semantic memory has been traditionally conceptualized as a relatively static memory system that consists of knowledge about the world, concepts, and symbols. Considerable work in the past few decades has challenged this static view of semantic memory, and instead proposed a more fluid and flexible system that is sensitive to context, task demands, and perceptual and sensorimotor information from the environment. This paper (1) reviews traditional and modern computational models of seman- tic memory, within the umbrella of network (free association-based), feature (property generation norms-based), and distribu- tional semantic (natural language corpora-based) models, (2) discusses the contribution of these models to important debates in the literature regarding knowledge representation (localist vs. distributed representations) and learning (error-free/Hebbian learning vs. error-driven/predictive learning), and (3) evaluates how modern computational models (neural network, retrieval- based, and topic models) are revisiting the traditional “static” conceptualization of semantic memory and tackling important challenges in semantic modeling such as addressing temporal, contextual, and attentional influences, as well as incorporating grounding and compositionality into semantic representations. The review also identifies new challenges -
Knowledge Graphs on the Web – an Overview Arxiv:2003.00719V3 [Cs
January 2020 Knowledge Graphs on the Web – an Overview Nicolas HEIST, Sven HERTLING, Daniel RINGLER, and Heiko PAULHEIM Data and Web Science Group, University of Mannheim, Germany Abstract. Knowledge Graphs are an emerging form of knowledge representation. While Google coined the term Knowledge Graph first and promoted it as a means to improve their search results, they are used in many applications today. In a knowl- edge graph, entities in the real world and/or a business domain (e.g., people, places, or events) are represented as nodes, which are connected by edges representing the relations between those entities. While companies such as Google, Microsoft, and Facebook have their own, non-public knowledge graphs, there is also a larger body of publicly available knowledge graphs, such as DBpedia or Wikidata. In this chap- ter, we provide an overview and comparison of those publicly available knowledge graphs, and give insights into their contents, size, coverage, and overlap. Keywords. Knowledge Graph, Linked Data, Semantic Web, Profiling 1. Introduction Knowledge Graphs are increasingly used as means to represent knowledge. Due to their versatile means of representation, they can be used to integrate different heterogeneous data sources, both within as well as across organizations. [8,9] Besides such domain-specific knowledge graphs which are typically developed for specific domains and/or use cases, there are also public, cross-domain knowledge graphs encoding common knowledge, such as DBpedia, Wikidata, or YAGO. [33] Such knowl- edge graphs may be used, e.g., for automatically enriching data with background knowl- arXiv:2003.00719v3 [cs.AI] 12 Mar 2020 edge to be used in knowledge-intensive downstream applications. -
Large Semantic Network Manual Annotation 1 Introduction
Large Semantic Network Manual Annotation V´aclav Nov´ak Institute of Formal and Applied Linguistics Charles University, Prague [email protected] Abstract This abstract describes a project aiming at manual annotation of the content of natural language utterances in a parallel text corpora. The formalism used in this project is MultiNet – Multilayered Ex- tended Semantic Network. The annotation should be incorporated into Prague Dependency Treebank as a new annotation layer. 1 Introduction A formal specification of the semantic content is the aim of numerous semantic approaches such as TIL [6], DRT [9], MultiNet [4], and others. As far as we can tell, there is no large “real life” text corpora manually annotated with such markup. The projects usually work only with automatically generated annotation, if any [1, 6, 3, 2]. We want to create a parallel Czech-English corpora of texts annotated with the corresponding semantic network. 1.1 Prague Dependency Treebank From the linguistic viewpoint there language resources such as Prague Dependency Treebank (PDT) which contain a deep manual analysis of texts [8]. PDT contains annotations of three layers, namely morpho- logical, analytical (shallow dependency syntax) and tectogrammatical (deep dependency syntax). The units of each annotation level are linked with corresponding units on the preceding level. The morpho- logical units are linked directly with the original text. The theoretical basis of the treebank lies in the Functional Gener- ative Description of language system [7]. PDT 2.0 is based on the long-standing Praguian linguistic tradi- tion, adapted for the current computational-linguistics research needs. The corpus itself is embedded into the latest annotation technology. -
Universal Or Variation? Semantic Networks in English and Chinese
Universal or variation? Semantic networks in English and Chinese Understanding the structures of semantic networks can provide great insights into lexico- semantic knowledge representation. Previous work reveals small-world structure in English, the structure that has the following properties: short average path lengths between words and strong local clustering, with a scale-free distribution in which most nodes have few connections while a small number of nodes have many connections1. However, it is not clear whether such semantic network properties hold across human languages. In this study, we investigate the universal structures and cross-linguistic variations by comparing the semantic networks in English and Chinese. Network description To construct the Chinese and the English semantic networks, we used Chinese Open Wordnet2,3 and English WordNet4. The two wordnets have different word forms in Chinese and English but common word meanings. Word meanings are connected not only to word forms, but also to other word meanings if they form relations such as hypernyms and meronyms (Figure 1). 1. Cross-linguistic comparisons Analysis The large-scale structures of the Chinese and the English networks were measured with two key network metrics, small-worldness5 and scale-free distribution6. Results The two networks have similar size and both exhibit small-worldness (Table 1). However, the small-worldness is much greater in the Chinese network (σ = 213.35) than in the English network (σ = 83.15); this difference is primarily due to the higher average clustering coefficient (ACC) of the Chinese network. The scale-free distributions are similar across the two networks, as indicated by ANCOVA, F (1, 48) = 0.84, p = .37. -
Structure at Every Scale: a Semantic Network Account of the Similarities Between Very Unrelated Concepts
De Deyne, S., Navarro D. J., Perfors, A. and Storms, G. (2016). Structure at every scale: A semantic network account of the similarities between very unrelated concepts. Journal of Experimental Psychology: General, 145, 1228-1254 https://doi.org/10.1037/xge0000192 Structure at every scale: A semantic network account of the similarities between unrelated concepts Simon De Deyne, Danielle J. Navarro, Amy Perfors University of Adelaide Gert Storms University of Leuven Word Count: 19586 Abstract Similarity plays an important role in organizing the semantic system. However, given that similarity cannot be defined on purely logical grounds, it is impor- tant to understand how people perceive similarities between different entities. Despite this, the vast majority of studies focus on measuring similarity between very closely related items. When considering concepts that are very weakly re- lated, little is known. In this paper we present four experiments showing that there are reliable and systematic patterns in how people evaluate the similari- ties between very dissimilar entities. We present a semantic network account of these similarities showing that a spreading activation mechanism defined over a word association network naturally makes correct predictions about weak sim- ilarities and the time taken to assess them, whereas, though simpler, models based on direct neighbors between word pairs derived using the same network cannot. Keywords: word associations, similarity, semantic networks, random walks. This work was supported by a research grant funded by the Research Foundation - Flanders (FWO), ARC grant DE140101749 awarded to the first author, and by the interdisciplinary research project IDO/07/002 awarded to Dirk Speelman, Dirk Geeraerts, and Gert Storms. -
Untangling Semantic Similarity: Modeling Lexical Processing Experiments with Distributional Semantic Models
Untangling Semantic Similarity: Modeling Lexical Processing Experiments with Distributional Semantic Models. Farhan Samir Barend Beekhuizen Suzanne Stevenson Department of Computer Science Department of Language Studies Department of Computer Science University of Toronto University of Toronto, Mississauga University of Toronto ([email protected]) ([email protected]) ([email protected]) Abstract DSMs thought to instantiate one but not the other kind of sim- Distributional semantic models (DSMs) are substantially var- ilarity have been found to explain different priming effects on ied in the types of semantic similarity that they output. Despite an aggregate level (as opposed to an item level). this high variance, the different types of similarity are often The underlying conception of semantic versus associative conflated as a monolithic concept in models of behavioural data. We apply the insight that word2vec’s representations similarity, however, has been disputed (Ettinger & Linzen, can be used for capturing both paradigmatic similarity (sub- 2016; McRae et al., 2012), as has one of the main ways in stitutability) and syntagmatic similarity (co-occurrence) to two which it is operationalized (Gunther¨ et al., 2016). In this pa- sets of experimental findings (semantic priming and the effect of semantic neighbourhood density) that have previously been per, we instead follow another distinction, based on distri- modeled with monolithic conceptions of DSM-based seman- butional properties (e.g. Schutze¨ & Pedersen, 1993), namely, tic similarity. Using paradigmatic and syntagmatic similarity that between syntagmatically related words (words that oc- based on word2vec, we show that for some tasks and types of items the two types of similarity play complementary ex- cur in each other’s near proximity, such as drink–coffee), planatory roles, whereas for others, only syntagmatic similar- and paradigmatically related words (words that can be sub- ity seems to matter. -
Ontology-Based Approach to Semantically Enhanced Question Answering for Closed Domain: a Review
information Review Ontology-Based Approach to Semantically Enhanced Question Answering for Closed Domain: A Review Ammar Arbaaeen 1,∗ and Asadullah Shah 2 1 Department of Computer Science, Faculty of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia 2 Faculty of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia; [email protected] * Correspondence: [email protected] Abstract: For many users of natural language processing (NLP), it can be challenging to obtain concise, accurate and precise answers to a question. Systems such as question answering (QA) enable users to ask questions and receive feedback in the form of quick answers to questions posed in natural language, rather than in the form of lists of documents delivered by search engines. This task is challenging and involves complex semantic annotation and knowledge representation. This study reviews the literature detailing ontology-based methods that semantically enhance QA for a closed domain, by presenting a literature review of the relevant studies published between 2000 and 2020. The review reports that 83 of the 124 papers considered acknowledge the QA approach, and recommend its development and evaluation using different methods. These methods are evaluated according to accuracy, precision, and recall. An ontological approach to semantically enhancing QA is found to be adopted in a limited way, as many of the studies reviewed concentrated instead on Citation: Arbaaeen, A.; Shah, A. NLP and information retrieval (IR) processing. While the majority of the studies reviewed focus on Ontology-Based Approach to open domains, this study investigates the closed domain. -
Web Search Result Clustering with Babelnet
Web Search Result Clustering with BabelNet Marek Kozlowski Maciej Kazula OPI-PIB OPI-PIB [email protected] [email protected] Abstract 2 Related Work In this paper we present well-known 2.1 Search Result Clustering search result clustering method enriched with BabelNet information. The goal is The goal of text clustering in information retrieval to verify how Babelnet/Babelfy can im- is to discover groups of semantically related docu- prove the clustering quality in the web ments. Contextual descriptions (snippets) of docu- search results domain. During the evalua- ments returned by a search engine are short, often tion we tested three algorithms (Bisecting incomplete, and highly biased toward the query, so K-Means, STC, Lingo). At the first stage, establishing a notion of proximity between docu- we performed experiments only with tex- ments is a challenging task that is called Search tual features coming from snippets. Next, Result Clustering (SRC). Search Results Cluster- we introduced new semantic features from ing (SRC) is a specific area of documents cluster- BabelNet (as disambiguated synsets, cate- ing. gories and glosses describing synsets, or Approaches to search result clustering can be semantic edges) in order to verify how classified as data-centric or description-centric they influence on the clustering quality (Carpineto, 2009). of the search result clustering. The al- The data-centric approach (as Bisecting K- gorithms were evaluated on AMBIENT Means) focuses more on the problem of data clus- dataset in terms of the clustering quality. tering, rather than presenting the results to the user. Other data-centric methods use hierarchical 1 Introduction agglomerative clustering (Maarek, 2000) that re- In the previous years, Web clustering engines places single terms with lexical affinities (2-grams (Carpineto, 2009) have been proposed as a solu- of words) as features, or exploit link information tion to the issue of lexical ambiguity in Informa- (Zhang, 2008). -
The Use of a Semantic Network in a Deductive Question-Answering System
THE USE OF A SEMANTIC NETWORK IN A DEDUCTIVE QUESTION-ANSWERING SYSTEM James R. McSkimin .Jack Minker Bell Telephone Laboratories Department of Computer Science Columbus, Ohio University of Maryland 43209 College Park, Maryland 20742 Abstract tions. Thus, it should be possible to deter• mine that a query such as "Who is the person The use of a semantic network to aid in the who is both father and the mother of a given deductive search process of a Question-Answering individual?n is not answerable. System is described. The semantic network is (3) To identify those queries which have a known based on an adaptation of the predicate calculus. maximum number of solutions so as to termi• It makes available user-supplied, domain-dependent nate searches for additional answers once the info mat ion so as to permit semantic data to be known fixed number is found. used during the search process. The semantic network discussed in this paper Three ways are discussed in which semantic is described briefly in Section 2. In Section 3, information may be used. These are: we describe how the semantic network may be used (a) To apply semantic information during the to solve some of the problems associated with the pattern-matching process. above items. An example which illustrates the (b) To apply semantic well-formedness tests to use of the semantic network is presented in query and data inputs. Section 4. A summary of the work and future (c) To determine when subproblems are fully- directions is given in Section 5. solved (i.e., they have no solutions other than a fixed, finite number). -
New Generation of Social Networks Based on Semantic Web Technologies: the Importance of Social Data Portability
Workshop on Adaptation and Personalization for Web 2.0, UMAP'09, June 22-26, 2009 New Generation of Social Networks Based on Semantic Web Technologies: the Importance of Social Data Portability Liana Razmerita1, Martynas Jusevičius2, Rokas Firantas2 Copenhagen Business School, Denmark1 IT University of Copenhagen, Copenhagen, Denmark2 [email protected], [email protected], [email protected] Abstract. This article investigates several well-known social network applications such as Last.fm, Flickr and identifies social data portability as one of the main technical issues that need to be addressed in the future. We argue that this issue can be addressed by building social networks as Semantic Web applications with FOAF, SIOC, and Linked Data technologies, and prove it by implementing a prototype application using Java and core Semantic Web standards. Furthermore, the developed prototype shows how features from semantic websites such as Freebase and DBpedia can be reused in social applications and lead to more relevant content and stronger social connections. 1 Introduction Social networking sites are developing at a very fast pace, attracting more and more users. Their application domain can be music sharing, photos sharing, videos sharing, bookmarks sharing, professional networks, and others. Despite their tremendous success social networking websites have a number of limitations that are identified and discussed within this article. Most of the social networking applications are “walled websites” and the “online communities are like islands in a sea”[1]. Lack of interoperability between data in different social networks applications limits access to relevant content available on different social networking sites, and limits the integration and reuse of available data and information.