Summarizing ontology-based schemas in PDMS

Pires, Carlos Eduardo Santos; Sousa, Paulo Fernando Silva; Kedad, Zoubida; Salgado, Ana Carolina

doi:10.1109/icdew.2010.5452706

Cited by 22 publications

(21 citation statements)

References 7 publications

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“…Metrics like precision, recall and F-measure, used by the previous works [8,[13][14][15], are limited in exhibiting the added value of a summarization system because of the "disagreement due to synonymy" [16] meaning that they fail to identify closeness with the ideal result when the results are not exactly the same with the reference ones. On the other hand, content based metrics compute the similarity between two summaries in a more reliable way [7].…”

Section: Discussionmentioning

confidence: 99%

“…In addition, some of these works (e.g. [8,13]) provide a list of the more important nodes, whereas others [7,8,14] and our approach, create a valid summary schema. Finally, other approaches try to navigate on the Linked Data Cloud using summaries of interlinked datasets [20].…”

Section: Related Workmentioning

confidence: 98%

“…Pires et al [14], propose an automatic method to summarize ontologies that represent schemas of peers participating in a peer-to-peer system. In order to determine the relevance of a concept, a combination two measures, centrality and frequency is used.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

RDF Digest: Efficient Summarization of RDF/S KBs

Troullinou¹,

Kondylakis²,

Daskalaki³

et al. 2015

The Semantic Web. Latest Advances and New Domains

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The exponential growth of the web and the extended use of semantic web technologies has brought to the fore the need for quick understanding, flexible exploration and selection of complex web documents and schemas. To this direction, ontology summarization aspires to produce an abridged version of the original ontology that highlights its most representative concepts. In this paper, we present RDF Digest, a novel platform that automatically produces summaries of RDF/S Knowledge Bases (KBs). A summary is a valid RDFS document/graph that includes the most representative concepts of the schema adapted to the corresponding instances. To construct this graph, our algorithm exploits the semantics and the structure of the schema and the distribution of the corresponding data/instances. The performed preliminary evaluation demonstrates the benefits of our approach and the considerable advantages gained.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 98%

See 1 more Smart Citation

RDF Digest: Efficient Summarization of RDF/S KBs

Troullinou¹,

Kondylakis²,

Daskalaki³

et al. 2015

The Semantic Web. Latest Advances and New Domains

View full text Add to dashboard Cite

show abstract

“…Besides that, only few efforts have been reported in the literature addressing the quality of the schema summarization methods in general [30,25,10], i.e. the quality of the RDF schema that can be obtained through RDF summarization.…”

Section: Structural Extraction Approachesmentioning

confidence: 99%

Quality metrics for RDF graph summarization

Zneika

Vodislav

Kotzinos

2019

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RDF Graph Summarization pertains to the process of extracting concise but meaningful summaries from RDF Knowledge Bases (KBs) representing as close as possible the actual contents of the KB. RDF Summarization allows for better exploration and visualization of the underlying RDF graphs, optimizstion of queries or query evaluation in multiple steps, better understanding of connections in Linked Datasets and many other applications. In the literature, there are efforts reported presenting algorithms for extracting summaries from RDF KBs. These efforts though provide different results while applied on the same KB, thus a way to compare the produced summaries and decide on their quality, in the form of a quality framework, is necessary. So in this work, we propose a comprehensive Quality Framework for RDF Graph Summarization that would allow a better, deeper and more complete understanding of the quality of the different summaries and facilitate their comparison. We work at two levels: the level of the ideal summary (or ideal schema) of the KB that could be provided by an expert user and the level of the instances contained by the KB. For the first level, we are computing how close the proposed summary is to the ideal solution (when this is available) by computing its precision and recall against the ideal solution. For the second level, we are computing if the existing instances are covered (i.e. can be retrieved) and in what degree by the proposed summary. We use our quality framework to test the results of three of the best RDF Graph Summarization algorithms, when summarizing different (in terms of content) and diverse (in terms of total size and number of instances, classes and predicates) KBs and we present comparative results for them. We conclude this work by discussing these results and the suitability of the proposed quality framework in order to get useful insights for the quality of the presented results.

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“…Similar methods have been used in [20,8]. Pirez et al [13] and Queiroz-Sousa et al [16] divide edges by their types into standard (e.g., is-a, part-of, same-as) and user-defined, which are weighted separately.…”

Section: Centrality-based Measuresmentioning

confidence: 99%

Graph-Based Methods for Ontology Summarization: A Survey

Pouriyeh

Allahyari

Liu

et al. 2018

2018 IEEE First International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)

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Ontologies have been widely used in numerous and varied applications, e.g., to support data modeling, information integration, and knowledge management. With the increasing size of ontologies, ontology understanding, which is playing an important role in different tasks, is becoming more difficult. Consequently, ontology summarization, as a way to distill key information from an ontology and generate an abridged version to facilitate a better understanding, is getting growing attention. In this survey paper, we review existing ontology summarization techniques and focus mainly on graph-based methods, which represent an ontology as a graph and apply centrality-based and other measures to identify the most important elements of an ontology as its summary. After analyzing their strengths and weaknesses, we highlight a few potential directions for future research.

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Summarizing ontology-based schemas in PDMS

Cited by 22 publications

References 7 publications

RDF Digest: Efficient Summarization of RDF/S KBs

RDF Digest: Efficient Summarization of RDF/S KBs

Quality metrics for RDF graph summarization

Graph-Based Methods for Ontology Summarization: A Survey

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