Wiki'mantics: interpreting ontologies with WikipediA

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Wiki'mantics: interpreting ontologies with WikipediA
Authors: Bo Hu [edit item]
Citation: Knowledge and Information Systems 25 (3): 445. 2010 December.
Publication type: Journal article
Peer-reviewed: Yes
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DOI: Define doi.
Google Scholar cites: Citations
Link(s): Paper link
Added by Wikilit team: Added on initial load
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Wiki'mantics: interpreting ontologies with WikipediA is a publication by Bo Hu.


[edit] Abstract

In the context of the Semantic Web, many ontology-related operations can be boiled down to one fundamental task: finding as accurately as possible the semantics hiding beneath the superficial representation of ontological entities. This, however, is not an easy task due to the ambiguous nature of semantics and a lack of systematic engineering method to guide how we comprehend semantics. We acknowledge the gap between human cognition and knowledge representation formalisms: even though precise logic formulae can be used as the canonical representation of ontological entities, understanding of such formulae may vary. A feasible solution to juxtaposing semantics interpretation, therefore, is to reflect such cognitive variations. In this paper, we propose an approximation of semantics using sets of words/phrases, referred to as WKmantic vectors. These vectors are emerged through a set of well-tuned methods gradually surfacing the semantics that remain implicit otherwise. Given a concept, we first identify its conceptual niche amongst its neighbours in the graph representation of the ontology. We generate a natural language paraphrases of the isolated sub-graph and project this textual description upon a large document repository. WKmantic vectors are then drawn from the document repository. We evaluated each of the aforementioned steps by way of user study.

[edit] Research questions

"How can we find out the meanings of a concept? we propose an approximation of semantics using sets of words/phrases, referred to as WiKimantic vectors. These vectors are emerged through a set of well-tuned methods gradually surfacing the semantics that remain implicit otherwise."

Research details

Topics: Ontology building [edit item]
Domains: Computer science [edit item]
Theory type: Design and action [edit item]
Wikipedia coverage: Other [edit item]
Theories: "Undetermined" [edit item]
Research design: Experiment [edit item]
Data source: Experiment responses, Wikipedia pages [edit item]
Collected data time dimension: Cross-sectional [edit item]
Unit of analysis: Article [edit item]
Wikipedia data extraction: Dump [edit item]
Wikipedia page type: Article [edit item]
Wikipedia language: English, French [edit item]

[edit] Conclusion

"This paper investigated a method that combines conceptualisations together with the emerging new web resources. More specifically, we see ontologies as undirected and weighted graphs which are partitioned to identify the niche of a concept, that is the network/web community closely associated with the target concept. We employ conceptualisation preserving weighting schema to estimate how significant these other ontological entities contribute to constructing the target concept. These significance values are then fed to the niching algorithm. Paraphrases are generated for every concept niche. In order to minimise the intraand inter-individual modelling variation, niche paraphrases are projected upon to a carefully crafted text corpus composed by selected WikipediA articles.We refer to the resultant sets of WikipediA-enhanced concept descriptors as WiKimantic vectors which can facilitate further ontology engineering operations. We also listed potential applications that can benefit from the WiKimantic vectors."

[edit] Comments

"we see ontologies as undirected and weighted graphs which are partitioned to identify the niche of a concept, that is the network/web community closely associated with the target concept."


Further notes[edit]

Facts about "Wiki'mantics: interpreting ontologies with WikipediA"RDF feed
AbstractIn the context of the Semantic Web, many oIn the context of the Semantic Web, many ontology-related operations can be boiled down to one fundamental task: finding as accurately as possible the semantics hiding beneath the superficial representation of ontological entities. This, however, is not an easy task due to the ambiguous nature of semantics and a lack of systematic engineering method to guide how we comprehend semantics. We acknowledge the gap between human cognition and knowledge representation formalisms: even though precise logic formulae can be used as the canonical representation of ontological entities, understanding of such formulae may vary. A feasible solution to juxtaposing semantics interpretation, therefore, is to reflect such cognitive variations. In this paper, we propose an approximation of semantics using sets of words/phrases, referred to as WKmantic vectors. These vectors are emerged through a set of well-tuned methods gradually surfacing the semantics that remain implicit otherwise. Given a concept, we first identify its conceptual niche amongst its neighbours in the graph representation of the ontology. We generate a natural language paraphrases of the isolated sub-graph and project this textual description upon a large document repository. WKmantic vectors are then drawn from the document repository. We evaluated each of the aforementioned steps by way of user study.aforementioned steps by way of user study.
Added by wikilit teamAdded on initial load +
Collected data time dimensionCross-sectional +
Commentswe see ontologies as undirected and weighted graphs which are partitioned to identify the niche of a concept, that is the network/web community closely associated with the target concept.
ConclusionThis paper investigated a method that combThis paper investigated a method that combines conceptualisations together with the emerging

new web resources. More specifically, we see ontologies as undirected and weighted graphs which are partitioned to identify the niche of a concept, that is the network/web community closely associated with the target concept. We employ conceptualisation preserving weighting schema to estimate how significant these other ontological entities contribute to constructing the target concept. These significance values are then fed to the niching algorithm. Paraphrases are generated for every concept niche. In order to minimise the intraand inter-individual modelling variation, niche paraphrases are projected upon to a carefully crafted text corpus composed by selected WikipediA articles.We refer to the resultant sets of WikipediA-enhanced concept descriptors as WiKimantic vectors which can facilitate further ontology engineering operations. We also listed potential applications that can benefit from the WiKimantic vectors.t can benefit from

the WiKimantic vectors.
Data sourceExperiment responses + and Wikipedia pages +
Google scholar urlhttp://scholar.google.com/scholar?ie=UTF-8&q=%22Wiki%27mantics%3A%2Binterpreting%2Bontologies%2Bwith%2BWikipediA%22 +
Has authorBo Hu +
Has domainComputer science +
Has topicOntology building +
Issue3 +
MonthDecember +
Pages445 +
Peer reviewedYes +
Publication typeJournal article +
Published inKnowledge and Information Systems +
Research designExperiment +
Research questionsHow can we find out the meanings of a concHow can we find out the meanings of a concept? we propose an approximation of semantics using sets of words/phrases, referred to as WiKimantic vectors. These vectors are emerged through a set of well-tuned methods gradually surfacing the semantics that remain implicit otherwise. semantics that remain implicit otherwise.
Revid11,061 +
TheoriesUndetermined
Theory typeDesign and action +
TitleWiki'mantics: interpreting ontologies with WikipediA
Unit of analysisArticle +
Urlhttp://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4118648&tag=1 +
Volume25 +
Wikipedia coverageOther +
Wikipedia data extractionDump +
Wikipedia languageEnglish + and French +
Wikipedia page typeArticle +
Year2010 +