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RISS 인기검색어
- 검색키워드
- 저자 : Nansu Zong (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
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Constructing faceted taxonomy for heterogeneous entities based on object properties in linked data
Zong, Nansu,Kim, Hong-Gee,Nam, Sejin Elsevier 2017 Data & knowledge engineering Vol.112 No.-
<P><B>Abstract</B></P> <P>The interlinking of data across the web, a concept known as Linked Data, fosters opportunities in data sharing and reusability. However, it may also pose some challenges, which includes the absence of concept taxonomies by which to organize heterogeneous entities that are from different data sources and diverse domains. Learning T-Box (Terminology Box) from A-Box (Assertion Box) has been studied to provide users with concept taxonomies, and is considered a better solution than mapping Linked Data sets with published ontologies. Yet, the existing process of automatically generated taxonomies that classify entities in a particular manner can be improved. Thus, this study aims to automatically create a faceted taxonomy to organize heterogeneous entities, enabling varying classifications of entities by diverse sub-taxonomies, to support faceted search and navigation for linked data applications. The authors have developed a framework on which each facet represented by an object property is used to extract portions of data in the data space, and an Instance-based Concept Taxonomy generation algorithm is developed to build a sub-taxonomy. Additionally, the strategies for sub-taxonomy refinement are proposed. Two experiments have been conducted to prove the promising performances of the proposed method in terms of efficiency and effectiveness.</P> <P><B>Highlights</B></P> <P> <UL> <LI> We defined the notions of facet in a faceted taxonomy as well as the concept and its naming strategy for the taxonomy. </LI> <LI> We developed a faceted taxonomy construction algorithm, ICT, based on object properties of instances in Linked Data. </LI> <LI> The experiments show that ICT outperforms FCA-and Subsumption-based method in a single taxonomy construction. (new bullet point) The experiments show a dramatic reduction of the search space with the faceted taxonomy generated. </LI> </UL> </P>
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Discovering expansion entities for keyword-based entity search in linked data
Zong, Nansu,Lee, Sungin,Kim, Hong-Gee SAGE Publications 2015 JOURNAL OF INFORMATION SCIENCE Vol.41 No.2
<P>There is an inherent rift between the characteristics of Web of documents and the Web of data – the latter is enriched with semantic properties that are not present in the former. This creates a formidable challenge for entity search in the era of Linked Data, requiring a new method that leverages on such features. Query expansion, used in keyword-based search, improves search quality by enhancing a query with terms. Existing query-expansion methods, statistical- and lexical-based approaches, are inadequate for linked data in two ways: (a) term-to-term co-occurrence, used in the statistical-based approach, cannot find satisfactory expansions in internal corpus (SPO triples) or external corpus (Web of documents); and (b) lexical incomparability between ontologies (or thesauri) as reference knowledge and linked data renders tenuous the possibility of creating lexically sound expanded queries. The study introduces a framework to expand keyword queries with expansion entities for keyword-based entity search in linked data. The framework offers two structures, star-shaped and multi-shaped RDF graphs (documents), to model the entities in linked data for indexing and search, and an algorithm called PFC for expansion entities by which to expand a given query. PFC obtains expansion entities by measuring a global importance (PageRank and entity–document Frequency) and a local importance (Centrality) of the candidates extracted from the returned RDF documents of the entity search. Our experiments illustrate that PFC improves search results by approximately 7%. This study also includes suggestions on how to glean important link types for extracting candidate expansion entities, as well as identifying properties of these entities by which to expand the query.</P>
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시맨틱 웹 매쉬업 서비스를 위한 링크드 데이터 및 Open API 통합 연계 시스템의 설계 및 구현
정진욱(Jinuk Jung),임동혁(Dong-Hyuk Im),이경민(Kyungmin Lee),Nansu Zong,김홍기(Hong-gee Kim) 한국정보과학회 2012 한국정보과학회 학술발표논문집 Vol.39 No.1A
최근 웹 2.0과 시맨틱 웹의 대중화와 더불어 Open API와 링크드 데이터를 이용한 시맨틱 웹 융복합(매쉬업) 서비스가 주목을 받고 있다. 다양한 링크드 데이터와 Open API들을 조합함으로써 새로운 서비스들을 쉽고 빠르게 만드는 것이 가능하기 때문이다. 하지만 사용자가 링크드 데이터와 Open API 서비스를 사용하기 위해서는 서비스 입력 값이나 출력값 등의 해당 정보를 얻어야 하며 이를 위해 링크드 데이터와 Open API를 제공해 주는 사이트를 직접 방문해야만 하는 불편함을 가지게 된다. 본 논문에서는 시맨틱 웹 매쉬업 서비스를 위한 통합 링크드 데이터 및 Open API 관리 시스템을 설계하고 구현하였다. 제안한 시스템에서 사용자는 사전 지식 없이 통합 관리 시스템을 통해 원하는 링크드 데이터와 Open API 서비스를 검색하고 실행할 수 있다. 또한 실행된 결과는 XML 형태로 저장되어 추후 매쉬업 시 재사용이 가능하도록 한다.
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