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A federated query answering system f...

Li, Yingjie.

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A federated query answering system for semantic web data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A federated query answering system for semantic web data./
作者:	Li, Yingjie.
面頁冊數:	220 p.
附註:	Source: Dissertation Abstracts International, Volume: 74-05(E), Section: B.
Contained By:	Dissertation Abstracts International74-05B(E).
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3550255
ISBN:	9781267872531

A federated query answering system for semantic web data.
Li, Yingjie.

A federated query answering system for semantic web data. - 220 p.

Source: Dissertation Abstracts International, Volume: 74-05(E), Section: B.

Thesis (Ph.D.)--Lehigh University, 2013.

The Semantic Web extends the Web as a global information space from a Web of documents to a Web of data. Currently, there are billions of triples publicly available in the web data space of different domains. These data become more tightly interrelated as the number of links in the form of mappings is also growing. Typically, these data are heterogeneous, distributed and prone to dynamic changes. Although centralized knowledge bases and/or triple stores can be used to collect and query large volumes of heterogeneous Semantic Web data, they suffer from many disadvantages. First, they will become stale unless they are frequently reloaded with fresh data. Second, they can require significant disk space, especially for triple stores that use multiple triple indices to optimize queries. Finally, there may be legal or policy issues that prevent one from copying data or storing it in a centralized place. Therefore, this dissertation explores ways to address the above challenges from the perspective of building a federated query answering system for semantic web data. The system can quickly and effectively find relevant data sources and further answer queries. It employs an automated mechanism for creating an inverted index used in determining source relevance. Then, a hybrid approach to answering queries that involves ideas from information retrieval, information integration and knowledge bases is applied.

ISBN: 9781267872531Subjects--Topical Terms:

523869
Computer science.

A federated query answering system for semantic web data.
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245 1 2 $a A federated query answering system for semantic web data.
300 $a 220 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-05(E), Section: B.
500 $a Adviser: Jeffrey D. Heflin.
502 $a Thesis (Ph.D.)--Lehigh University, 2013.
520 $a The Semantic Web extends the Web as a global information space from a Web of documents to a Web of data. Currently, there are billions of triples publicly available in the web data space of different domains. These data become more tightly interrelated as the number of links in the form of mappings is also growing. Typically, these data are heterogeneous, distributed and prone to dynamic changes. Although centralized knowledge bases and/or triple stores can be used to collect and query large volumes of heterogeneous Semantic Web data, they suffer from many disadvantages. First, they will become stale unless they are frequently reloaded with fresh data. Second, they can require significant disk space, especially for triple stores that use multiple triple indices to optimize queries. Finally, there may be legal or policy issues that prevent one from copying data or storing it in a centralized place. Therefore, this dissertation explores ways to address the above challenges from the perspective of building a federated query answering system for semantic web data. The system can quickly and effectively find relevant data sources and further answer queries. It employs an automated mechanism for creating an inverted index used in determining source relevance. Then, a hybrid approach to answering queries that involves ideas from information retrieval, information integration and knowledge bases is applied.
520 $a First, the dissertation formally defines a group of concepts to describe a federated query answering problem for the Semantic Web. Guided by the theoretical framework, it then presents and implements an efficient, IR-inspired inverted index named term index to integrate semantic web data sources and determine source relevance. Based on this term index, four query answering algorithms are proposed. Each of them is optimized in order to overcome the drawbacks of the previous ones. The non-structure algorithm takes a set of query subgoals as inputs and dynamically loads all relevant sources into a reasoner to solve the original query. The flat-structure algorithm optimizes source selection and dynamically answers queries by reformulating the original conjunctive query into a list of conjunctive query rewritings. The tree-structure algorithm answers queries by reformulating the original conjunctive query into an AND/OR tree, generating a query execution plan on the fly and dynamically executing a bottom-up greedy source collection. The dynamic cyclic axiom handling algorithm is to make the tree-structure algorithm still return complete query answers when cyclic axioms are considered. Experiments conducted using synthetic data and real world data and the theoretical correctness proof of algorithms have demonstrated that a system based on these algorithms can effectively and correctly scale to dynamic, web-scale knowledge bases.
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