skip to content
Reasoning and querying the semantic Web: A document-centric perspective. Preview this item
ClosePreview this item
Checking...

Reasoning and querying the semantic Web: A document-centric perspective.

Author: Yuanbo Guo
Publisher: 2007.
Dissertation: Thesis (Ph.D.)--Lehigh University, 2007.
Edition/Format:   Thesis/dissertation : Thesis/dissertation : Manuscript   Archival Material : English
Database:WorldCat
Summary:
This dissertation discusses reasoning and querying Semantic Web data. We consider documents described in the standard OWL Web ontology language. We focus on reasoning and querying with respect to collections of OWL ABoxes, i.e., instance data.
Rating:

(not yet rated) 0 with reviews - Be the first.

Subjects
More like this

 

Find a copy online

Links to this item

Find a copy in the library

&AllPage.SpinnerRetrieving; Finding libraries that hold this item...

Details

Material Type: Thesis/dissertation, Manuscript, Internet resource
Document Type: Book, Archival Material, Internet Resource
All Authors / Contributors: Yuanbo Guo
ISBN: 9780549107552 054910755X
OCLC Number: 237189733
Notes: Adviser: Jeff Heflin.
Description: 246 p.
More information:

Abstract:

This dissertation discusses reasoning and querying Semantic Web data. We consider documents described in the standard OWL Web ontology language. We focus on reasoning and querying with respect to collections of OWL ABoxes, i.e., instance data.

We propose a framework for document-centric query answering for the Semantic Web. The idea is novel in that it extends traditional queries on logical knowledge bases by integrating the notion of documents. We consider two broad types of queries: document entailment queries, which are concerned with what assertions are entailed by a specific subset of documents in the knowledge base, and document provenance queries, which ask for the minimal consistent subsets of documents in order for specific assertions to hold.

We develop algorithms to support the above queries in a knowledge base system. We adopt a preprocessing strategy that reasons with documents and caches selected results. This allows us to reuse expensive OWL reasoning at query time and reduce query answering to a simple semantic network-like inference procedure. In addition, we make use of an assumption-based truth maintenance system to represent the contexts of an assertion, i.e., minimal consistent subsets of documents that entail the assertion, as well as recording information about inconsistent document subsets.

Another issue we try to address is scalability, which is crucial for the Semantic Web to succeed. Reasoning with OWL is highly expensive, and moreover, the scale of the Semantic Web poses great challenges. We explore ways that could help improve the scalability of Semantic Web knowledge base systems that need to handle large scales of data. First, we examine the logical relationships, i.e., logical dependence and logical independence, between OWL knowledge bases. On the one hand, we introduce a set of theorems that state conditions under which logical independence is guaranteed. At the same time, we present an algorithm that detects logical independence for the general case. On the other hand, we set up a theoretic framework for identifying logical dependence in terms of how collections of knowledge bases may together lead to new inferences.

In addition, we describe a scalable and practical approach for partitioning large OWL ABoxes so that specific kinds of reasoning can be performed separately on each partition, and additionally the results can be combined in order to answer conjunctive queries. The main features of our approach include: a reasonable tradeoff between the complexity of determining partitions and the granularity of partitioning; worst-case polynomial time complexity of partitioning; and the ability to handle problems that are too large for main memory. In addition, we show promising experimental results on both the LUBM (Lehigh University Benchmark) data and the FOAF (The Friend of a Friend) data collected from the Web.

Moreover, in order to further improve the query answering system, we apply and extend the above partitioning approach in order to cut down the number of document sets we need to reason with during preprocessing. Also, we give algorithms for answering document-centric queries that are extensions to conjunctive ABox queries. Finally, empirical experiments on both the LUBM data and the FOAF data demonstrate satisfactory results, in particular, good scalability of the system.

Reviews

User-contributed reviews
Retrieving GoodReads reviews...
Retrieving DOGObooks reviews...

Tags

Be the first.

Similar Items

Related Subjects:(1)

Confirm this request

You may have already requested this item. Please select Ok if you would like to proceed with this request anyway.

Linked Data


<http://www.worldcat.org/oclc/237189733>
library:oclcnum"237189733"
owl:sameAs<info:oclcnum/237189733>
rdf:typej.1:Thesis
rdf:typeschema:Book
schema:about
schema:creator
schema:datePublished"2007"
schema:description"We develop algorithms to support the above queries in a knowledge base system. We adopt a preprocessing strategy that reasons with documents and caches selected results. This allows us to reuse expensive OWL reasoning at query time and reduce query answering to a simple semantic network-like inference procedure. In addition, we make use of an assumption-based truth maintenance system to represent the contexts of an assertion, i.e., minimal consistent subsets of documents that entail the assertion, as well as recording information about inconsistent document subsets."
schema:description"This dissertation discusses reasoning and querying Semantic Web data. We consider documents described in the standard OWL Web ontology language. We focus on reasoning and querying with respect to collections of OWL ABoxes, i.e., instance data."
schema:description"Moreover, in order to further improve the query answering system, we apply and extend the above partitioning approach in order to cut down the number of document sets we need to reason with during preprocessing. Also, we give algorithms for answering document-centric queries that are extensions to conjunctive ABox queries. Finally, empirical experiments on both the LUBM data and the FOAF data demonstrate satisfactory results, in particular, good scalability of the system."
schema:description"We propose a framework for document-centric query answering for the Semantic Web. The idea is novel in that it extends traditional queries on logical knowledge bases by integrating the notion of documents. We consider two broad types of queries: document entailment queries, which are concerned with what assertions are entailed by a specific subset of documents in the knowledge base, and document provenance queries, which ask for the minimal consistent subsets of documents in order for specific assertions to hold."
schema:description"Another issue we try to address is scalability, which is crucial for the Semantic Web to succeed. Reasoning with OWL is highly expensive, and moreover, the scale of the Semantic Web poses great challenges. We explore ways that could help improve the scalability of Semantic Web knowledge base systems that need to handle large scales of data. First, we examine the logical relationships, i.e., logical dependence and logical independence, between OWL knowledge bases. On the one hand, we introduce a set of theorems that state conditions under which logical independence is guaranteed. At the same time, we present an algorithm that detects logical independence for the general case. On the other hand, we set up a theoretic framework for identifying logical dependence in terms of how collections of knowledge bases may together lead to new inferences."
schema:description"In addition, we describe a scalable and practical approach for partitioning large OWL ABoxes so that specific kinds of reasoning can be performed separately on each partition, and additionally the results can be combined in order to answer conjunctive queries. The main features of our approach include: a reasonable tradeoff between the complexity of determining partitions and the granularity of partitioning; worst-case polynomial time complexity of partitioning; and the ability to handle problems that are too large for main memory. In addition, we show promising experimental results on both the LUBM (Lehigh University Benchmark) data and the FOAF (The Friend of a Friend) data collected from the Web."
schema:exampleOfWork<http://worldcat.org/entity/work/id/140830497>
schema:inLanguage"en"
schema:name"Reasoning and querying the semantic Web: A document-centric perspective."
schema:numberOfPages"246"
schema:url
schema:workExample

Content-negotiable representations

Close Window

Please sign in to WorldCat 

Don't have an account? You can easily create a free account.