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Non-deductive reasoning for the semantic web and software analysis


Kiefer, C. Non-deductive reasoning for the semantic web and software analysis. 2008, University of Zurich, Faculty of Economics.

Abstract

The Semantic Web uses a number of knowledge representation (KR) languages to represent the terminological knowledge of a domain in a structured and formally sound way. Such KRs are typically description logics (DL), which are a particular kind of knowledge representation languages. One of the underpinnings of the Semantic Web
and, therefore, a strength of any such semantic architecture, is the ability to reason from data, that is, to derive new knowledge from basic facts. In other words, the information that is already known and stored in the knowledgebase is extended with the information that can be logically deduced from the ground truth.

The world does, however, generally not fit into a fixed, predetermined logic system of zeroes and ones. To account for this, especially in order to deal with the uncertainty inherent in the physical world, different models of human
reasoning are required. Two prominent ways to model human reasoning are similarity reasoning (aka analogical reasoning) and inductive reasoning. It has been shown in recent years that the notion of similarity plays an important role in a number of Semantic Web tasks, such as Semantic Web service matchmaking, similarity-based service discovery, and ontology alignment. With inductive
reasoning, two prominent tasks that can benefit from the use of statistical induction techniques are Semantic Web service classification and (semi-) automatic semantic data annotation.

This dissertation transfers these ideas to the Semantic Web. To this end, it extends the well-known RDF query language SPARQL with two novel, non-deductive reasoning extensions in order to enable similarity and inductive reasoning. To address these issues, specifically to implement the two novel reasoning variants by using SPARQL, we introduce the concept of virtual triple patterns. Virtual triples are not asserted but inferred. Hence, they do not exist in the knowledgebase, but, rather, only as a result of the similarity/inductive reasoning process.

To address similarity reasoning, we present the iSPARQL (imprecise SPARQL) framework---an extension of traditional SPARQL that supports customized similarity strategies via virtual triple patterns in order to explore an RDF dataset for similar resources. For our inductive reasoning extension, we introduce our SPARQL-ML (SPARQL Machine Learning) approach to create and work with statistical induction/data mining models in traditional SPARQL.

Our presented iSPARQL and SPARQL-ML frameworks are validated using five different case studies of heavily researched Semantic Web and Software Analysis tasks.
For the Semantic Web, these tasks are semantic service matchmaking, service discovery, and service classification. For Software Analysis, we conduct some experiments in software evolution and bug prediction. By applying our approaches to this large number of different tasks, we hope to show the approaches' generality, ease-of-use, extensibility, and high degree of flexibility in terms of customization to the actual task.

Abstract

The Semantic Web uses a number of knowledge representation (KR) languages to represent the terminological knowledge of a domain in a structured and formally sound way. Such KRs are typically description logics (DL), which are a particular kind of knowledge representation languages. One of the underpinnings of the Semantic Web
and, therefore, a strength of any such semantic architecture, is the ability to reason from data, that is, to derive new knowledge from basic facts. In other words, the information that is already known and stored in the knowledgebase is extended with the information that can be logically deduced from the ground truth.

The world does, however, generally not fit into a fixed, predetermined logic system of zeroes and ones. To account for this, especially in order to deal with the uncertainty inherent in the physical world, different models of human
reasoning are required. Two prominent ways to model human reasoning are similarity reasoning (aka analogical reasoning) and inductive reasoning. It has been shown in recent years that the notion of similarity plays an important role in a number of Semantic Web tasks, such as Semantic Web service matchmaking, similarity-based service discovery, and ontology alignment. With inductive
reasoning, two prominent tasks that can benefit from the use of statistical induction techniques are Semantic Web service classification and (semi-) automatic semantic data annotation.

This dissertation transfers these ideas to the Semantic Web. To this end, it extends the well-known RDF query language SPARQL with two novel, non-deductive reasoning extensions in order to enable similarity and inductive reasoning. To address these issues, specifically to implement the two novel reasoning variants by using SPARQL, we introduce the concept of virtual triple patterns. Virtual triples are not asserted but inferred. Hence, they do not exist in the knowledgebase, but, rather, only as a result of the similarity/inductive reasoning process.

To address similarity reasoning, we present the iSPARQL (imprecise SPARQL) framework---an extension of traditional SPARQL that supports customized similarity strategies via virtual triple patterns in order to explore an RDF dataset for similar resources. For our inductive reasoning extension, we introduce our SPARQL-ML (SPARQL Machine Learning) approach to create and work with statistical induction/data mining models in traditional SPARQL.

Our presented iSPARQL and SPARQL-ML frameworks are validated using five different case studies of heavily researched Semantic Web and Software Analysis tasks.
For the Semantic Web, these tasks are semantic service matchmaking, service discovery, and service classification. For Software Analysis, we conduct some experiments in software evolution and bug prediction. By applying our approaches to this large number of different tasks, we hope to show the approaches' generality, ease-of-use, extensibility, and high degree of flexibility in terms of customization to the actual task.

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Additional indexing

Item Type:Dissertation
Referees:Bernstein A, Gall H
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Date:21 January 2008
Deposited On:05 Mar 2009 10:22
Last Modified:06 Dec 2017 18:47
Number of Pages:205
Related URLs:http://opac.nebis.ch/F/?local_base=NEBIS&con_lng=GER&func=find-b&find_code=SYS&request=005728312

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