Chapter 17 Fuzzy logic aggregation for semantic web search for the best (top-k) answer

doi:10.1016/S1574-9576(06)80019-5

Capturing Intelligence

Volume 1, 2006, Pages 341-359

https://doi.org/10.1016/S1574-9576(06)80019-5 Get rights and content

Abstract

The paper is dealing with the problem of finding best (top-k) answers depending on scoring multicriterial user requirements which appears quite often on the web. Our solution is based on formal models of many valued logic (fuzzy, annotated) where user preference (score) is modeled by ordering of truth values — meaning best answer is the one with biggest truth value. The user global score is modeled by fuzzy aggregation (annotation) operators of all user attribute score. In this paper, we present several many valued definite logic programming based techniques with fuzzy aggregation. We present models for both deductive and inductive tasks and also an extension with similarities and finite domains. We advocate for an extension of web rule languages by fuzzy aggregation.

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Recent years have witnessed unprecedented volumes of structured data published in RDF format. Taking full advantage of such data has attracted a growing amount of research interest from both academia and industry. However, efficient processing of top-k queries in RDF data is still a new topic. Most existing approaches ignore top-k queries, or only provide a limited number of ranking functions. In this paper, we provide an effective and efficient processing algorithm for top-k queries that consists of a novel tree-style index MS-tree and MS-tree-based filtering and pattern-matching functions. Firstly, candidate entities in RDF data were efficiently ranked and filtered through an MS-tree-based top-down method. Then, query structure patterns are matched in RDF data through graphic exploration. In order to handle more complex scoring functions, a dynamic variable selecting optimization is employed to accelerate the threshold decrease. We evaluate our solutions with both synthetic and real-world datasets. The experimental results show that our model significantly outperforms state-of-the-art approaches.
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Citation Excerpt :
While there are many works addressing the top-k problem for vague queries in databases (see e.g., [14] for a survey), little is known for the corresponding problem in knowledge representation and reasoning. For instance, [36] considers non-recursive fuzzy logic programs in which the score combination function is a function of the score of the atoms in the body only (no scoring predicates are allowed). The work [25] considers non-recursive fuzzy logic programs as well, though the score combination function may consider scoring predicates.
We address the problem of evaluating ranked top-k queries in the context of ontology mediated access over relational databases. An ontology layer is used to define the relevant abstract concepts and relations of the application domain, while facts with associated score are stored into a relational database. Queries are conjunctive queries with ranking aggregates and scoring functions. The results of a query may be ranked according to the score and the problem is to find efficiently the top-k ranked query answers.
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^*: Supported by Czech IT projects 1ET100300517 and 1ET100300419.

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Chapter 17 Fuzzy logic aggregation for semantic web search for the best (top-k) answer

Abstract

Artificial Intelligence

J. Comput. System Sci.

J. Comput. System Sci.

J. Logic Programming

Fuzzy Sets and Systems

Fuzzy Sets and Systems

Inform. and Control

Fuzzy Sets and Systems

Description logic for databases

The skyline operator

Learning qualitative models

AI Magazine

An introduction to inductive logic programming

Combining answer set programming with description logics for the semantic web

Description logic programs: combining logic programs with description logic

Optimizing multi-feature queries for image databases