Clustering Ontology-Based Metadata in the Semantic Web

Maedche, Alexander; Zacharias, Valentin

doi:10.1007/3-540-45681-3_29

Cited by 137 publications

(77 citation statements)

References 2 publications

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“…Yet there are several differences between (Maedche and Staab 2000) and the present work: (Maedche and Staab 2000) is conceived for Ontology Extraction instead of Ontology Refinement, uses generalized association patterns (bottom-up search) instead of multi-level association patterns (top-down search), adopts propositional logic instead of FOL. Within the same application area, (Maedche and Zacharias 2002) proposes a distance-based method for clustering in RDF which is not conceptual. Also the relation between Frequent Pattern Discovery and Concept Formation as such has never been investigated.…”

Section: Discussionmentioning

confidence: 99%

Building Rules on Top of Ontologies for the Semantic Web with Inductive Logic Programming

Lisi

2008

Theory and Practice of Logic Programming

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Building rules on top of ontologies is the ultimate goal of the logical layer of the Semantic Web. To this aim an ad-hoc mark-up language for this layer is currently under discussion. It is intended to follow the tradition of hybrid knowledge representation and reasoning systems such as AL-log that integrates the description logic ALC and the function-free Horn clausal language Datalog. In this paper we consider the problem of automating the acquisition of these rules for the Semantic Web. We propose a general framework for rule induction that adopts the methodological apparatus of Inductive Logic Programming and relies on the expressive and deductive power of AL-log. The framework is valid whatever the scope of induction (description vs. prediction) is. Yet, for illustrative purposes, we also discuss an instantiation of the framework which aims at description and turns out to be useful in Ontology Refinement.

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Section: Discussionmentioning

confidence: 99%

Building Rules on Top of Ontologies for the Semantic Web with Inductive Logic Programming

Lisi

2008

Theory and Practice of Logic Programming

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“…They stated that the ontology can improve document clustering performance with its concept hierarchy knowledge. This system integrates core ontologies as background knowledge into the process of clustering [14,15].…”

Section: Concept or Feature Weightingmentioning

confidence: 99%

Study of Ontology or Thesaurus Based Document Clustering and Information Retrieval

Bharathi¹,

Venkatesan²

2012

J. of Engineering and Applied Sciences

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Document clustering generates clusters from the whole document collection automatically and is used in many fields, including data mining and information retrieval. Clustering text data faces a number of new challenges. Among others, the volume of text data, dimensionality, sparsity and complex semantics are the most important ones. These characteristics of text data require clustering techniques to be scalable to large and high dimensional data, and able to handle sparsity and semantics. In the traditional vector space model, the unique words occurring in the document set are used as the features. But because of the synonym problem and the polysemous problem, such a bag of original words cannot represent the content of a document precisely. Most of the existing text clustering methods use clustering techniques which depend only on term strength and document frequency where single terms are used as features for representing the documents and they are treated independently which can be easily applied to non-ontological clustering. To overcome the above issues, this paper makes a survey of recent research done on ontology or thesaurus based document clustering.

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“…More precisely, the concept similarity (CS) value is calculated based on Concept Match and Upwards Cotopy, proposed by Maedche, et al 6) . We have made a subtle extension to their definition in order to be able to compute between concepts, as Maedche, et al defined the equations for computing similarity between what they call "instances".…”

Section: Item Matchingmentioning

confidence: 99%

“…The final similarity score (semantic similarity of item; SOI) is obtained by summing the concept similarity (CS) and the attribute similarity (AS) scores as shown in Eq. (6). The value of SOI ranges from 0 to 2, where 0 represents non-match and 2 represents exact match between the two items.…”

Section: Cs(cmentioning

confidence: 99%

A Multi-faceted Approach for Searching Web Applications

Usanavasin

Nakamori

Takada

et al. 2005

ipsjdc

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Today the WWW contains not only a tremendous amount of information but also a variety of Web applications, such as online shopping. Searching for such applications can be difficult and time consuming because current keyword-based approaches do not always deliver results that match the user's intention. The key underlying problem is that keywords do not capture the semantics of the user's query and the functional capabilities of Web applications. This paper presents a multi-faceted approach for searching Web applications. In our approach, the search process is based on various facets, such as service functionality, item (product), result type, inputs/outputs, and the detailed information of items. It is based on matching queries and Web application profiles that are described in DAML-S. The matching process is augmented with the use of ontologies. The result of applying our approach to a set of Web applications resulted in a precision of 93% and a recall of 99%.

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Clustering Ontology-Based Metadata in the Semantic Web

Cited by 137 publications

References 2 publications

Building Rules on Top of Ontologies for the Semantic Web with Inductive Logic Programming

Building Rules on Top of Ontologies for the Semantic Web with Inductive Logic Programming

Study of Ontology or Thesaurus Based Document Clustering and Information Retrieval

A Multi-faceted Approach for Searching Web Applications

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