On Reducing Redundancy in Mining Relational Association Rules from the Semantic Web

Józefowska, Joanna; Ławrynowicz, Agnieszka; Łukaszewski, Tomasz

doi:10.1007/978-3-540-88737-9_16

Cited by 12 publications

(4 citation statements)

References 7 publications

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“…Θ-subsumption, however, does not capture the semantics of background knowledge. Neglecting the presence of background knowledge, may lead to inefficiency due to semantic redundancies in discovered patterns Józefowska, Lawrynowicz, & Lukaszewski (2008).…”

Section: Figure 1 General Overview Of the Proposed Pattern-based Clas...mentioning

confidence: 99%

Pattern Based Feature Construction in Semantic Data Mining

Ławrynowicz

Potoniec

2014

International Journal on Semantic Web and Information Systems

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The authors propose a new method for mining sets of patterns for classification, where patterns are represented as SPARQL queries over RDFS. The method contributes to so-called semantic data mining, a data mining approach where domain ontologies are used as background knowledge, and where the new challenge is to mine knowledge encoded in domain ontologies, rather than only purely empirical data. The authors have developed a tool that implements this approach. Using this the authors have conducted an experimental evaluation including comparison of our method to state-of-the-art approaches to classification of semantic data and an experimental study within emerging subfield of meta-learning called semantic meta-mining. The most important research contributions of the paper to the state-of-art are as follows. For pattern mining research or relational learning in general, the paper contributes a new algorithm for discovery of new type of patterns. For Semantic Web research, it theoretically and empirically illustrates how semantic, structured data can be used in traditional machine learning methods through a pattern-based approach for constructing semantic features.

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Section: Figure 1 General Overview Of the Proposed Pattern-based Clas...mentioning

confidence: 99%

Pattern Based Feature Construction in Semantic Data Mining

Ławrynowicz

Potoniec

2014

International Journal on Semantic Web and Information Systems

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“…Initial results on the algorithm development have been presented in Józefowska et al (2005Józefowska et al ( , 2008. This section advances them.…”

Section: Solution Algorithmmentioning

confidence: 99%

“…The main contribution of this paper is the algorithm for frequent pattern discovery in combined knowledge bases represented in DL-safe rules as described in Section 3. Initial results on the algorithm development have been presented in Józefowska et al (2005Józefowska et al ( , 2008. This section advances them.…”

Section: Solution Algorithmmentioning

confidence: 99%

The role of semantics in mining frequent patterns from knowledge bases in description logics with rules

Józefowska

Ławrynowicz

Łukaszewski

2010

Theory and Practice of Logic Programming

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We propose a new method for mining frequent patterns in a language that combines both Semantic Web ontologies and rules. In particular, we consider the setting of using a language that combines description logics (DLs) with DL-safe rules. This setting is important for the practical application of data mining to the Semantic Web. We focus on the relation of the semantics of the representation formalism to the task of frequent pattern discovery, and for the core of our method, we propose an algorithm that exploits the semantics of the combined knowledge base. We have developed a proof-of-concept data mining implementation of this. Using this we have empirically shown that using the combined knowledge base to perform semantic tests can make data mining faster by pruning useless candidate patterns before their evaluation. We have also shown that the quality of the set of patterns produced may be improved: the patterns are more compact, and there are fewer patterns. We conclude that exploiting the semantics of a chosen representation formalism is key to the design and application of (onto-)relational frequent pattern discovery methods.

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“…The main contribution of this paper is the algorithm for frequent pattern discovery in combined knowledge bases represented in DL-safe rules as described in Section 3. Initial results on the algorithm development have been presented in (Józefowska et al 2006;Józefowska et al 2008). This section advances them.…”

Section: Solution Algorithmmentioning

confidence: 99%

The role of semantics in mining frequent patterns from knowledge bases in description logics with rules

Józefowska¹,

Lawrynowicz²,

Łukaszewski³

2010

Preprint

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We propose a new method for mining frequent patterns in a language that combines both Semantic Web ontologies and rules. In particular we consider the setting of using a language that combines description logics with DL-safe rules. This setting is important for the practical application of data mining to the Semantic Web. We focus on the relation of the semantics of the representation formalism to the task of frequent pattern discovery, and for the core of our method, we propose an algorithm that exploits the semantics of the combined knowledge base. We have developed a proof-of-concept data mining implementation of this. Using this we have empirically shown that using the combined knowledge base to perform semantic tests can make data mining faster by pruning useless candidate patterns before their evaluation. We have also shown that the quality of the set of patterns produced may be improved: the patterns are more compact, and there are fewer patterns. We conclude that exploiting the semantics of a chosen representation formalism is key to the design and application of (onto-)relational frequent pattern discovery methods.

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On Reducing Redundancy in Mining Relational Association Rules from the Semantic Web

Cited by 12 publications

References 7 publications

Pattern Based Feature Construction in Semantic Data Mining

Pattern Based Feature Construction in Semantic Data Mining

The role of semantics in mining frequent patterns from knowledge bases in description logics with rules

The role of semantics in mining frequent patterns from knowledge bases in description logics with rules

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