Metric-based stochastic conceptual clustering for ontologies

Fanizzi, Nicola; d’Amato, Claudia; Esposito, Floriana

doi:10.1016/j.is.2009.03.008

Cited by 27 publications

(15 citation statements)

References 30 publications

(60 reference statements)

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“…For instance, Genetic Programming has been successfully applied in Information Retrieval to reveal the most appropriate document ranking functions for search engines [2,4,13,23]. In the Linked Data context, Genetic Pro-gramming was used to identify similarity functions for discovering links [16,17], instance clustering [6] or matching [11] across different datasets, but not, prior to the work presented here, to assess relationship strength between Linked Data entities.…”

Section: Related Workmentioning

confidence: 99%

Learning to Assess Linked Data Relationships Using Genetic Programming

Tiddi

d’Aquin

Motta

2016

Lecture Notes in Computer Science

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Abstract. The goal of this work is to learn a measure supporting the detection of strong relationships between Linked Data entities. Such relationships can be represented as paths of entities and properties, and can be obtained through a blind graph search process traversing Linked Data. The challenge here is therefore the design of a cost-function that is able to detect the strongest relationship between two given entities, by objectively assessing the value of a given path. To achieve this, we use a Genetic Programming approach in a supervised learning method to generate path evaluation functions that compare well with human evaluations. We show how such a cost-function can be generated only using basic topological features of the nodes of the paths as they are being traversed (i.e. without knowledge of the whole graph), and how it can be improved through introducing a very small amount of knowledge about the vocabularies of the properties that connect nodes in the graph.

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Section: Related Workmentioning

confidence: 99%

Learning to Assess Linked Data Relationships Using Genetic Programming

Tiddi

d’Aquin

Motta

2016

Lecture Notes in Computer Science

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“…Some works have focused on improving the quality of data by grouping resources to detect concepts and induce new classes or refine existing one [4,5].…”

Section: Related Workmentioning

confidence: 99%

Theme Identification in RDF Graphs

Ouksili

Kedad

Lopes

2014

Model and Data Engineering

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Abstract. An increasing number of RDF datasets is published on the Web. A user willing to use these datasets will first have to explore them in order to determine which information is relevant for his specific needs. To facilitate this exploration, we present an approach allowing to provide a thematic view of a given RDF dataset, making it easier to target the relevant resources and properties. Our approach combines a density-based graph clustering algorithm with semantic criteria in order to identify clusters, each one corresponding to a theme. Prior to clustering, the initial RDF graph is simplified, and user preferences are mapped into a set of transformations applied to the graph. Once the clusters are identified, labels are extracted to express their semantics. In this paper, we describe the main features of our approach to generate a set of themes from an RDF dataset.

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“…The general form of the family of dissimilarity measures for individuals inspired by the Minkowski's distances (L p ) can be defined as follows [9,12]: 1], is defined as follows:…”

Section: Metrics For Dl: Comparing Individuals Within Ontologiesmentioning

confidence: 99%

“…Although some structural dissimilarity measures have been proposed for some specific DLs of fair expressiveness [5], they are still partly based on structural criteria which make them fail to fully grasp the underlying semantics and hardly scale to more complex DL languages such as those backing the OWL ontology language 1 . Therefore, we have devised a family of semi-distance measures for semantically annotated resources, which can overcome the aforementioned limitations [9,12]. Such measures are merely based on the the criterion of semantic discernibility of the input individuals with respect to a fixed reference context [14] represented by a set of concept definitions.…”

Section: Introduction: Clustering In Complex Categorical Spacesmentioning

confidence: 99%

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Fuzzy Clustering for Semantic Knowledge Bases

Esposito

d’Amato

Fanizzi

2010

Fundamenta Informaticae

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This work focusses on the problem of clustering resources contained in knowledge bases represented through multi-relational standard languages that are typical for the context of the Semantic Web, and ultimately founded in Description Logics. The proposed solution relies on effective and language-independent dissimilarity measures that are based on a finite number of dimensions corresponding to a committee of discriminating features, that stands for a context, represented by concept descriptions in Description Logics. The proposed clustering algorithm expresses the possible clusterings in tuples of central elements: in this categorical setting, we resort to the notion of medoid, w.r.t. the given metric. These centers are iteratively adjusted following the rationale of fuzzy clustering approach, i.e. one where the membership to each cluster is not deterministic but graded, ranging in the unit interval. This better copes with the inherent uncertainty of the knowledge bases expressed in Description Logics which adopt an open-world semantics. An extensive experimentation with a number of ontologies proves the feasibility of our method and its effectiveness in terms of major clustering validity indices.

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Metric-based stochastic conceptual clustering for ontologies

Cited by 27 publications

References 30 publications

Learning to Assess Linked Data Relationships Using Genetic Programming

Learning to Assess Linked Data Relationships Using Genetic Programming

Theme Identification in RDF Graphs

Fuzzy Clustering for Semantic Knowledge Bases

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