Learning Probabilistic Description Logic Concepts Under Alternative Assumptions on Incompleteness

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

doi:10.1007/978-3-319-13413-0_10

Cited by 2 publications

(2 citation statements)

References 26 publications

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“…Although an important amount of work has been carried about DLs, the application of machine learning techniques to OWL 2 data, is relatively marginally addressed [6,9,10,11,12,14,15,16,17,18,19,20,21,22,24,25,26,28,30,31,32,33,34,35,36,37,38,39,41,42,43,44,46,50,51,52,53,54]. We refer the reader to [50] for a recent overview.…”

Section: Introductionmentioning

confidence: 99%

pFOIL-DL

Straccia

Mucci

2015

Proceedings of the 30th Annual ACM Symposium on Applied Computing

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OWL ontologies are nowadays a quite popular way to describe structured knowledge in terms of classes, relations among classes and class instances.In this paper, given an OWL target class T , we address the problem of inducing EL(D) concept descriptions that describe sufficient conditions for being an individual instance of T . To do so, we use a Foil-based method with a probabilistic candidate ensemble estimation. We illustrate its effectiveness by means of an experimentation.

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Section: Introductionmentioning

confidence: 99%

pFOIL-DL

Straccia

Mucci

2015

Proceedings of the 30th Annual ACM Symposium on Applied Computing

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“…However, none of the KBs has a satisfactory coverage of knowledge and relation inference is an indispensable task to extend the coverage. In fact, relation inference has been paid much attention due to the successful applications in many areas such as semantic search, machine question and answering, description of logic concepts (Minervini and d'Amato 2012), and learning on the Semantic Web (Huang and Tresp, 2011) etc. There are a lot of related techniques, such as, FOIL (Quinlan and Cameron 1993), PRA (Lao and Subramanya 2012), etc.…”

Section: Introductionmentioning

confidence: 99%

Content-Structural Relation Inference in Knowledge Base

Zeya

Jia

Wang

et al. 2014

AAAI

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Relation inference between concepts in knowledge base has been extensively studied in recent years. Previous methods mostly apply the relations in the knowledge base, without fully utilizing the contents, i.e., the attributes of concepts in knowledge base. In this paper, we propose a content-structural relation inference method (CSRI) which integrates the content and structural information between concepts for relation inference. Experiments on data sets show that CSRI obtains 15% improvement compared with the state-of-the-art methods.

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Learning Probabilistic Description Logic Concepts Under Alternative Assumptions on Incompleteness

Cited by 2 publications

References 26 publications

pFOIL-DL

pFOIL-DL

Content-Structural Relation Inference in Knowledge Base

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