Towards integrating fuzzy logic capabilities into an ontology-based Inductive Logic Programming framework

Iglesias, Josué; Lehmann, Jens

doi:10.1109/isda.2011.6121843

Cited by 12 publications

(10 citation statements)

References 15 publications

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“…19 http://hcs.science.uva.nl/projects/NewKACTUS/library/lib/mereology.html. 20 http://www.cs.auc.dk/~csj/Glossary/. On the other hand, we say that the valid time of a fact is represented in relative terms when it is related to the valid time of another fact.…”

Section: Time Modelingmentioning

confidence: 99%

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Methodological guidelines for reusing general ontologies

Fernández-López

Gómez-Pérez

Suárez-Figueroa

2013

Data & Knowledge Engineering

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Currently, there is a great deal of well-founded explicit knowledge formalizing general notions, such as time concepts and the part_of relation. Yet, it is often the case that instead of reusing ontologies that implement such notions (the so-called general ontologies), engineers create procedural programs that implicitly implement this knowledge. They do not save time and code by reusing explicit knowledge, and devote effort to solve problems that other people have already adequately solved. Consequently, we have developed a methodology that helps engineers to: (a) identify the type of general ontology to be reused; (b) find out which axioms and definitions should be reused; (c) make a decision, using formal concept analysis, on what general ontology is going to be reused; and (d) adapt and integrate the selected general ontology in the domain ontology to be developed. To illustrate our approach we have employed use-cases. For each use case, we provide a set of heuristics with examples. Each of these heuristics has been tested in either OWL or Prolog. Our methodology has been applied to develop a pharmaceutical product ontology. Additionally, we have carried out a controlled experiment with graduated students doing a MCs in Artificial Intelligence. This experiment has yielded some interesting findings concerning what kind of features the future extensions of the methodology should have.

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Section: Time Modelingmentioning

confidence: 99%

“…Although this relation is already a primitive in languages like OWL [17], other possible interpretations to the one provided in OWL [18] are interesting. One example is the development of an ontology that requires either non-monotonic [19] or imprecise reasoning [20].…”

Section: Introductionmentioning

confidence: 99%

Methodological guidelines for reusing general ontologies

Fernández-López

Gómez-Pérez

Suárez-Figueroa

2013

Data & Knowledge Engineering

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“…Note that ALC and DL-Lite (the DL supported in Soft FACTS) are two incomparable DLs. Very recently, an extension of DL-Learner with some of the most up-to-date fuzzy ontology tools has been proposed [14]. Last, the work reported in [16] is based on an ad-hoc translation of fuzzy Łukasiewicz ALC DL constructs into LP and then uses a conventional ILP method to lean rules.…”

Section: Final Remarksmentioning

confidence: 99%

A Logic-based Computational Method for the Automated Induction of Fuzzy Ontology Axioms

Lisi

Straccia

2013

Fundamenta Informaticae

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Fuzzy Description Logics (DLs) are logics that allow to deal with structured vague knowledge. Although a relatively important amount of work has been carried out in the last years concerning the use of fuzzy DLs as ontology languages, the problem of automatically managing the evolution of fuzzy ontologies has received very little attention so far. We describe here a logic-based computational method for the automated induction of fuzzy ontology axioms which follows the machine learning approach of Inductive Logic Programming. The potential usefulness of the method is illustrated by means of an example taken from the tourism application domain.

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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%

“…[19,53] uses crisp terminological decision trees [5], while [25] is tailored to learn crisp ALC definitions. Very recently, an extension of DL-Learner with some of the most up-to-date fuzzy ontology tools has been proposed [26]. Notably, it can learn fuzzy OWL DL equivalence axioms from FuzzyOWL 2 ontologies 4 by interfacing the fuzzyDL reasoner [7].…”

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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Towards integrating fuzzy logic capabilities into an ontology-based Inductive Logic Programming framework

Cited by 12 publications

References 15 publications

Methodological guidelines for reusing general ontologies

Methodological guidelines for reusing general ontologies

A Logic-based Computational Method for the Automated Induction of Fuzzy Ontology Axioms

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