Similarity measures for FL&lt;sub&gt;0&lt;/sub&gt; concept descriptions from an automata-theoretic point of view

Racharak, Teeradaj; Suntisrivaraporn, Boontawee

doi:10.1109/ictemsys.2015.7110811

Cited by 9 publications

(5 citation statements)

References 12 publications

(24 reference statements)

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“…Concerning the choice of representation language, it is an obvious future work to explore nonapproximate similarity measure for ALC by investi-gating under scrutiny into the original tableau algorithm. Another direction for future work could be to compare the measure presented in this paper to those two notions of similarity for F L 0 introduced in (Racharak and Suntisrivaraporn, 2015). Since F L 0 is a sub-logic of ALEH and as such sim ALEH is applicable also to F L 0 , it is interesting to explore whether sim ALEH is stronger (see (Racharak and Suntisrivaraporn, 2015)) than the skeptical and credulous similarity measures.…”

Section: Discussion and Future Workmentioning

confidence: 99%

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A Structural Subsumption based Similarity Measure for the Description Logic ALEH

Suntisrivaraporn

Tongphu

2016

Proceedings of the 8th International Conference on Agents and Artificial Intelligence

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Description Logics (DLs) are a family of logic-based knowledge representation formalisms, which can be used to develop ontologies in a formally well-founded way. The standard reasoning service of subsumption has proved indispensable in ontology design and maintenance. This checks, relative to the logical definitions in the ontology, whether one concept is more general/specific than another. When no subsumption relationship is identified, however, no information about the two concepts can be given. This work extends from an existing work on similarity measure in ELH to the more expressive description logic ALEH. We introduce generalizations of the notion of normalization and homomorphism in ALEH which are then employed at the heart of our semantic similarity measure. The proposed similarity measure computes a numerical degree of similarity between two ALEH concept descriptions despite not being in the subsumption relation. 204 Suntisrivaraporn, B. and Tongphu, S. A Structural Subsumption based Similarity Measure for the Description Logic ALEH .

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Section: Discussion and Future Workmentioning

confidence: 99%

“…Racharak and Suntisrivaraporn suggested two new notions of similarity for the DL F L 0 (Racharak and Suntisrivaraporn, 2015). Both the skeptical and credulous similarity measures are derived from the known structural characterization subsumption through inclusion of regular languages.…”

Section: Related Workmentioning

confidence: 99%

A Structural Subsumption based Similarity Measure for the Description Logic ALEH

Suntisrivaraporn

Tongphu

2016

Proceedings of the 8th International Conference on Agents and Artificial Intelligence

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“…In sim π , the structural subsumption characterization by means of tree homomorphism was adopted to indicate a degree of similarity under the agent's preferences. To account for the concept similarity under the agent's preferences for the DL FL 0 , Racharak and Tojo [5] formulated two concrete measures based on the the structural subsumption characterization by means of language inclusion [26]. On the same logic FL 0 , Baader et al [27] developed a family of concept similarity measures based on the weighted tree automata and showed how to re-construct the approaches developed in [26].…”

Section: A On the Viewpoint Of Ontology Similaritymentioning

confidence: 99%

On Approximation of Concept Similarity Measure in Description Logic ELH With Pre-Trained Word Embedding

Racharak

2021

IEEE Access

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Data-driven and knowledge-driven methods are two mainstream techniques in the pursuit of developing artificial intelligence systems. While data-driven methods seek to develop a decision model from observations in the real world, they are difficult to provide an explanation for the results in human terms. On the other hand, knowledge-driven methods that employ symbolic reasoning based on formal semantics of a knowledge-base are thus more interpretable and explainable, while lacking an ability to deal with incomplete modeling of the structured knowledge-bases. This work aims to tackle these issues on ontology similarity by proposing a general framework that combines the strengths of both approaches for measuring semantic similarity of concepts in a description logic (DL) ontology. More specifically, a neuro-symbolic integrated framework is defined to exploit the pre-trained word embeddings with semantic definitions in an ontology to yield an explainable degree of concept similarity. To demonstrate its applicability, we develop a concrete similarity measure sim conforming to the proposed framework and also introduce an efficient algorithm that can extract an explanation for why such a degree is indicated. The correctness is shown by analyzing theoretical properties that it guarantees to preserve and also by performing an empirical evaluation with a medical ontology SNOMED CT and a medical pre-trained embedding BioWordVec. The results show that our proposed method remains both interpretability and explainability while achieving comparable performance, relative to the state-of-the-art approaches in the data and knowledge-driven methods.

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“…SNOMED CT [13] and gene ontology). Recently, a similarity measures for a less-expressive DL FL 0 was proposed by Racharak and Suntisrivaraporn [14]. Lehmann and Turhan [15] extended the work of Jaccard to support more constructors.…”

Section: Introductionmentioning

confidence: 99%

Toward semantic similarity measure between concepts in an ontology

Tongphu

Suntisrivaraporn

Aimmanee

2019

IJEECS

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<p>A similarity measure is one classical problem in Description Logic which aims at identifying the similarity between concepts in an ontology. Finding a hierarchy distance among concepts in an ontology is one popular technique. However, one major drawback of such a technique is that it usually ignores a concept definition analysis. This work introduces a new method for similarity measure. The proposed system semantically analyzes structures of two concept descriptions and then computes the similarity score based on the number of shared features. The efficiency of the proposed algorithm is measured by means of the satisfaction of desirable properties and intensive experiments on the Snomed ct ontology.</p>

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Similarity measures for FL<sub>0</sub> concept descriptions from an automata-theoretic point of view

Cited by 9 publications

References 12 publications

A Structural Subsumption based Similarity Measure for the Description Logic ALEH

A Structural Subsumption based Similarity Measure for the Description Logic ALEH

On Approximation of Concept Similarity Measure in Description Logic ELH With Pre-Trained Word Embedding

Toward semantic similarity measure between concepts in an ontology

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