Identifying an Agent’s Preferences Toward Similarity Measures in Description Logics

Racharak, Teeradaj; Suntisrivaraporn, Boontawee; Tojo, Satoshi

doi:10.1007/978-3-319-31676-5_14

Cited by 5 publications

(6 citation statements)

References 7 publications

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“…We first introduced preference profile (denoted by π) in (Racharak et al, 2016a) as a collection of pref-erential elements in which the development of concept similarity measure should consider. Its first intuition is to model different forms of preferences (of an agent) based on concept names and role names.…”

Section: Preference Profilementioning

confidence: 99%

“…Definition 2.1 (Preference Profile (Racharak et al, 2016a)). Let CN pri (T ), RN pri (T ), and RN(T ) be a set of primitive concept names occurring in T , a set of primitive role names occurring in T , and a set of role names occurring in T , respectively.…”

Section: Preference Profilementioning

confidence: 99%

“…Lastly, for any r ∈ RN(T ), d(r) = 1 captures an expression of total importance on a role (over a corresponding nested concept) and d(r) = 0 captures an expression of total importance on a nested concept (over a corresponding role). (Racharak et al, 2016a)). The default preference profile, in symbol π 0 , is the quintuple…”

Section: Preference Profilementioning

confidence: 99%

“…The primary motivation of this aspect is to capture an expression of total expression on a role beyond a corresponding nested concept (Racharak et al, 2016a).…”

Section: Tuning Dmentioning

confidence: 99%

“…an agent's preferences. A set of preferential aspects is identified in (Racharak et al, 2016a) called preference profile and is extended toward (Racharak et al, 2016b) called sim π . However, realistic ontologies are complex -consisting in plenty of concept names and role names.…”

Section: Introductionmentioning

confidence: 99%

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Tuning Agent's Profile for Similarity Measure in Description Logic ELH

Racharak

Tojo

2017

Proceedings of the 9th International Conference on Agents and Artificial Intelligence

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In Description Logics (DLs), concept similarity measure aims at identifying a degree of commonality of two given concepts and is often regarded as a generalization of the classical reasoning problem of equivalence. That is, any two concepts are equivalent if and only if their similarity degree is one. When two concepts are not equivalent, the level of similarity varies depending not only on the objective factors (e.g. the structure of concept descriptions) but also on the subjective factors (i.e. the agent's preferences). Realistic ontologies are generally complex. Methodologies for tuning a measure to conform with the agent's preferences should be practical, i.e. it is doable in practice. In this work, we investigate and formalize the task of tuning the preference functions based on the information defined in a TBox and an ABox. We also show how the proposed approaches can be reconciled with the measure sim π , i.e. a concept similarity measure under preference profile for DL ELH. Finally, the paper relates the approach to others and discusses future direction.

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Section: Preference Profilementioning

confidence: 99%

Section: Preference Profilementioning

confidence: 99%

Section: Preference Profilementioning

confidence: 99%

“…The primary motivation of this aspect is to capture an expression of total expression on a role beyond a corresponding nested concept (Racharak et al, 2016a).…”

Section: Tuning Dmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations