In Description Logics (DLs), concept similarity measures (CSMs) aim at identifying a degree of commonality between two given concepts and are often regarded as a generalization of the classical reasoning problem of equivalence. That is, any two concepts are equivalent if their similarity degree is one, and vice versa. When two concepts are not equivalent, the level of similarity varies depending not only on the objective factors (i.e. the concept descriptions) but also on the subjective factors (i.e. the agent's preferences). This work presents the notion of a general preference profile to be used in existing similarity measures and exemplifies its applicability with the similarity measure for the DL ELH , called sim π . We show that our measure is expressible for all aspects of preference profile and prove that sim π is preference-invariant w.r.t. equivalence, i.e. similarity between two equivalent concepts is always one regardless of agents' preferences. Agents' preferences are used in a variety of related, but not identical, ways in their daily life: to express what they like and dislike, to express their desired goals when choosing routes for travelling (Son et al., 2003) , etc. In psychology, preferences may be conceived of as an agent's attitude towards a set of objects when making decisions (Lichtenstein and Slovic, 2006) . Alternatively, preferences can be interpreted as a judgment in a sense of liking or disliking an object (Scherer, 2005) . In Description Logics (DLs), concept similarity measures (CSMs) aim at identifying a degree of commonality between two given concept names and are often regarded as a generalization of the classical reasoning problem of equivalence. That is, any two concepts are equivalent if their similarity degree is one, and vice versa. To date, many semantic CSMs have been developed (cf. Section 4). These developments can induce efficient similarity-oriented DL reasoning services, i.e., to measure if two concepts are similar, to check if a given instance is a relaxed instance of a concept, and to retrieve those instances similar to a given instance. However, relatively limited efforts have been placed on addressing real-world similarity services executed by a user agent, i.e., finding similarity w.r.t. the needs and preferences of an agent. These issues can be illustrated with the following example: Example 1.1. Suppose that Bob, a Ph.D student, wants to visit a place for active activities, and he feels like a place where he can enjoy walking. According to his world, a terminology might have been modeled in DL as follows: Considering merely the objective aspects of the world, it is reasonable to conclude that both Mangrove and Beach are equally similar to the notion of ActivePlace. Taking into account also Bob's preferences, however, Mangrove appears more suitable to his perception of ActivePlace. The example shows that preferences of an agent play a decisive role in the choice of alternatives. Thus, we need to be able to fine-tune the degree of similarity by employing aspects apart from the objective factors (i.e. the concept descriptions themselves). It is worth observing that, with a few exceptions like 480 Racharak, T., Suntisrivaraporn, B. and Tojo, S.
To help such investigation, a general notion called concept similarity measure under preference profile was introduced in REF with the developed measure sim π for the DL ELH .
simπ: A Concept Similarity Measure under an Agent’s Preferences in Description Logic ELH
ICAART
Computer Science