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IADIS International Conference WWW/Internet 2010This ConceptNet intends to reflect a simplistic, usable structure related to human cognitive structure. Itcan support people, through the information encoded, to plan, to execute and to complete an activity. For ourpurposes, it is not important whether the “facts” are true or not that are encoded in the database; in fact, theywill likely be inconsistent as it is the perceptions and beliefs which is what we are after.2. GENTNER’S THEORYGentner worked with Natural Language Processing (NLP) to identify methods to discover literal similarity,analogies, metaphors and abstraction (Gentner, 1983). The base that she works is different from OMCS-Brbase which uses Minsky’s relations to connect and store concepts. The main challenge that we consider is tounderstand Gentner’s theory and map it to be applied in order to expand the semantic network of the OMCS-Br project to improve analogies search.Analogy is a comparison between source domain and target domain relations, i.e. edges (relations)(Gentner 1983). For example, it is shown in Figure 3 one example that illustrates an analogy between Earthand Electron because there is the same relation (revolve) in both independently of the others concepts. Inother words, Earth and Electron revolve around the sun and the nucleus, respectively.Figure 3. Two analogy concepts3. MINSKY AND GENTNERMinsky and Gentner use different relations to map the knowledge. Gentner uses actions and properties tocompose relations. Therefore, the number of relations are bigger and more specific than Minsky’s one, whichcontain approximately 20 relation (Liu and Singh, 2004), augmenting the concept semantics.In next section, we present the first experiment using Gentner theory applied in OMCS-Br base.3.1 First ExperimentWe developed a prototype to investigate analogies in the OMCS-Br semantic network. We choose randomlya concept that we want to look for analogies, in this case the concept “banana”. Through this concept theprototype generated a specific semantic network related to the concept “banana” as shown in Table 1:Table 1. Specific semantic network of the concept bananaFormal SpecificationRelation(Concept1, Concept2)LocationOf(banana, fruit bowl) R1 (X, Y1)UsedFor(banana, eat) R2 (X, Y2)IsA(banana, fruit) R3 (X, Y3)PropertyOf(banana, yellow) R4 (X, Y4)MadeOf(ice cream, banana) R5 (Y5, X)MadeOf(cake, banana) R6 (Y6, X)Table 1 shows just the first six results as example. With the result of Table 1, the prototype search fordifferent concepts that match just each relation R in the whole semantic network of the OMCS-Br project.For each relation, in Table 1, we will show three matches in Table 2:With these matches we can define a score which represents the times that a concept is linked through thesame relation (R). For example, the score between “banana” and “legume” is 1, because they have therelation LocationOf in common. Table 2 shows the concepts with higher score based in the results of Table 1.37