0a9日日“m?GE I ORLIDFITTION SYSTEM RFCOMDBENDATION USER PVALLATION Ontology evalation regia 1291698334 amdaleey overview NucleicAcid Lexical ONT Leveal KB C LnSiHybrdrate 0nn345 vencelas Figure 3. WebCORE system recommendation phase 3.2.2 Ontology ranking Hence, the similarity measure between an ontology o and the Once the list of ontologies is formed, the ontology-search engine query q is simply compute as follows similarity value between the query and each sm(q,o,)=0 ntology We represent each ontology in the search vector 0, eO, where O is the mean of the 3. 2. 3 Combination with knowledge base retrieval erm t; similarities with all the matched entities in the ontology if any matching exists, and zero otherwise ranking algorithm performs very poorly. Queries will return less The components oj are calculated as esults than expected, the relevant ontologies will not be retrieved or will get a much lower similarity value than it should. For Instance, if there are ontologies about“ restaurants";and“ dishes” v(m2) (KB), a user searching for ontologies in this domain may be s are expressed as instances in the corresponding Knowledge Ba interested in the instances and literals contained in the Kb. to where My is the set of matches of the term 4; in the ontology cope with this issue, our ranking model combines the similarity oi,w(m/ represents the similarities between the term t; and the obtained from the terms that belong to the ontology with the entities of the ontology o; that matches with it, M, is the set of similarity obtained from the terms that belong to the Kb using the matches of the term I, within all the ontologies and w(m adaptation of the vector space model explained before weights of each of On the other hand, the combination of outputs of several search For example, if we define in the Golden Standard a term"acid", engines has been a widely addressed research topic in the this term may return several matches in the same ontology with Information Retrieval field [9]. After testing several approaches difterent entities as: "acid,"amino acid, etc. In order to we have selected the so-called Comb-MNZ strategy. This establish the appropriate weight in the ontology vector, Oi, the goal is to compute the number of matches of one term in the technique has been shown in prior works as one of the simplest nd most effective rank aggregation techniques, and consists of whole repository of ontologies and give more relevance to those computing a combined ranking score by a linear combination of ontologies that have matched that specific term more times the input scores with additional factors that measure the relevance Due to the way in which the vector o; is constructed, each of each score in the final ranking. In our case, the relevancies of between n ent Oy contains specific information about the similarity the scores. i.e. the relevancies of the similarity computation the ontology and the corresponding term f. To compute within the ontology and within the knowledge base, are given by the final similarity between the query vector q and the ontology the user. He can select a value v, E [1, 5] for each kind of search o.the vectorial model calculates the cosine measure and this value is then mapped to a corresponding value s; using both vectors. However. if we follow the traditiona the following normalization. vectorial model, we will only be considering the difference between the query and the ontology vectors according to the angle hey form, but not taking into account their dimensions. Thus, to overcome this limitation, the above cosine measure used in the Following this idea, the final score is computed as vectorial model has been replaced by the simple dot product.3.2.2 Ontology ranking Once the list of ontologies is formed, the ontology-search engine computes a semantic similarity value between the query and each ontology as follows. We represent each ontology in the search space as an ontology vector oj ∈ O, where oji is the mean of the term ti similarities with all the matched entities in the ontology if any matching exists, and zero otherwise. The components oji are calculated as: ( ) ( ) ji i ji M ji ji i M w m o M w m = ∑ ∑ where Mji is the set of matches of the term ti in the ontology oj, w(mji) represents the similarities between the term ti and the entities of the ontology oj that matches with it, Mi is the set of matches of the term ti within all the ontologies and w(mi) represents the weights of each of these matches. For example, if we define in the Golden Standard a term “acid”, this term may return several matches in the same ontology with different entities as: “acid”, “amino acid”, etc. In order to establish the appropriate weight in the ontology vector, oij, the goal is to compute the number of matches of one term in the whole repository of ontologies and give more relevance to those ontologies that have matched that specific term more times. Due to the way in which the vector oj is constructed, each component oij contains specific information about the similarity between the ontology and the corresponding term ti. To compute the final similarity between the query vector q and the ontology vector oj, the vectorial model calculates the cosine measure between both vectors. However, if we follow the traditional vectorial model, we will only be considering the difference between the query and the ontology vectors according to the angle they form, but not taking into account their dimensions. Thus, to overcome this limitation, the above cosine measure used in the vectorial model has been replaced by the simple dot product. Hence, the similarity measure between an ontology oj and the query q is simply compute as follows: j j sim(q,o ) =q ⋅o 3.2.3 Combination with Knowledge Base Retrieval If the knowledge in the ontology is incomplete, the ontology ranking algorithm performs very poorly. Queries will return less results than expected, the relevant ontologies will not be retrieved, or will get a much lower similarity value than it should. For instance, if there are ontologies about “restaurants”, and “dishes” are expressed as instances in the corresponding Knowledge Base (KB), a user searching for ontologies in this domain may be also interested in the instances and literals contained in the KB. To cope with this issue, our ranking model combines the similarity obtained from the terms that belong to the ontology with the similarity obtained from the terms that belong to the KB using the adaptation of the vector space model explained before. On the other hand, the combination of outputs of several search engines has been a widely addressed research topic in the Information Retrieval field [9]. After testing several approaches, we have selected the so-called Comb-MNZ strategy. This technique has been shown in prior works as one of the simplest and most effective rank aggregation techniques, and consists of computing a combined ranking score by a linear combination of the input scores with additional factors that measure the relevance of each score in the final ranking. In our case, the relevancies of the scores, i.e., the relevancies of the similarity computation within the ontology and within the knowledge base, are given by the user. He can select a value vi ∈ [1, 5] for each kind of search, and this value is then mapped to a corresponding value si using the following normalization. 5 i i v s = Following this idea, the final score is computed as: s sim(q,o) s sim(q,kb) O × + kb × Figure 3. WebCORE system recommendation phase