PROHLFM DEFNITION SISTYM RECOMMFNDATION USER FVALUATION Uxes evalation ef 129169833 ontology Nuclei Arid InSirmHybaadnarien Gement Linkage Map FxessedseeueneeT吨 中y5中印 Figure 4. WebCoRE user evaluation ph For future work, we are considering to set s, using statistical focused ric types of tasks or activities) and information about the knowledge contained in the ontologies the gies (for ontologies describing a domain knowledge contained in the KBs and the information requested by dependent manner). the user during the golden Standard definition phase The above criteria can have discrete numeric or non-numeric Figure 3 shows the system recommendation interface. At the left alues. The users interests are expressed like a subset of these side the user can select the matching methodology (fuzzy or criteria, and their respective values, meaning thresholds or exact), the search spaces (ontology entities and knowledge base restrictions to be satisfied by user evaluations. Thus, a numeric entities), and the weight or importance given to each of the riterion will be satisfied if an evaluation value is equal or great previously selected search spaces. In the right part the user can than that expressed by its interest threshold, while a non-numeric isualize the ontology and navigate across it. Finally, the middle riterion will be satisfied only when the evaluation is exactly the of the interface presents the list of ontologies selected for the user given threshold (i.e. in a boolean or yes/no manner). to be evaluated during the collaborative evaluation phase According to both types of user evaluation and interest criteria, 3.3 Collaborative Ontology Evaluation numeric and Boolean, the recommendation algorithm wi The third and last phase of the system is compound easure the degree in which each user restriction is satisfied by ontology recommendation algorithm that exploits th the evaluations, and will recommend a ranked ontology list of Collaborative Filtering [1], exploring the manual according to similarity measures between the thresholds and the stored in the system to rank the set of ontologies that best fulfils collaborative evaluations. To create the final ranked ontology list the user's interests the recommender module follows two phases. In the first one it calculates the similarity degrees between all the user evaluations In WebCORE, user evaluations are represented as a set of five different criteria [15 and their respective values, manually and the specified user interest criteria thresholds. In the second determined by the users who made the evaluations one it combines the similarity measures of the evaluations generating the overall rankings of the ontologies. Correctness: specifies whether the information stored in the Figure 4 shows all the previous definitions and ideas, locating ntology is true, independently of the domain of interest. them in the graphical interface of the system. On the left side of Readability: indicates the non-ambiguous interpretation of the screen, the user introduces the thresholds for the the meaning of the concept names. recommendations and obtains the final collaborative ontology Flexibility: points out the adaptability or capability of the ontologies and checks evaluations given by the rest of the usere ranking. On the right side, the user adds new evaluations for 3.3. Collaborative Evaluation Measures Level of formality: highly informal, semi-informal, sem As mentioned before, a user evaluates an ontology considering formal, rigorously-formal five different criteria that can be divided different groups Type of model: upper-level(for ontologies describir eneral, domain-independent concepts ), core-ontologies(for which take discrete numeric values [1, 2, 3, 4, 5], where 1 the ontology does not fulfil the criterion, and 5 means the logies that contain the most important concepts on a ontology completely satisfies the criterion b) boolean broadly describe a domain), task-ontologies(for ontologies criteria (level of formality' and type ofFor future work, we are considering to set si using statistical information about the knowledge contained in the ontologies, the knowledge contained in the KBs and the information requested by the user during the Golden Standard definition phase. Figure 3 shows the system recommendation interface. At the left side the user can select the matching methodology (fuzzy or exact), the search spaces (ontology entities and knowledge base entities), and the weight or importance given to each of the previously selected search spaces. In the right part the user can visualize the ontology and navigate across it. Finally, the middle of the interface presents the list of ontologies selected for the user to be evaluated during the collaborative evaluation phase. 3.3 Collaborative Ontology Evaluation The third and last phase of the system is compound of a novel ontology recommendation algorithm that exploits the advantages of Collaborative Filtering [1], exploring the manual evaluations stored in the system to rank the set of ontologies that best fulfils the user’s interests. In WebCORE, user evaluations are represented as a set of five different criteria [15] and their respective values, manually determined by the users who made the evaluations. • Correctness: specifies whether the information stored in the ontology is true, independently of the domain of interest. • Readability: indicates the non-ambiguous interpretation of the meaning of the concept names. • Flexibility: points out the adaptability or capability of the ontology to change. • Level of formality: highly informal, semi-informal, semi￾formal, rigorously-formal. • Type of model: upper-level (for ontologies describing general, domain-independent concepts), core-ontologies (for ontologies that contain the most important concepts on a specific domain), domain-ontologies (for ontologies that broadly describe a domain), task-ontologies (for ontologies focused on generic types of tasks or activities) and application-ontologies (for ontologies describing a domain in an application-dependent manner). The above criteria can have discrete numeric or non-numeric values. The user’s interests are expressed like a subset of these criteria, and their respective values, meaning thresholds or restrictions to be satisfied by user evaluations. Thus, a numeric criterion will be satisfied if an evaluation value is equal or greater than that expressed by its interest threshold, while a non-numeric criterion will be satisfied only when the evaluation is exactly the given threshold (i.e. in a Boolean or yes/no manner). According to both types of user evaluation and interest criteria, numeric and Boolean, the recommendation algorithm will measure the degree in which each user restriction is satisfied by the evaluations, and will recommend a ranked ontology list according to similarity measures between the thresholds and the collaborative evaluations. To create the final ranked ontology list the recommender module follows two phases. In the first one it calculates the similarity degrees between all the user evaluations and the specified user interest criteria thresholds. In the second one it combines the similarity measures of the evaluations, generating the overall rankings of the ontologies. Figure 4 shows all the previous definitions and ideas, locating them in the graphical interface of the system. On the left side of the screen, the user introduces the thresholds for the recommendations and obtains the final collaborative ontology ranking. On the right side, the user adds new evaluations for the ontologies and checks evaluations given by the rest of the users. 3.3.1 Collaborative Evaluation Measures As mentioned before, a user evaluates an ontology considering five different criteria that can be divided in two different groups: a) numeric criteria (‘correctness’, ‘readability’ and ‘flexibility’), which take discrete numeric values [1, 2, 3, 4, 5], where 1 means the ontology does not fulfil the criterion, and 5 means the ontology completely satisfies the criterion, and, b) Boolean criteria (‘level of formality’ and ‘type of model’), which are Figure 4. WebCORE user evaluation phase