②⊙a9日C一的⊙心 PROBLEM DEFINITION SYSTEM RECONDMLENDATION USER EVALLATION Related tetms te gene LAddroottem Root terms Figure 2. WebcoRe problem definition phase a discussion on a mailing list about the system of zation user for each of the terms to be developed in Delicious'and Flickr. It is associated to those ontologies. In our system, these information retrieval methodologies consisting of collaboratively assigned considering the depth measure o generated, open-ended labels that categorize content included in the Golden Standard Although they suffer from problems of imprecision and Let T be the set of all terms defined in the golden ambiguity, techniques employing free-form tagging encourage definition phase. Let d, be the depth measure information in their own w actively term t, E T. Let q be query vector extract nteract with the system Standard definition, and let w be the weight 3.2 Automatic Ontology recommendation these terms, where for each; E T, w E [0, 1. Then, the weight w, is calculated as. Once the user has selected the most appropriate set of terms to describe the problem domain, the tool performs the processes ontology retrieval and ranking. These processes play a key role within the system, since they provide the first level of information This measure gives more relevance to the terms explicitly to the user. To enhance the previous approaches of CORE, an expressed by the user, and less importance to those ones extended adaptation of traditional Information Retrieval techniques have or derived from previously selected terms. An interesting future popularity, or other more complex strategies as terms frequency retrieval techniques(21), where textual documents are replaced by analysis ontologies To carry out the process of ontology retrieval, the approach is 3.2.1 Query encoding and ontology retrieval focused on the lexical level, retrieving those ontologies that The queries supported by our model are expressed using the terms contain a subset of the terms expressed by the user during the selected during the Golden Standard definition phase Golden Standard definition. To compute the matching, two In classic keyword-based vector-space models for on difterent options are available within the tool: search for exact etrieval [21], each of the query keywords is assigned on matches and search for matches based on the Levenshtein distance that represents the importance of the keyword in the between two terms ed expressed by the query, or its discriminating In both cases, the query execution returns a set of ontologies that discerning relevant from irrelevant documents user requirements. Considering that not all the retrieved logously, in our model, the terms included in the Golden gies fulfil the same level of satisfaction, it is the system task dard can be weighted to indicate the relative interest of the them and present the ranked list to the user delicio.us-socialbookmarkinghttp://del.icio.us 6fliCkr-photosharinghttp://www.flickr.com/a discussion on a mailing list about the system of organization developed in Delicious5 and Flickr6 . It is associated to those information retrieval methodologies consisting of collaboratively generated, open-ended labels that categorize content. Although they suffer from problems of imprecision and ambiguity, techniques employing free-form tagging encourage users to organize information in their own ways and actively interact with the system. 3.2 Automatic Ontology Recommendation Once the user has selected the most appropriate set of terms to describe the problem domain, the tool performs the processes of ontology retrieval and ranking. These processes play a key role within the system, since they provide the first level of information to the user. To enhance the previous approaches of CORE, an adaptation of traditional Information Retrieval techniques have been integrated into the system. Our novel strategy to ontology retrieval can be seen as an evolution of classic keyword-based retrieval techniques [21], where textual documents are replaced by ontologies. 3.2.1 Query encoding and ontology retrieval The queries supported by our model are expressed using the terms selected during the Golden Standard definition phase. In classic keyword-based vector-space models for information retrieval [21], each of the query keywords is assigned a weight that represents the importance of the keyword in the information need expressed by the query, or its discriminating power for discerning relevant from irrelevant documents. Analogously, in our model, the terms included in the Golden Standard can be weighted to indicate the relative interest of the 5 del.icio.us - social bookmarking, http://del.icio.us/ 6 Flickr - photo sharing, http://www.flickr.com/ user for each of the terms to be explicitly mentioned in the ontologies. In our system, these weights are automatically assigned considering the depth measure of each of the terms included in the Golden Standard. Let T be the set of all terms defined in the Golden Standard definition phase. Let di be the depth measure associate with each term ti ∈ T. Let q be query vector extracted from the Golden Standard definition, and let wi be the weight associated to each of these terms, where for each ti ∈ T, wi ∈ [0,1]. Then, the weight wi is calculated as: 1 1 i i w d = + This measure gives more relevance to the terms explicitly expressed by the user, and less importance to those ones extended or derived from previously selected terms. An interesting future work could be to enhance and refine the query, e.g. based on terms popularity, or other more complex strategies as terms frequency analysis. To carry out the process of ontology retrieval, the approach is focused on the lexical level, retrieving those ontologies that contain a subset of the terms expressed by the user during the Golden Standard definition. To compute the matching, two different options are available within the tool: search for exact matches and search for matches based on the Levenshtein distance between two terms. In both cases, the query execution returns a set of ontologies that satisfy user requirements. Considering that not all the retrieved ontologies fulfil the same level of satisfaction, it is the system task to sort them and present the ranked list to the user. Figure 2. WebCORE problem definition phase