As it has been shown before. current aptation. Techniques are needed to adapt the user take advantage of ontology evaluation, to new interests and forget old ones as user interests and ranking methodologies, All thes evolve with time. Again, in our approach profile adaptation advantages to the process of ontology evaluation and reuse, but is done manually(manual update of ontology evaluations) they do not exploit others related to the well known Recommender Systems [1]; is it helpful to know other users Filtering method. Items or actions are recom d to a user ions to evaluate and select the most suitable ontology? taking into account the available informatio em content descriptions and user profiles). There are three The collaboration between users has been addressed in the area of filtering approaches for making recommendation ontology design and construction [23]. In [14], the necessity of mechanisms for ontology maintenance is presented under Demographic filtering: Descriptions of people (e.g. age, scenarios like ontology-development in collaborative gender, etc)are used to learn the relationship between a environments. Moreover. works 7, present tools and single item and the type of people who like it. common shared ontologies by geographically distributed groups ontent-based filtering The user is recommended items based on the descriptions of items previously evaluated by However, despite all these common scenarios where the users other users. Content-based filtering is chosen approach in collaboration is required for ontology design and construction, the our work(the system recommends ontologies using previous use of collaborative tools for ontology evaluation is still a novel evaluations of those ontologies) and incipient approach in the literature [8] Collaborative filtering: People with similar interests are 2.2 Recommender systems matched and then recommendations are made Collaborative filtering strategies make automatic pr (filter)about the interests of a user by collecting taste information Matching method. It defines how user interests and item rom many users(collaborating). This approach usually consists of two steps: a)look for users that have a similar rating pattern to identified that of the active user(the user for whom the prediction is done), User profile matching: people with similar interests are and b)use the ratings of users found in the previous step to matched before making recommendations ompute the predictions for the active user. These predictions are specific to the user, differently to those given by more simple User profile-item matching: a direct comparison is made approaches that provide average scores for each item of interest, between the user profile and the items. The degree of for example based on its number of votes ppropriateness of the ontologies is computed by taking into Collaborative is a widely explored field. Three main ccount previous evaluations of those ontologies pects typical guish the different techniques reported in In WebCORE, a new ontology evaluation measure based on the literature [1 profile representation and management, collaborative filtering is proposed, considering users'interests and filtering method, and matching method. previous assessments of the ontologies. User profile representation and management can be divided into five different tasks 3. SYSTEM ARCHITECTURE mentioned before, WebCORE is a web ap plication for Profile representation. Accurate profiles are vital for the Collaborative Ontology Reuse and Evaluation. a user logins into ind. thanks to AjAX technol appropriate)and the collaborative component(to ensure that and the Google Web Toolkit, dynamically describes a probler users with similar profiles are in fact similar). The type of domain, searches for ontologies related to this domain,obtains profile chosen in this work is the user-item ratings matrix relevant ontologies ranked by several lexical, taxonomic and (ontology evaluations based on specific criteria) collaborative criteria, and optionally evaluates by himself those Initial profile generation. The user is not usually willing to spend too much time in defining her/his interests to create a In this section, we describe the server-side architecture of personal profile. Moreover, user interests may change WebCoRE. Figure 1 shows an overview of the system. We e. The type of initial profile generatio distinguish three different modules. The first one. the left module chosen in this work is a manual selection of values for only receives the problem description( Golden Standard) as a full text five specific evaluation criteria or as a set of initial terms. In the first case. the system uses a NLP Profile learning. User profiles can be learned or updated module to obtain the most relevant terms of the given text. The initial set of terms can also be modified and extended by the user using different sources of information that are potentially using WordNet [12]. The second one, represented in the centre of representative of user interests. In our work, profile learning the figure allows the user to select a set of ontology evaluation techniques are not used. techniques provided by the system to recover the ontologies The source of user input and feedback to infer user interes closest to the given Golden Standard. Finally, the third one, on the from information used to update user profiles. It can be right of the figure, is a collaborative module that re-ranks the list obtained in two different ways: using information explicitly of recovered ontologies, taking into consideration previous provided by the user, and using information implicit feedback and evaluations of the users bserved in the users interaction. Our system uses no feedback to update the user profiles 3 Google Web toolkit. htAs it has been shown before, current ontology reuse approaches take advantage of ontology evaluation, search, retrieval, selection and ranking methodologies. All these areas provide different advantages to the process of ontology evaluation and reuse, but they do not exploit others related to the well known Recommender Systems [1]; is it helpful to know other users’ opinions to evaluate and select the most suitable ontology? The collaboration between users has been addressed in the area of ontology design and construction [23]. In [14], the necessity of mechanisms for ontology maintenance is presented under scenarios like “ontology-development in collaborative environments”. Moreover, works as [7], present tools and services to support the process of achieving consensus on common shared ontologies by geographically distributed groups. However, despite all these common scenarios where the user’s collaboration is required for ontology design and construction, the use of collaborative tools for ontology evaluation is still a novel and incipient approach in the literature [8]. 2.2 Recommender Systems Collaborative filtering strategies make automatic predictions (filter) about the interests of a user by collecting taste information from many users (collaborating). This approach usually consists of two steps: a) look for users that have a similar rating pattern to that of the active user (the user for whom the prediction is done), and b) use the ratings of users found in the previous step to compute the predictions for the active user. These predictions are specific to the user, differently to those given by more simple approaches that provide average scores for each item of interest, for example based on its number of votes. Collaborative filtering is a widely explored field. Three main aspects typically distinguish the different techniques reported in the literature [13]: user profile representation and management, filtering method, and matching method. User profile representation and management can be divided into five different tasks: • Profile representation. Accurate profiles are vital for the content-based component (to ensure recommendations are appropriate) and the collaborative component (to ensure that users with similar profiles are in fact similar). The type of profile chosen in this work is the user-item ratings matrix (ontology evaluations based on specific criteria). • Initial profile generation. The user is not usually willing to spend too much time in defining her/his interests to create a personal profile. Moreover, user interests may change dynamically over time. The type of initial profile generation chosen in this work is a manual selection of values for only five specific evaluation criteria. • Profile learning. User profiles can be learned or updated using different sources of information that are potentially representative of user interests. In our work, profile learning techniques are not used. • The source of user input and feedback to infer user interests from information used to update user profiles. It can be obtained in two different ways: using information explicitly provided by the user, and using information implicit observed in the user’s interaction. Our system uses no feedback to update the user profiles. • Profile adaptation. Techniques are needed to adapt the user profiles to new interests and forget old ones as user interests evolve with time. Again, in our approach profile adaptation is done manually (manual update of ontology evaluations). Filtering method. Items or actions are recommended to a user taking into account the available information (item content descriptions and user profiles). There are three main information filtering approaches for making recommendations: • Demographic filtering: Descriptions of people (e.g. age, gender, etc) are used to learn the relationship between a single item and the type of people who like it. • Content-based filtering: The user is recommended items based on the descriptions of items previously evaluated by other users. Content-based filtering is chosen approach in our work (the system recommends ontologies using previous evaluations of those ontologies). • Collaborative filtering: People with similar interests are matched and then recommendations are made. Matching method. It defines how user interests and item characteristics are compared. Two main approaches can be identified: • User profile matching: people with similar interests are matched before making recommendations. • User profile-item matching: a direct comparison is made between the user profile and the items. The degree of appropriateness of the ontologies is computed by taking into account previous evaluations of those ontologies. In WebCORE, a new ontology evaluation measure based on collaborative filtering is proposed, considering users’ interests and previous assessments of the ontologies. 3. SYSTEM ARCHITECTURE As mentioned before, WebCORE is a web application for Collaborative Ontology Reuse and Evaluation. A user logins into the system via a web browser, and, thanks to AJAX technology and the Google Web Toolkit3 , dynamically describes a problem domain, searches for ontologies related to this domain, obtains relevant ontologies ranked by several lexical, taxonomic and collaborative criteria, and optionally evaluates by himself those ontologies that he likes or dislikes most. In this section, we describe the server-side architecture of WebCORE. Figure 1 shows an overview of the system. We distinguish three different modules. The first one, the left module, receives the problem description (Golden Standard) as a full text or as a set of initial terms. In the first case, the system uses a NLP module to obtain the most relevant terms of the given text. The initial set of terms can also be modified and extended by the user using WordNet [12]. The second one, represented in the centre of the figure, allows the user to select a set of ontology evaluation techniques provided by the system to recover the ontologies closest to the given Golden Standard. Finally, the third one, on the right of the figure, is a collaborative module that re-ranks the list of recovered ontologies, taking into consideration previous feedback and evaluations of the users. 3 Google Web Toolkit, http://code.google.com/webtoolkit/