Technorati, in an interview for a Spanish newspaper, "Intern To obtain the most appropriate ontology and fulfil ontold been transformed from the great library to the engineers'requirements, search engines and libraries should be complemented with evaluation methodologies Following this aspiration, the work presented here aims to Ontology evaluation can be defined as assessing the quality and the adequacy of an ontology for being used in a specific context, automatic evaluation techniques with explicit users'opinions and for a specific goal. From our perspective, onto experiences. This work follows a previous approach for constitutes the cornerstone of ontology reuse because it faces the Collaborative Ontology Reuse and Evaluation over controlled complex task of evaluate, and consequently select the most epositories, named CORE [8]. For the work reported in this appropriate ontology on each situation. paper, the tool has been enhanced and adapted to the Web. Novel An overview of ontology evaluation approaches is presented in as AJAX, have been incorporated system for the design and implementation of the user interface. It evaluate an ontology by comparing it to a Golden Standard [11] limitations, such as handling large numbers of ontologies. The application and measuring the quality of the results that the different frameworks. Firstly, during the problem definition phase application returns [16 those that evaluate ontologies by showing other problem descriptions previously given by different documents)[5), and those based on human interaction to measure users. Secondly, during the ontology retrieval phase, the system the above approaches several evaluation levels are identified using other user evaluations and comments lexical, taxonomical, syntactic, semantic, contextual, and structural between others. Table I summarized these ideas Following Leonardo Da Vincis words, Wisdom is the daughter of experience", our tool aims to take a step forwards for helping Table 1. An overview of approaches to ontology evaluation users to be wise in exploiting other people's experience and expertise. Approach Golden Application Data Assessment The rest of the paper has been organized as follows. Section 2 standard summarizes some relevant work related to our system. Its Lexical entries, architecture is described in Section 3. Section 4 contains empirical esults obtained from early experiments done with a prototype of he system. Finally, several conclusions and future research lines Hierarc are given in Section 2 RELATED WORK 2.1 Ontology Evaluation application X Two well-known scenarios for ontology reuse have been identified in the Semantic Web area. The first one addresses the common problem of Structure specific domain. The second scenario envisions the not so architecture, design common but real situation in which Semantic Web applications Once the ontologies have been searched retrieved and evaluated need to automatically and dynamically find an ontology. In this the next step is to select the most appropriate one that fulfils user work. we focus our attention on the fist scenario. where users are or application goals. Some approaches for ontology selection have the ones who express their information needs. In this scenario, ntology reuse involves several areas such as ontology evaluation, complete study is presented to determine the connections betwee selection. search and ranking ontology selection and evaluation Several ontology libraries and search engines have been When the user and not the application is the one that demands an developed in the last few years to address the problem of ontology ontology, the selection task should be less categorical, returning search and retrieval. [6] presents a complete study of ontology ot only one but the set of the libraries (WebOnto, Ontolingua, SHOE, etc. ) where their esults according to the evaluation criteria, several ontology functionalities are evaluated attending to different criteria such ranking measures have been proposed in the literature. Some of them are presented in [2]and 3]. Both works aim to take a step ry beyond to the approaches based on the page- rank algorithm [24], olution for ontology retrieval, they suffer from the where ontologies are ranked considering the number of links limitation of not being opened to the web. In that sense, S between them, because this ranking methodology does not work [24] constitutes one of the biggest efforts carried out to for ontologies with poor connectivity and lack of referrals from index and search for ontologies distributed across the Web Garrett, J. J.(2005). AJAX. A New Approach to Web ApplicationsiNhttp://wTechnorati, in an interview for a Spanish newspaper, “Internet has been transformed from the great library to the great conversation”. Following this aspiration, the work presented here aims to enhance ontology retrieval and recommendation, combining automatic evaluation techniques with explicit users’ opinions and experiences. This work follows a previous approach for Collaborative Ontology Reuse and Evaluation over controlled repositories, named CORE [8]. For the work reported in this paper, the tool has been enhanced and adapted to the Web. Novel technologies, such as AJAX2 , have been incorporated to the system for the design and implementation of the user interface. It has also been modified and improved to overcome previous limitations, such as handling large numbers of ontologies. The collaborative capabilities have also been extended within two different frameworks. Firstly, during the problem definition phase, the system helps users to express their needs and requirements by showing other problem descriptions previously given by different users. Secondly, during the ontology retrieval phase, the system helps users to enhance the automatic system recommendations by using other user evaluations and comments. Following Leonardo Da Vinci’s words, “Wisdom is the daughter of experience”, our tool aims to take a step forwards for helping users to be wise in exploiting other people’s experience and expertise. The rest of the paper has been organized as follows. Section 2 summarizes some relevant work related to our system. Its architecture is described in Section 3. Section 4 contains empirical results obtained from early experiments done with a prototype of the system. Finally, several conclusions and future research lines are given in Section 5. 2. RELATED WORK 2.1 Ontology Evaluation Two well-known scenarios for ontology reuse have been identified in the Semantic Web area. The first one addresses the common problem of finding the most adequate ontologies for a specific domain. The second scenario envisions the not so common but real situation in which Semantic Web applications need to automatically and dynamically find an ontology. In this work, we focus our attention on the fist scenario, where users are the ones who express their information needs. In this scenario, ontology reuse involves several areas such as ontology evaluation, selection, search and ranking. Several ontology libraries and search engines have been developed in the last few years to address the problem of ontology search and retrieval. [6] presents a complete study of ontology libraries (WebOnto, Ontolingua, SHOE, etc.), where their functionalities are evaluated attending to different criteria such as ontology management, ontology adaptation and ontology standardization. Although ontology libraries are a good temporary solution for ontology retrieval, they suffer from the current limitation of not being opened to the web. In that sense, Swoogle [24] constitutes one of the biggest efforts carried out to crawl, index and search for ontologies distributed across the Web. 2 Garrett, J. J. (2005). AJAX: A New Approach to Web Applications. In http://www.adaptivepath.com/ To obtain the most appropriate ontology and fulfil ontology engineers’ requirements, search engines and libraries should be complemented with evaluation methodologies. Ontology evaluation can be defined as assessing the quality and the adequacy of an ontology for being used in a specific context, for a specific goal. From our perspective, ontology evaluation constitutes the cornerstone of ontology reuse because it faces the complex task of evaluate, and consequently select the most appropriate ontology on each situation. An overview of ontology evaluation approaches is presented in [4], where four different categories are identified: those that evaluate an ontology by comparing it to a Golden Standard [11]; those that evaluate the ontologies by plugging them in an application and measuring the quality of the results that the application returns [16]; those that evaluate ontologies by comparing them to unstructured or informal data (e.g. text documents) [5], and those based on human interaction to measure ontology features not recognizable by machines [10]. In each of the above approaches several evaluation levels are identified: lexical, taxonomical, syntactic, semantic, contextual, and structural between others. Table 1 summarized these ideas. Table 1. An overview of approaches to ontology evaluation Approach Level Golden Standard Application based Data driven Assessment by humans Lexical entries, vocabulary, concept, data X X X X Hierarchy, taxonomy X X X X Other semantic relations X X X X Context, application X X Syntactic X X Structure, architecture, design X Once the ontologies have been searched, retrieved and evaluated, the next step is to select the most appropriate one that fulfils user or application goals. Some approaches for ontology selection have been addressed in [20] and complemented in [19], where a complete study is presented to determine the connections between ontology selection and evaluation. When the user and not the application is the one that demands an ontology, the selection task should be less categorical, returning not only one but the set of the most suitable results. To sort these results according to the evaluation criteria, several ontology ranking measures have been proposed in the literature. Some of them are presented in [2] and [3]. Both works aim to take a step beyond to the approaches based on the page-rank algorithm [24], where ontologies are ranked considering the number of links between them, because this ranking methodology does not work for ontologies with poor connectivity and lack of referrals from other ontologies