Improving Ontology Recommendation and Reuse in Webcore by collaborative Assessments Ivan Cantador. Miriam Fernandez. Pablo castells Escuela Politecnica Superior Universidad autonoma de madrid Campus de Cantoblanco, 28049, Madrid, Spain livan. cantador, miriam. fernandez, pablo castells]@uames ABSTRACT automatically find, share and combine information in consistent In this work, we present an of CORE (8), a tool for ways. As put by Tim Berners-Lee in 1999, "I have a dream for Collaborative Ontology reuse the Web in which computers become capable of analyzing all the an informal description of ific semantic domain and data on the Web-the content, links, and transactions behveen determines which onto es from people and computers. A 'Semantic Web, which should make this appropriate to describe the given domain. For this task, the possible, has emerge, but when it does, the day- to-day environment is divided into three modules. The first component mechanisms of trade, bureaucracy and our daily lives will be handled by machines talking to machines. The ' intelligent agents user to refine and enlarge it using WordNet. The second module people have touted for ages will finally materialize applies multiple automatic criteria to evaluate the ontologies of the At the core of these new technologies, ontologies are envisioned repository, and determines which ones fit best the problem as key elements to represent knowledge that can be understood, on. A ranked list of ontologies is returned for each criterio used and shared among distributed applications and machines and the lists are combined by means of rank fusion techniques However, ontological knowledge mining and development are Finally, the third component uses manual user evaluations in order difficult and costly tasks that require major engineering efforts to incorporate a human, collaborative assessment of the ontologies. Developing an ontology from scratch requires the expertise of at The new version of the system incorporates several novelties, such least two different individuals an ontology engineer that ensures as its implementation as a web application; the incorporation of a the correctness during the ontology design and development, and NLP module to manage the problem definitions, modifications on a domain expert, responsible for capturing the semantics of a ecific field into the ontology. In this context, ontology reuse plos find potential relevant terms according to previous becomes an essential need in order to exploit past and current Finally, we present some early experiments on efforts and achievements leval and evaluation, showing the benefits of our system. In this scenario, it is also important to emphasize that ontologies as well as content, do not stop evolving and growing within the Categories and subject descriptors Web. They are part of its wave of growth and evolution, and they H.3.3 [Information Storage and Retrieval]: Information Search anaged and kept up to date in distributed and Retrieval -information filtering, retrieval models, selection environments. In this perspective, the initial efforts to collect ontologies in libraries [17 are not sufficient, and novel technologies are necessary to successfully retrieve this special General Terms Algorithms, Measurement, Human Factors Novel tools have been recently developed, such as ontology search engines [24] represent an important first step towards Keywords automatically assessing and retrieving ontologies which satisfy Ontology evaluation, ontology reuse, rank fusion, collaborative additional efforts to address special needs and requirements from ontology engineers and practitioners. It is necessary to evaluate and measure specific ontology features, such as lexical 1. INTRODUCTION vocabulary, relations [11, restrictions, consistency, correctness, he Web can be considered as a live entity that grows and before making an adequate selection. Some of these features fast over time. The amount of content stored and shared can be measured automatically, but some, like the correctness web is increasing quickly and continuously. The global the level of formality, require a human judgment to be assessed ultimedia resources on the Internet is undergoing a significant In this context, the Web 2.0 is arising as a new trend where peopl growth, reaching a presence comparable to that of traditional text collaborate and share their knowledge to successfully achieve contents. The consequences of this enlargement result in well their goals. New search engines like Technorati exploit blogs known difficulties and problems, such as finding and properly with the aim of finding not only the information that the user is managing all the existing amount of sparse information. oking for, but also the experts that might better answer the To overcome these limitations the so-called "Semantic Web" users'requirements. As put by David Sifry, one of the founders of d has emerged with the aim of helping machines process nformation, enabling browsers or other software agents to I Technorati, blog search engin horath.cImproving Ontology Recommendation and Reuse in WebCORE by Collaborative Assessments Iván Cantador, Miriam Fernández, Pablo Castells Escuela Politécnica Superior Universidad Autónoma de Madrid Campus de Cantoblanco, 28049, Madrid, Spain {ivan.cantador, miriam.fernandez, pablo.castells}@uam.es ABSTRACT In this work, we present an extension of CORE [8], a tool for Collaborative Ontology Reuse and Evaluation. The system receives an informal description of a specific semantic domain and determines which ontologies from a repository are the most appropriate to describe the given domain. For this task, the environment is divided into three modules. The first component receives the problem description as a set of terms, and allows the user to refine and enlarge it using WordNet. The second module applies multiple automatic criteria to evaluate the ontologies of the repository, and determines which ones fit best the problem description. A ranked list of ontologies is returned for each criterion, and the lists are combined by means of rank fusion techniques. Finally, the third component uses manual user evaluations in order to incorporate a human, collaborative assessment of the ontologies. The new version of the system incorporates several novelties, such as its implementation as a web application; the incorporation of a NLP module to manage the problem definitions; modifications on the automatic ontology retrieval strategies; and a collaborative framework to find potential relevant terms according to previous user queries. Finally, we present some early experiments on ontology retrieval and evaluation, showing the benefits of our system. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval – information filtering, retrieval models, selection process. General Terms Algorithms, Measurement, Human Factors. Keywords Ontology evaluation, ontology reuse, rank fusion, collaborative filtering, WordNet. 1. INTRODUCTION The Web can be considered as a live entity that grows and evolves fast over time. The amount of content stored and shared on the web is increasing quickly and continuously. The global body of multimedia resources on the Internet is undergoing a significant growth, reaching a presence comparable to that of traditional text contents. The consequences of this enlargement result in well known difficulties and problems, such as finding and properly managing all the existing amount of sparse information. To overcome these limitations the so-called “Semantic Web” trend has emerged with the aim of helping machines process information, enabling browsers or other software agents to automatically find, share and combine information in consistent ways. As put by Tim Berners-Lee in 1999, “I have a dream for the Web in which computers become capable of analyzing all the data on the Web – the content, links, and transactions between people and computers. A ‘Semantic Web’, which should make this possible, has yet to emerge, but when it does, the day-to-day mechanisms of trade, bureaucracy and our daily lives will be handled by machines talking to machines. The ‘intelligent agents’ people have touted for ages will finally materialize”. At the core of these new technologies, ontologies are envisioned as key elements to represent knowledge that can be understood, used and shared among distributed applications and machines. However, ontological knowledge mining and development are difficult and costly tasks that require major engineering efforts. Developing an ontology from scratch requires the expertise of at least two different individuals: an ontology engineer that ensures the correctness during the ontology design and development, and a domain expert, responsible for capturing the semantics of a specific field into the ontology. In this context, ontology reuse becomes an essential need in order to exploit past and current efforts and achievements. In this scenario, it is also important to emphasize that ontologies, as well as content, do not stop evolving and growing within the Web. They are part of its wave of growth and evolution, and they need to be managed and kept up to date in distributed environments. In this perspective, the initial efforts to collect ontologies in libraries [17] are not sufficient, and novel technologies are necessary to successfully retrieve this special kind of content. Novel tools have been recently developed, such as ontology search engines [24] represent an important first step towards automatically assessing and retrieving ontologies which satisfy user queries and requests. However, ontology reuse demands additional efforts to address special needs and requirements from ontology engineers and practitioners. It is necessary to evaluate and measure specific ontology features, such as lexical vocabulary, relations [11], restrictions, consistency, correctness, etc., before making an adequate selection. Some of these features can be measured automatically, but some, like the correctness or the level of formality, require a human judgment to be assessed. In this context, the Web 2.0 is arising as a new trend where people collaborate and share their knowledge to successfully achieve their goals. New search engines like Technorati1 exploit blogs with the aim of finding not only the information that the user is looking for, but also the experts that might better answer the users’ requirements. As put by David Sifry, one of the founders of 1 Technorati, blog search engine, http://technorati.com/