A platform for semantic web studies

The Semantic Web can be seen as a large, heterogeneous network of ontologies and semantic documents. Characterizing these ontologies, the way they relate and the way they are organized can help in better understanding how knowledge is produced and published online. It also provides new ways to explore and exploit this large collection of ontologies. In this paper, we present the foundation of a research platform for characterizing the Semantic Web, relying on the collection of ontologies and the functionalities provided by the Watson Semantic Web search engine. We more specifically focus on formalizing and monitoring relationships between ontologies online, considering a variety of different relations (similarity, versioning, agreement, modularity) and how they can help us obtaining meaningful overviews of the current state of the Semantic Web

Characterizing semantic web services

Semantic Web is an extension of the current web in which data contained in the web documents are machine-understandable. On the other hand, Web Services provide a new model of the web in which sites exchange dynamic information on demand. Combination of both introduces a new concept named Semantic Web Services in which semantic information is added to the different activities involved in Web Services, such as discovering, publication, composition, etc. In this paper, we analyze several proposals implementing Semantic Web Services. In order to describe them, we create a conceptual framework characterizing the main aspects of each proposal.Eje: I - Workshop de Ingeniería de Software y Base de DatosRed de Universidades con Carreras en Informática (RedUNCI

Discovering user access pattern based on probabilistic latent factor model

There has been an increased demand for characterizing user access patterns using web mining techniques since the informative knowledge extracted from web server log files can not only offer benefits for web site structure improvement but also for better understanding of user navigational behavior. In this paper, we present a web usage mining method, which utilize web user usage and page linkage information to capture user access pattern based on Probabilistic Latent Semantic Analysis (PLSA) model. A specific probabilistic model analysis algorithm, EM algorithm, is applied to the integrated usage data to infer the latent semantic factors as well as generate user session clusters for revealing user access patterns. Experiments have been conducted on real world data set to validate the effectiveness of the proposed approach. The results have shown that the presented method is capable of characterizing the latent semantic factors and generating user profile in terms of weighted page vectors, which may reflect the common access interest exhibited by users among same session cluster. © 2005, Australian Computer Society, Inc

Characterizing semantic web services

Semantic Web is an extension of the current web in which data contained in the web documents are machine-understandable. On the other hand, Web Services provide a new model of the web in which sites exchange dynamic information on demand. Combination of both introduces a new concept named Semantic Web Services in which semantic information is added to the different activities involved in Web Services, such as discovering, publication, composition, etc. In this paper, we analyze several proposals implementing Semantic Web Services. In order to describe them, we create a conceptual framework characterizing the main aspects of each proposal.Eje: I - Workshop de Ingeniería de Software y Base de DatosRed de Universidades con Carreras en Informática (RedUNCI

Optimizing the computation of overriding

We introduce optimization techniques for reasoning in DLN---a recently introduced family of nonmonotonic description logics whose characterizing features appear well-suited to model the applicative examples naturally arising in biomedical domains and semantic web access control policies. Such optimizations are validated experimentally on large KBs with more than 30K axioms. Speedups exceed 1 order of magnitude. For the first time, response times compatible with real-time reasoning are obtained with nonmonotonic KBs of this size

Selecting answers to questions from Web documents by a robust validation process

International audienceQuestion answering (QA) systems aim at finding answers to question posed in natural language using a collection of documents. When the collection is extracted from the Web, the structure and style of the texts are quite different from those of newspaper articles. We developed a QA system based on an answer validation process able to handle Web specificity. A large number of candidate answers are extracted from short passages in order to be validated according to question and passages characteristics. The validation module is based on a machine learning approach. It takes into account criteria characterizing both the passage and answer relevance at the surface, lexical, syntactic and semantic levels to deal with different types of texts. We present and compare results obtained for factual questions posed on a Web and on a newspaper collection. We show that our system outperforms a baseline by up to 48% in MRR