Data linking: capturing and utilising implicit schema-level relations

Schema-level heterogeneity represents an obstacle for automated discovery of coreference resolution links between individuals. Although there is a multitude of existing schema matching solutions, the Linked Data environment differs from the standard scenario assumed by these tools. In particular, large volumes of data are available, and repositories are connected into a graph by instance-level mappings. In this paper we describe how these features can be utilised to produce schema-level mappings which facilitate the instance coreference resolution process. Initial experiments applying this approach to public datasets have produced encouraging results

The Analysis of Rank Fusion Techniques to Improve Query Relevance

Rank fusion meta-search engine algorithms can be used to merge web search results of multiple search engines. In this paper we introduce two variants of the Weighted Borda-Fuse algorithm. The first variant retrieves documents based on popularities of component engines. The second one is based on k user-defined toplist of component engines. In this research, experiments were performed on k={50,100,200} toplist with AND/OR combinations implemented on ‘UNIB Meta Fusion’ meta-search engine prototype which employed 3 out of 5 popular search engines. Both of our two algorithms outperformed other rank fusion algorithms (relevance score is upto 0.76 compare to Google that is 0.27, at P@10). The pseudo-relevance automatic judgement techniques involved are Reciprocal Rank, Borda Count, and Condorcet. The optimal setting was reached for queries with operator "AND" (degree 1) or "AND ... AND" (degree 2) with k=200. The ‘UNIB Meta Fusion’ meta-search engine system was built correctly

Towards data fusion in a multi-ontology environment

With the growing amount of semantic data being published on the Web the problem of finding individuals in different datasets which correspond to the same entity is gaining importance. Given that datasets are often structured using different ontologies, automatic schema-matching techniques have to be utilized before proceeding with data-level alignment. In this paper we discuss how ontology schema mis- matches influence data-level alignment based on our first experience with implementing a data fusion tool for a multi-ontology environment

La ressource et l'ontologie du Web

27 pages. Soumis à la revue Intellectica.Au plan architectural, le Web se compose de trois éléments : les URIs, le protocole Http et des langages associées (HTML, RDF...). Or, ce qu'une URI identifie n'est autre qu'une ressource. Les débats se sont généralement focalisés sur la manière dont ces URIs font référence, par analogie avec la question philosophique du nom propre logique, opposant théorie des descriptions définies (Russell), de la référence directe (Kripke) et de la signification par l'usage (Wittgenstein). Cependant, cela revient à laisser de côté un point essentiel : qu'est-ce qu'une URI identifie ? Le Web, avant d'être un Web de document ou d'objet est un Web de ressources. En donner une caractérisation précise est essentiel. Nous proposons ici de nous appuyer sur les axiomes de l'architecture du Web pour en fournir un examen ontologique aboutissant à faire de la ressource une entité abstraite semblable à une règle à double usage : identifier des entités et en générer des " Http-représentations "

American Square Dance Vol. 61, No. 1 (Jan. 2006)

Monthly square dance magazine that began publication in 1945

Contextual Healing: What to Do About Scandalous Trademarks and Lanham Act 2(a)

Offensive trademarks have come to the forefront of trademark policy and practice in recent years. While it was once true that more attention had been paid to Lanham Act section 2(a) in the pages of law reviews than in the courts, recent prominent cases have focused attention on the ban on registration of offensive marks and the widespread impact of this ban on trademark owners. In this Article, I answer the fundamental question: Given the problems that my previous research has identified, what should be done about the 2(a) bar on registration of scandalous trademarks? This Article argues, as a preliminary matter, that the registration bar on scandalous marks should be removed from the Lanham Act because morality is outside the function and purpose of trademark law. Furthermore, removal of the bar would be in line with other forms of intellectual property, which have moved away from regulating morality. Finally, removing the bar would resolve concerns about the constitutionality of section 2(a). However, if the 2(a) bar remains part of the Lanham Act, it should be applied in a way that is fair and effective within in the legal framework of trademark law. Specifically, this Article argues that trademark examiners should evaluate offensiveness in the same way other bars to registration — and content in broadcast media — are evaluated: by considering the context of the marketplace

User Semantic Model for Hybrid Recommender Systems

International audienceRecommender systems provide relevant items to users from a large number of choices. In this work, we are interested in personalized recommender systems where user model is based on an analysis of usage. Collaborative filtering and content-based filtering are the most widely used techniques in personalized recommender systems. Each technique has its drawbacks, so hybrid solutions, combining the two techniques, have emerged to overcome their disadvantages and benefit from their strengths. In this paper, we propose a hybrid solution combining collaborative filtering and content-based filtering. With this aim, we have defined a new user model, called user semantic model, to model user semantic preferences based on items' features and user ratings. The user semantic model is built from the user-item model by using a fuzzy clustering algorithm: the Fuzzy C Mean (FCM) algorithm. Then, we used the user semantic model in a user-based collaborative filtering algorithm to calculate the similarity between users. Applying our approach to the MoviesLens dataset, significant improvements can be noticed comparatively to standards user-based and item-based collaborative filtering algorithms