A feature and information theoretic framework for semantic similarity and relatedness

pirro2010bInternational audienceSemantic similarity and relatedness measures between ontology concepts are useful in many research areas. While similarity only considers subsumption relations to assess how two objects are alike, relatedness takes into account a broader range of relations (e.g., part-of). In this paper, we present a framework, which maps the feature-based model of similarity into the information theoretic domain. A new way of computing IC values directly from an ontology structure is also introduced. This new model, called Extended Information Content (eIC) takes into account the whole set of semantic relations defined in an ontology. The proposed framework enables to rewrite existing similarity measures that can be augmented to compute semantic relatedness. Upon this framework, a new measure called FaITH (Feature and Information THeoretic) has been devised. Extensive experimental evaluations confirmed the suitability of the framework

From Theoretical Framework To Generic Semantic Measures Library

International audienceThanks to the ever-increasing use of the Semantic Web, a growing number of entities (e.g. documents) are characterized by non-ambiguous mean-ings. Based on this characterization, entities can subsequently be compared us-ing semantic measures. A plethora of measures have been designed given their critical importance in numerous treatments relying on ontologies. However, the improvement and use of semantic measures are currently hampered by the lack of a dedicated theoretical framework and an extensive generic software solution dedicated to them. To meet these needs, this paper presents a unified theoretical framework of graph-based semantic measures, from which we developed the open source Semantic Measures Library and toolkit; a solution that paves the way for straightforward design, computation and analysis of semantic measures for both users and developers. Downloads, documentation and technical support at dedicated website http://www.semantic-measures-library.org

Sélection Robuste de Mesures de Similarité Sémantique à partir de Données Incertaines d'Expertise

National audienceKnowledge-based semantic measures are cornerstone to exploit ontologies not only for exact inferences or retrieval processes, but also for data analyses and inexact searches. Abstract theoretical frameworks have recently been proposed in order to study the large diversity of measures available; they demonstrate that groups of measures are particular instantiations of general parameterized functions. In this paper, we study how such frameworks can be used to support the selection/design of measures. Based on (i) a theoretical framework unifying the measures, (ii) a software solution implementing this framework and (iii) a domain-specific benchmark, we define a semi-supervised learning technique to distinguish best measures for a concrete application. Next, considering uncertainty in both experts’ judgments and measures’ selection process, we extend this proposal for robust selection of semantic measures that best resists to these uncertainties. We illustrate our approach through a real use case in the biomedical domain..L'exploitation d'ontologies pour la recherche d'information, la découverte de connaissances ou le raisonnement approché nécessite l'utilisation de mesures sémantiques qui permettent d'estimer le degré de similarité entre des entités lexicales ou conceptuelles. Récemment un cadre théorique abstrait a été proposé afin d'unifier la grande diversité de ces mesures, au travers de fonctions paramétriques générales. Cet article propose une utilisation de ce cadre unificateur pour choisir une mesure. A partir du (i) cadre unificateur exprimant les mesures basées sur un ensemble limité de primitives, (ii) logiciel implémentant ce cadre et (iii) benchmark d'un domaine spécifique, nous utilisons une technique d'apprentissage semi-supervisé afin de fournir la meilleure mesure sémantique pour une application donnée. Ensuite, sachant que les données fournies par les experts sont entachées d'incertitude, nous étendons notre approche pour choisir la plus robuste parmi les meilleures mesures, i.e. la moins perturbée par les erreurs d'évaluation experte. Nous illustrons notre approche par une application dans le domaine biomédical. Mots-clés: Cadre unificateur, robustesse de mesures, incertitude d'expert, mesures de similarité sémantique, ontologies

A REVIEW ON FEATURE BASED APPROACH IN SEMANTIC SIMILARITY FOR MULTIPLE ONTOLOGY

Measuring semantic similarity between terms is an important step in information retrieval and information integration which requires semantic content matching. Semantic similarity has attracted great concern for a long time in artificial intelligence, psychology and cognitive science. This paper contains a review of the state of art approaches including structure based approach, information content based approach, and feature based approach and hybrid approach. We also discussed similarity according to their advantages, disadvantages and issues related to multiple ontology especially on method in features based approach

Creating a Probabilistic Model for WordNet

We present a probabilistic model for extracting and storing information from WordNet and the British National Corpus. We map the data into a directed probabilistic graph that can be used to compute the conditional probability between a pair of words from the English language. For example, the graph can be used to deduce that there is a 10% probability that someone who is interested in dogs is also interested in the word “canine”. We propose three ways for computing this probability, where the best results are achieved when performing multiple random walks in the graph. Unlike existing approaches that only process the structured data in WordNet, we process all available information, including natural language descriptions. The available evidence is expressed as simple Horn clauses with probabilities. It is then aggregated using a Markov Logic Network model to create the probabilistic graph. We experimentally validate the quality of the data on five different benchmarks that contain collections of pairs of words and their semantic similarity as determined by humans. In the experimental section, we show that our random walk algorithm with logarithmic distance metric produces higher correlation with the results of the human judgment on three of the five benchmarks and better overall average correlation than the current state-of-the-art algorithms

Enhanced matching engine for improving the performance of semantic web service discovery

Web services are the means to realize the Service Oriented Architecture (SOA) paradigm. One of the key tasks of the Web services is discovery also known as matchmaking. This is the act of locating suitable Web services to fulfill a specific goal and adding semantic descriptions to the Web services is the key to enabling an automated, intelligent discovery process. Current Semantic Web service discovery approaches are primarily classified into logic-based, non-logic-based and hybrid categories. An important challenge yet to be addressed by the current approaches is the use of the available constructs in Web service descriptions to achieve a better performance in matchmaking. Performance is defined in terms of precision and recall as well-known metrics in the information retrieval field. Moreover, when matchmaking a large number of Web services, maintaining a reasonable execution time becomes a crucial challenge. In this research, to address these challenges, a matching engine is proposed. The engine comprises a new logic-based and nonlogic- based matchmaker to improve the performance of Semantic Web service discovery. The proposed logic-based and non-logic-based matchmakers are also combined as a hybrid matchmaker for further improvement of performance. In addition, a pre-matching filter is used in the matching engine to enhance the execution time of matchmaking. The components of the matching engine were developed as prototypes and evaluated by benchmarking the results against data from the standard repository of Web services. The comparative evaluations in terms of performance and execution time highlighted the superiority of the proposed matching engine over the existing and prominent matchmakers. The proposed matching engine has been proven to enhance both the performance and execution time of the Semantic Web service discovery