1,090 research outputs found

    Improving approximation of domain-focused, corpus-based, lexical semantic relatedness

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    Semantic relatedness is a measure that quantifies the strength of a semantic link between two concepts. Often, it can be efficiently approximated with methods that operate on words, which represent these concepts. Approximating semantic relatedness between texts and concepts represented by these texts is an important part of many text and knowledge processing tasks of crucial importance in many domain-specific scenarios. The problem of most state-of-the-art methods for calculating domain-specific semantic relatedness is their dependence on highly specialized, structured knowledge resources, which makes these methods poorly adaptable for many usage scenarios. On the other hand, the domain knowledge in the fields such as Life Sciences has become more and more accessible, but mostly in its unstructured form - as texts in large document collections, which makes its use more challenging for automated processing. In this dissertation, three new corpus-based methods for approximating domain-specific textual semantic relatedness are presented and evaluated with a set of standard benchmarks focused on the field of biomedicine. Nonetheless, the proposed measures are general enough to be adapted to other domain-focused scenarios. The evaluation involves comparisons with other relevant state-of-the-art measures for calculating semantic relatedness and the results suggest that the methods presented here perform comparably or better than other approaches. Additionally, the dissertation also presents an experiment, in which one of the proposed methods is applied within an ontology matching system, DisMatch. The performance of the system was evaluated externally on a biomedically themed ‘Phenotype’ track of the Ontology Alignment Evaluation Initiative 2016 campaign. The results of the track indicate, that the use distributional semantic relatedness for ontology matching is promising, as the system presented in this thesis did stand out in detecting correct mappings that were not detected by any other systems participating in the track. The work presented in the dissertation indicates an improvement achieved w.r.t. the stat-of-the-art through the domain adapted use of the distributional principle (i.e. the presented methods are corpus-based and do not require additional resources). The ontology matching experiment showcases practical implications of the presented theoretical body of work

    From Theoretical Framework To Generic Semantic Measures Library

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    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

    Dealing with uncertain entities in ontology alignment using rough sets

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    This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Ontology alignment facilitates exchange of knowledge among heterogeneous data sources. Many approaches to ontology alignment use multiple similarity measures to map entities between ontologies. However, it remains a key challenge in dealing with uncertain entities for which the employed ontology alignment measures produce conflicting results on similarity of the mapped entities. This paper presents OARS, a rough-set based approach to ontology alignment which achieves a high degree of accuracy in situations where uncertainty arises because of the conflicting results generated by different similarity measures. OARS employs a combinational approach and considers both lexical and structural similarity measures. OARS is extensively evaluated with the benchmark ontologies of the ontology alignment evaluation initiative (OAEI) 2010, and performs best in the aspect of recall in comparison with a number of alignment systems while generating a comparable performance in precision

    A Semantic neighborhood approach to relatedness evaluation on well-founded domain ontologies

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    In the context of natural language processing and information retrieval, ontologies can improve the results of the word sense disambiguation (WSD) techniques. By making explicit the semantics of the term, ontology-based semantic measures play a crucial role in determining how different ontology classes have a similar or related meaning. In this context, it is common to use semantic similarity as a basis for WSD. However, the measures generally consider only taxonomic relationships, which negatively affect the discrimination of two ontology classes that are related by the other relationship types. On the other hand, semantic relatedness measures consider diverse types of relationships to determine how much two classes on the ontology are related. However, these measures, especially the path-based approaches, have as the main drawback a high computational complexity to calculate the relatedness value. Also, for both types of semantic measures, it is unpractical to store all similarity or relatedness values between all ontology classes in memory, especially for ontologies with a large number of classes. In this work, we propose a novel approach based on semantic neighbors that aim to improve the performance of the knowledge-based measures in relatedness analysis. We also explain how to use this proposal into the path and feature-based measures. We evaluate our proposal on WSD using an existent domain ontology for a well-core description. This ontology contains 929 classes related to rock facies. Also, we use a set of sentences from four different corpora on the Oil&Gas domain. In the experiments, we compare our proposal with state-of-the-art semantic relatedness measures, such as path-based, feature-based, information content, and hybrid methods regarding the F-score, evaluation time, and memory consumption. The experimental results show that the proposed method obtains F-score gains in WSD, as well as a low evaluation time and memory consumption concerning the traditional knowledge-based measures.No contexto do processamento de linguagem natural e recuperação de informaçÔes, as ontologias podem melhorar os resultados das tĂ©cnicas de desambiguação. Ao tornar explĂ­cita a semĂąntica do termo, as medidas semĂąnticas baseadas em ontologia desempenham um papel crucial para determinar como diferentes classes de ontologia tĂȘm um significado semelhante ou relacionado. Nesse contexto, Ă© comum usar similaridade semĂąntica como base para a desembiguação. No entanto, as medidas geralmente consideram apenas relaçÔes taxonĂŽmicas, o que afeta negativamente a discriminação de duas classes de ontologia relacionadas por outros tipos de relaçÔes. Por outro lado, as medidas de relacionamento semĂąntico consideram diversos tipos de relacionamentos ontolĂłgicos para determinar o quanto duas classes estĂŁo relacionadas. No entanto, essas medidas, especialmente as abordagens baseadas em caminhos, tĂȘm como principal desvantagem uma alta complexidade computacional para sua execução. AlĂ©m disso, tende a ser impraticĂĄvel armazenar na memĂłria todos os valores de similaridade ou relacionamento entre todas as classes de uma ontologia, especialmente para ontologias com um grande nĂșmero de classes. Neste trabalho, propomos uma nova abordagem baseada em vizinhos semĂąnticos que visa melhorar o desempenho das medidas baseadas em conhecimento na anĂĄlise de relacionamento. TambĂ©m explicamos como usar esta proposta em medidas baseadas em caminhos e caracterĂ­sticas. Avaliamos nossa proposta na desambiguação utilizando uma ontologia de domĂ­nio preexistente para descrição de testemunhos. Esta ontologia contĂ©m 929 classes relacionadas a fĂĄcies de rocha. AlĂ©m disso, usamos um conjunto de sentenças de quatro corpora diferentes no domĂ­nio PetrĂłleo e GĂĄs. Em nossos experimentos, comparamos nossa proposta com medidas de relacionamento semĂąntico do estado-daarte, como mĂ©todos baseados em caminhos, caracterĂ­sticas, conteĂșdo de informação, e mĂ©todos hĂ­bridos em relação ao F-score, tempo de avaliação e consumo de memĂłria. Os resultados experimentais mostram que o mĂ©todo proposto obtĂ©m ganhos de F-score na desambiguação, alĂ©m de um baixo tempo de avaliação e consumo de memĂłria em relação Ă s medidas tradicionais baseadas em conhecimento

    Local matching learning of large scale biomedical ontologies

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    Les larges ontologies biomĂ©dicales dĂ©crivent gĂ©nĂ©ralement le mĂȘme domaine d'intĂ©rĂȘt, mais en utilisant des modĂšles de modĂ©lisation et des vocabulaires diffĂ©rents. Aligner ces ontologies qui sont complexes et hĂ©tĂ©rogĂšnes est une tĂąche fastidieuse. Les systĂšmes de matching doivent fournir des rĂ©sultats de haute qualitĂ© en tenant compte de la grande taille de ces ressources. Les systĂšmes de matching d'ontologies doivent rĂ©soudre deux problĂšmes: (i) intĂ©grer la grande taille d'ontologies, (ii) automatiser le processus d'alignement. Le matching d'ontologies est une tĂąche difficile en raison de la large taille des ontologies. Les systĂšmes de matching d'ontologies combinent diffĂ©rents types de matcher pour rĂ©soudre ces problĂšmes. Les principaux problĂšmes de l'alignement de larges ontologies biomĂ©dicales sont: l'hĂ©tĂ©rogĂ©nĂ©itĂ© conceptuelle, l'espace de recherche Ă©levĂ© et la qualitĂ© rĂ©duite des alignements rĂ©sultants. Les systĂšmes d'alignement d'ontologies combinent diffĂ©rents matchers afin de rĂ©duire l'hĂ©tĂ©rogĂ©nĂ©itĂ©. Cette combinaison devrait dĂ©finir le choix des matchers Ă  combiner et le poids. DiffĂ©rents matchers traitent diffĂ©rents types d'hĂ©tĂ©rogĂ©nĂ©itĂ©. Par consĂ©quent, le paramĂ©trage d'un matcher devrait ĂȘtre automatisĂ© par les systĂšmes d'alignement d'ontologies afin d'obtenir une bonne qualitĂ© de correspondance. Nous avons proposĂ© une approche appele "local matching learning" pour faire face Ă  la fois Ă  la grande taille des ontologies et au problĂšme de l'automatisation. Nous divisons un gros problĂšme d'alignement en un ensemble de problĂšmes d'alignement locaux plus petits. Chaque problĂšme d'alignement local est indĂ©pendamment alignĂ© par une approche d'apprentissage automatique. Nous rĂ©duisons l'Ă©norme espace de recherche en un ensemble de taches de recherche de corresondances locales plus petites. Nous pouvons aligner efficacement chaque tache de recherche de corresondances locale pour obtenir une meilleure qualitĂ© de correspondance. Notre approche de partitionnement se base sur une nouvelle stratĂ©gie Ă  dĂ©coupes multiples gĂ©nĂ©rant des partitions non volumineuses et non isolĂ©es. Par consĂ©quence, nous pouvons surmonter le problĂšme de l'hĂ©tĂ©rogĂ©nĂ©itĂ© conceptuelle. Le nouvel algorithme de partitionnement est basĂ© sur le clustering hiĂ©rarchique par agglomĂ©ration (CHA). Cette approche gĂ©nĂšre un ensemble de tĂąches de correspondance locale avec un taux de couverture suffisant avec aucune partition isolĂ©e. Chaque tĂąche d'alignement local est automatiquement alignĂ©e en se basant sur les techniques d'apprentissage automatique. Un classificateur local aligne une seule tĂąche d'alignement local. Les classificateurs locaux sont basĂ©s sur des features Ă©lĂ©mentaires et structurelles. L'attribut class de chaque set de donne d'apprentissage " training set" est automatiquement Ă©tiquetĂ© Ă  l'aide d'une base de connaissances externe. Nous avons appliquĂ© une technique de sĂ©lection de features pour chaque classificateur local afin de sĂ©lectionner les matchers appropriĂ©s pour chaque tĂąche d'alignement local. Cette approche rĂ©duit la complexitĂ© d'alignement et augmente la prĂ©cision globale par rapport aux mĂ©thodes d'apprentissage traditionnelles. Nous avons prouvĂ© que l'approche de partitionnement est meilleure que les approches actuelles en terme de prĂ©cision, de taux de couverture et d'absence de partitions isolĂ©es. Nous avons Ă©valuĂ© l'approche d'apprentissage d'alignement local Ă  l'aide de diverses expĂ©riences basĂ©es sur des jeux de donnĂ©es d'OAEI 2018. Nous avons dĂ©duit qu'il est avantageux de diviser une grande tĂąche d'alignement d'ontologies en un ensemble de tĂąches d'alignement locaux. L'espace de recherche est rĂ©duit, ce qui rĂ©duit le nombre de faux nĂ©gatifs et de faux positifs. L'application de techniques de sĂ©lection de caractĂ©ristiques Ă  chaque classificateur local augmente la valeur de rappel pour chaque tĂąche d'alignement local.Although a considerable body of research work has addressed the problem of ontology matching, few studies have tackled the large ontologies used in the biomedical domain. We introduce a fully automated local matching learning approach that breaks down a large ontology matching task into a set of independent local sub-matching tasks. This approach integrates a novel partitioning algorithm as well as a set of matching learning techniques. The partitioning method is based on hierarchical clustering and does not generate isolated partitions. The matching learning approach employs different techniques: (i) local matching tasks are independently and automatically aligned using their local classifiers, which are based on local training sets built from element level and structure level features, (ii) resampling techniques are used to balance each local training set, and (iii) feature selection techniques are used to automatically select the appropriate tuning parameters for each local matching context. Our local matching learning approach generates a set of combined alignments from each local matching task, and experiments show that a multiple local classifier approach outperforms conventional, state-of-the-art approaches: these use a single classifier for the whole ontology matching task. In addition, focusing on context-aware local training sets based on local feature selection and resampling techniques significantly enhances the obtained results

    Finding disease similarity based on implicit semantic similarity

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    AbstractGenomics has contributed to a growing collection of gene–function and gene–disease annotations that can be exploited by informatics to study similarity between diseases. This can yield insight into disease etiology, reveal common pathophysiology and/or suggest treatment that can be appropriated from one disease to another. Estimating disease similarity solely on the basis of shared genes can be misleading as variable combinations of genes may be associated with similar diseases, especially for complex diseases. This deficiency can be potentially overcome by looking for common biological processes rather than only explicit gene matches between diseases. The use of semantic similarity between biological processes to estimate disease similarity could enhance the identification and characterization of disease similarity. We present functions to measure similarity between terms in an ontology, and between entities annotated with terms drawn from the ontology, based on both co-occurrence and information content. The similarity measure is shown to outperform other measures used to detect similarity. A manually curated dataset with known disease similarities was used as a benchmark to compare the estimation of disease similarity based on gene-based and Gene Ontology (GO) process-based comparisons. The detection of disease similarity based on semantic similarity between GO Processes (Recall=55%, Precision=60%) performed better than using exact matches between GO Processes (Recall=29%, Precision=58%) or gene overlap (Recall=88% and Precision=16%). The GO-Process based disease similarity scores on an external test set show statistically significant Pearson correlation (0.73) with numeric scores provided by medical residents. GO-Processes associated with similar diseases were found to be significantly regulated in gene expression microarray datasets of related diseases

    Semantic Similarity in Cheminformatics

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    Similarity in chemistry has been applied to a variety of problems: to predict biochemical properties of molecules, to disambiguate chemical compound references in natural language, to understand the evolution of metabolic pathways, to predict drug-drug interactions, to predict therapeutic substitution of antibiotics, to estimate whether a compound is harmful, etc. While measures of similarity have been created that make use of the structural properties of the molecules, some ontologies (the Chemical Entities of Biological Interest (ChEBI) being one of the most relevant) capture chemistry knowledge in machine-readable formats and can be used to improve our notions of molecular similarity. Ontologies in the biomedical domain have been extensively used to compare entities of biological interest, a technique known as ontology-based semantic similarity. This has been applied to various biologically relevant entities, such as genes, proteins, diseases, and anatomical structures, as well as in the chemical domain. This chapter introduces the fundamental concepts of ontology-based semantic similarity, its application in cheminformatics, its relevance in previous studies, and future potential. It also discusses the existing challenges in this area, tracing a parallel with other domains, particularly genomics, where this technique has been used more often and for longer
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