328 research outputs found

    Ontology-based semantic annotation: an automatic hybrid rule-based method

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    International audienceIn the perspective of annotating a text with respect to an ontology, we have partici-pated in the subtask 1 of the BB BioNLP-ST whose aim is to detect, in the text, Bacteria Habitats and associate to them one or several categories from the Onto-Biotope ontology provided for the task. We have used a rule-based machine learn-ing algorithm (WHISK) combined with a rule-based automatic ontology projection method and a rote learning technique. The combination of these three sources of rules leads to good results with a SER measure close to the winner and a best F-measure

    A SPEMOntology for Software Processes Reusing

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    Reusing the best practices and know-how capitalized from existing software process models is a promising solution to model high quality software processes. This paper presents a part of AoSP (Architecture oriented Software Process) for software processes reuse based on software architectures. The solution is proposed after the study of existing works on software process reusing. AoSP approach deals with the engineering "for" and "by" reusing software processes, it exploits the progress of two research fields that promote reusing in order to improve the software process reusing: domain ontologies and software architectures. AoSP exploits a domain ontology to reuse software process know-how, it allows retrieving, describing and deploring software process architectures. This article details the engineering "for" reusing SPs step of AoSP, it explains how the software process architectures are described and discusses the software process ontology conceptualization and software process knowledge acquisition

    A Case-study for the Semantic Analysis of Sentences in Coq

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    This paper presents a case-study devoted to the formaliza-tion of sentence frames in the Coq system. Therefore, we instanciate these frames for performing a semantic analysis of simple sentences. In particular, we rely on a hierarchy of types for type-checking the conceptual well-formedness of sentences. To do so, we investigate how to exploit the particular features of the Coq type system in order to take advantage of this elegant unifying framework for encoding the syntax-semantics interface and then we show how to improve our approach for combining it with linguistic resources distributed

    Galaxy-Gen: A Tool for Building Galaxy model from XML documents

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    National audienceA galaxy model is a multidimensional model dedicated for XML document warehouses. It can be seen as a network of entities (i.e., dimensions) connected via nodes. After giving an overview of our four-steps semi-automated method for the generation of galaxy models which aims to build data marts from XML documents. This paper focuses on the software tool, called Galaxy-Gen that implements the proposed method. We illustrate the Galaxy-Gen functionalities and make its first assessment through two experiments. The first experiment is applied to a set of twenty XML documents taken from the academic domain. The second one addressed a set of 1691 XML documents issued from the Clef-2007 collection. The assessment is performed by comparing manual design galaxy models with those produced by the Galaxy-Gen tool. The results are very promising

    Query refinement for patent prior art search

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    A patent is a contract between the inventor and the state, granting a limited time period to the inventor to exploit his invention. In exchange, the inventor must put a detailed description of his invention in the public domain. Patents can encourage innovation and economic growth but at the time of economic crisis patents can hamper such growth. The long duration of the application process is a big obstacle that needs to be addressed to maximize the benefit of patents on innovation and economy. This time can be significantly improved by changing the way we search the patent and non-patent literature.Despite the recent advancement of general information retrieval and the revolution of Web Search engines, there is still a huge gap between the emerging technologies from the research labs and adapted by major Internet search engines, and the systems which are in use by the patent search communities.In this thesis we investigate the problem of patent prior art search in patent retrieval with the goal of finding documents which describe the idea of a query patent. A query patent is a full patent application composed of hundreds of terms which does not represent a single focused information need. Other relevance evidences (e.g. classification tags, and bibliographical data) provide additional details about the underlying information need of the query patent. The first goal of this thesis is to estimate a uni-gram query model from the textual fields of a query patent. We then improve the initial query representation using noun phrases extracted from the query patent. We show that expansion in a query-dependent manner is useful.The second contribution of this thesis is to address the term mismatch problem from a query formulation point of view by integrating multiple relevance evidences associated with the query patent. To do this, we enhance the initial representation of the query with the term distribution of the community of inventors related to the topic of the query patent. We then build a lexicon using classification tags and show that query expansion using this lexicon and considering proximity information (between query and expansion terms) can improve the retrieval performance. We perform an empirical evaluation of our proposed models on two patent datasets. The experimental results show that our proposed models can achieve significantly better results than the baseline and other enhanced models

    Using Linguistic Information and Machine Learning Techniques to Identify Entities from Juridical Documents

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    Information extraction from legal documents is an important and open problem. A mixed approach, using linguistic information and machine learning techniques, is described in this paper. In this approach, top-level legal concepts are identified and used for document classifica- tion using Support Vector Machines. Named entities, such as, locations, organizations, dates, and document references, are identified using se- mantic information from the output of a natural language parser. This information, legal concepts and named entities, may be used to popu- late a simple ontology, allowing the enrichment of documents and the creation of high-level legal information retrieval systems. The proposed methodology was applied to a corpus of legal documents - from the EUR-Lex site – and it was evaluated. The obtained results were quite good and indicate this may be a promising approach to the legal information extraction problem

    CRITICAL REVIEW OF THE INTEGRATION OF BIM TO SEMANTIC WEB TECHNOLOGY

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    The AEC–FM industry (Architecture/Engineering/Construction and Facilities Management) is increasingly using different building information modeling (BIM) methodology to solve complex challenges. With help of Semantic WEB technology, product data models and other relevant information are increasingly linked to BIM models. The article discusses the challenges of existing BIM standards to meet future requirements, to fully utilize semantic technology. The article provides suggestions for further research, and it specifically calls for a more strategic research that can look a bit longer than just the challenges associated with various limited case projects. The article discusses whether existing BIM formats are able to meet future requirements, where the potential in the construction industry to fully utilize semantic web technology is difficult with today's BIM standards. Furthermore, it is suggested that previously developed SW resources should be gathered, then earlier initiatives are easier to find, use and build upon. The literature study shows many initiatives spread across many domains in the AEC-FM area. Most studied articles have a high degree of technological focus, where the semantic web opportunities are tested in a chosen case.The findings of this study can be used as a starting point for further strategic research and development

    Analysis and use of the emotional context with wearable devices for games and intelligent assistants

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    In this paper, we consider the use of wearable sensors for providing affect-based adaptation in Ambient Intelligence (AmI) systems. We begin with discussion of selected issues regarding the applications of affective computing techniques. We describe our experiments for affect change detection with a range of wearable devices, such as wristbands and the BITalino platform, and discuss an original software solution, which we developed for this purpose. Furthermore, as a test-bed application for our work, we selected computer games. We discuss the state-of-the-art in affect-based adaptation in games, described in terms of the so-called affective loop. We present our original proposal of a conceptual design framework for games, called the affective game design patterns. As a proof-of-concept realization of this approach, we discuss some original game prototypes, which we have developed, involving emotion-based control and adaptation. Finally, we comment on a software framework, that we have previously developed, for context-aware systems which uses human emotional contexts. This framework provides means for implementing adaptive systems using mobile devices with wearable sensors
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