Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

Ontological Engineering: with examples from the areas of Knowledge Management, e-Commerce and the Semantic Web. (Advanced Information and Knowledge Processing)

In this paper we review and compare the main methodologies, tools and languages for building ontologies that have been reported in the literature, as well as the main relationships among them. Ontology technology is nowadays mature enough: many methodologies, tools and languages are already available. The future work in this field should be driven towards the creation of a common integrated workbench for ontology developers to facilitate ontology development, exchange, evaluation, evolution and management, to provide methodological support for these tasks, and translations to and from different ontology languages. This workbench should not be created from scratch, but instead integrating the technology components that are currently available.

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Methodologies, tools and languages for building ontologies: where is their meeting point?

Measuring the similarity between words, sentences, paragraphs and documents is an important component in various tasks such as information retrieval, document clustering, word-sense disambiguation, automatic essay scoring, short answer grading, machine translation and text summarization. This survey discusses the existing works on text similarity through partitioning them into three approaches; String-based, Corpus-based and Knowledge-based similarities. Furthermore, samples of combination between these similarities are presented. General Terms Text Mining, Natural Language Processing. Keywords BasedText Similarity, Semantic Similarity, String-Based Similarity, Corpus-Based Similarity, Knowledge-Based Similarity. NeedlemanWunsch 1. INTRODUCTION Text similarity measures play an increasingly important role in text related research and applications in tasks Nsuch as information retrieval, text classification, document clustering, topic detection, topic tracking, questions generation, question answering, essay scoring, short answer scoring, machine translation, text summarization and others. Finding similarity between words is a fundamental part of text similarity which is then used as a primary stage for sentence, paragraph and document similarities. Words can be similar in two ways lexically and semantically. Words are similar lexically if they have a similar character sequence. Words are similar semantically if they have the same thing, are opposite of each other, used in the same way, used in the same context and one is a type of another. DistanceLexical similarity is introduced in this survey though different String-Based algorithms, Semantic similarity is introduced through Corpus-Based and Knowledge-Based algorithms. String-Based measures operate on string sequences and character composition. A string metric is a metric that measures similarity or dissimilarity (distance) between two text strings for approximate string matching or comparison. Corpus-Based similarity is a semantic similarity measure that determines the similarity between words according to information gained from large corpora. Knowledge-Based similarity is a semantic similarity measure that determines the degree of similarity between words using information derived from semantic networks. The most popular for each type will be presented briefly. This paper is organized as follows: Section two presents String-Based algorithms by partitioning them into two types character-based and term-based measures. Sections three and four introduce Corpus-Based and knowledge-Based algorithms respectively. Samples of combinations between similarity algorithms are introduced in section five and finally section six presents conclusion of the survey.

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A Survey of Text Similarity Approaches

This paper examines the meaning of context in relation to ontology based query expansion and contains a review of query expansion approaches. The various query expansion approaches include relevance feedback, corpus dependent knowledge models and corpus independent knowledge models. Case studies detailing query expansion using domain-specific and domain-independent ontologies are also included. The penultimate section attempts to synthesise the information obtained from the review and provide success factors in using an ontology for query expansion. Finally the area of further research in applying context from an ontology to query expansion within a newswire domain is described.

A review of ontology based query expansion

We promote a new approach for knowledge modelling based on knowledge elicitation from technical documents. It benefits of the increasing amount of available electronic texts and of the maturity of natural language processing tools. The approach defines a framework where the knowledge engineer selects the appropriate tools, combines their use and interprets their results to build up a domain model. The paper presents the method and reports an on-going application to design an ontology of knowledge engineering tools in French.

Revisiting Ontology Design: A Methodology Based on Corpus Analysis

La gamme des produits a base terminologique necessaires pour repondre aux nouveaux besoins en matiere de gestion de connaissances et de documents s'elargit considerablement. Dans cet article, nous defendons l'idee que chaque type d'application passe par la mise au point de produits specifiques a partir de textes et autres ressources du domaine. Nous proposons alors un cadre methodologique unificateur et un ensemble de logiciels dont l'utilisation ciblee facilite le recueil de connaissances et la modelisation de ressources adaptees. Nous evoquons en parallele les problemes fondamentaux qui se posent et, lorsqu'elles existent, les solutions, techniques ou theoriques qui peuvent etre envisagees. Nous nous appuyons pour cela sur trois etudes de cas de construction de ressources terminologiques a partir de textes pour des applications tres differentes.

Construction de ressources terminologiques ou ontologiques à partir de textes : Un cadre unificateur pour trois études de cas

Designed about ten years ago, the TERMINAE method and workbench for ontology engineering from texts have been going on evolving since then. Our investigations integrate the experience gained through its use in industrial and academic projects, the progress of natural language processing as well as the evolution of the ontology engineering. Several new methodological guidelines, such as the reuse of core ontologies, have been added to the method and implemented in the workbench. It has also been modified in order to be compliant to some recent standards such as the OWL knowledge representation.

The paper recalls the terminology engineering principles underlying TERMINAE and comments its originality. Then it presents the kind of conceptual model that is built with this method, and its knowledge representation. The method and the support provided by the workbench are detailed and illustrated with a case-study in law. With regard to the state of the art, TERMINAE is one of the most supervised methods in the trend of ontology learning. This option raises epistemological issues about how language and knowledge can be articulated and the distance that separate formal ontologies from learned conceptual models.

The TERMINAE Method and Platform for Ontology Engineering from Texts

The development of the Semantic Web has provoked an increasing interest in the development of ontologies. There are, however, few mechanisms for guiding users in making informed decisions on which ontology to use under given circumstances. In this paper, we propose a framework for evaluating the quality of ontologies based on the SQuaRE standard for software quality evaluation. This method requires the definition of both a quality model and quality metrics for evaluating the quality of the ontology. The quality model is divided into a series of quality dimensions or charac ter istics, such as structure or functional adequacy, which are organized into subcharacteristics, such as cohesion or tangledness. Thus, each subcharacteristic is evaluated by applying a series of quality metrics, which are automatically measured. Finally, each characteristic is evaluated by combining values of its subcharacteristics. This work also includes the application of this frame work for the evaluation of ontologies in two application domains.

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OQuaRE: A SQuaRE-based Approach for Evaluating the Quality of Ontologies

This paper deals with the use of ontologies for Information Retrieval. Roughly, the proposed approach consists in identifying important concepts in documents using two criterions, co-occurrence and semantic relatedness and then disambiguating them via an external general purpose ontology, namely WordNet. Matching the ontology and a document results in a set of scored concept-senses (nodes) with weighted links. This representation, called semantic core of a document best reveals the semantic content of the document. We regard our approach, of which the first evaluation results are encouraging, as a short but strong step toward the long term goal of Intelligent Indexing and Semantic Retrieval.

Nathalie Aussenac-Gilles

Papers

Revisiting Ontology Design: A Methodology Based on Corpus Analysis

Construction de ressources terminologiques ou ontologiques à partir de textes : Un cadre unificateur pour trois études de cas

The TERMINAE Method and Platform for Ontology Engineering from Texts

OQuaRE: A SQuaRE-based Approach for Evaluating the Quality of Ontologies

Semantic cores for representing documents in IR