Pruning-based identification of domain ontologies

We present a novel approach of extracting a domain ontology from large-scale thesauri. Concepts are identified to be relevant for a domain based on their frequent occurrence in domain texts. The approach allows to bootstrap the ontology engineering process from given legacy thesauri and identifies an initial domain ontology that may easily be refined by experts in a later stage. We present a thorough evaluation of the results obtained in building a biosecurity ontology for the UN FAO AOS project

A Novel Approach to Ontology Management

The term ontology is defined as the explicit specification of a conceptualization. While much of the prior research has focused on technical aspects of ontology management, little attention has been paid to the investigation of issues that limit the widespread use of ontologies and the evaluation of the effectiveness of ontologies in improving task performance. This dissertation addresses this void through the development of approaches to ontology creation, refinement, and evaluation. This study follows a multi-paper model focusing on ontology creation, refinement, and its evaluation. The first study develops and evaluates a method for ontology creation using knowledge available on the Web. The second study develops a methodology for ontology refinement through pruning and empirically evaluates the effectiveness of this method. The third study investigates the impact of an ontology in use case modeling, which is a complex, knowledge intensive organizational task in the context of IS development. The three studies follow the design science research approach, and each builds and evaluates IT artifacts. These studies contribute to knowledge by developing solutions to three important issues in the effective development and use of ontologies

Modélisation de connaissances à partir de textes pour une recherche d'information sémantique

Avec l'avènement d'Internet et des réseaux d'entreprise, les documents numériques ont subi de profondes transformations, tant dans la diversification de leur support (texte, image, son, vidéo), que dans la forte augmentation de leur nombre accessible informatiquement. La Recherche d'Information (RI) a alors pris une importance capitale : l'utilisateur en quête de données répondant à ses besoins veut disposer de logiciels capables d'exploiter les contenus textuels et de trouver automatiquement tout document pertinent pour la requête. Pour comparer selon leur sens requête et documents, la RI sémantique nécessite deux opérations préalables : l'obtention d'un modèle des connaissances manipulées et, grâce à lui, l'indexation sémantique des données textuelles. Dans ce mémoire, nous étudions les modèles de Ressources Termino-Ontologiques (RTO) adaptés à la RI et développons un formalisme qui, contrairement aux approches classiques, décrit explicitement la relation entre termes du lexique et concepts de l'ontologie, tout en respectant le standard OWL-DL. Nous abordons ensuite la problématique de maintenance d'une RTO pour la RI : quand un domaine évolue dans le temps, sa RTO correspondante doit être modifiée en conséquence. L'originalité de notre approche réside dans la mise en parallèle entre maintenance de RTO et indexation sémantique : l'ontographe définit des règles évaluant automatiquement la correction de la RTO en fonction des résultats d'indexation attendus ; appliquées aux documents à indexer, ces règles aident à repérer ceux qui témoignent de la nécessité de maintenance. L'outil présente alors ces documents avec des conseils de modification. Notre dernière contribution inclut notre formalisme de RTO et le cycle de maintenance au sein d'un processus global de RI sémantique. Nous nous intéressons notamment à la comparaison sémantique d'un document à une requête en langue naturelle. Nous proposons une mesure de similarité tenant compte de la proximité taxonomique de deux notions, ainsi que de la manière dont chacune est reliée sémantiquement à d'autres éléments. La pertinence de nos contributions a été principalement mise à l'épreuve par la réalisation et l'utilisation d'un prototype d'outil pour la RI sémantique dans le cadre d'un partenariat avec Actia, une société spécialiste du diagnostic automobile.With the spreading of Internet and local networks, numerical documents have been undergoing deep mutations, mainly due to the diversification of supports (text, image, sound, video) and their high number accessible by computers. Information Retrieval (IR) has thus become crucial: any user of a search engine wants it to be able to process textual contents to find automatically all documents relevant for their query. In order to compare a query with a document, semantic IR needs two prior operations to be carried out: obtaining a model for the handled knowledge and using it to index semantically the textual data. In this thesis, we study Ontological and Terminological Resources (OTR) adapted for IR and we develop a formalism which, unlike classical approaches, explicitly describes the relationship between terms and concepts, while respecting OWL-DL standard. Afterwards, we broach the topic of maintaining an OTR for IR: when a domain evolves in time, its corresponding OTR must be modified accordingly. The originality of our approach lies in the parallel computing of OTR maintenance and semantic indexing: the engineer can define rules which evaluate automatically the correctness of the OTR with respect to the expected indexing results; applied to the documents to be indexed, these rules help to spot the ones which show the necessity of maintaining the OTR. The tool then displays these documents with evolution advice. Our last contribution consists in integrating our OTR formalism and the maintenance cycle into a global semantic IR process. We especially focus on the semantic matching between a document and a keyword based query. We propose a semantic similarity measure which takes into account both the taxonomical proximity of two notions and the way each one is semantically connected to other entities. The relevance of our contributions was mainly tested by the implementation and use of a prototype tool for semantic IR as part of a partnership with ACTIA, a company specialized in automotive diagnosi

J.UCS Special Issue:I-Know 03 - Hot Spots in Knowledge Management

The increasing complexity of both the environment in which companies operate and of their internal workings combined with an increasingly high pressure for innovation make knowledge and its efficient management central to business success today. The management of knowledge is more than directly managing knowledge as a resource. It is more concerned with providing knowledge-friendly environments in which knowledge can flourish and develop. The development of such environments can be addressed from many different perspectives, which makes knowledge management a very interdisciplinary field of research. It concerns human resources management, organizational development and information technology management to mention just the three most important fields. This special issue on hot spots in knowledge management offers the reader a broad overview of the leading edge developments, technologies and applications in knowledge management. The issue serves two distinct purposes: (1) It may help shaping the reader s thinking in the way required for a successful implementation of knowledge management in an organization, (2) it may serve as a stimulus for the reader s research in knowledge management. The following 13 papers fall in four categories: Knowledge Management in Business Knowledge Mining Knowledge Representation Convergence of Knowledge Management with other Domains The first category - knowledge management in business - addresses knowledge management from a more business-oriented perspective. In more detail, the papers of this group address the following topics: Peter Schuett from IBM Stuttgart (Germany) presents in his paper The post-Nonaka Knowledge Management a new generation of knowledge management that can be divided in three categories (1) processes, (2) organization and culture, and (3) information technology. The objective of the paper is to provide solutions for increasing the productivity of knowledge workers through knowledge management. He argues that in order to increase productivity we need to understand the work environment of knowledge workers. To provide guidance, P. Schuett identifies 11 factors which help understanding and improving a knowledge worker s environment. This factors fall in the following three groups work processes, organisation and culture, and information technology. Klaus North and Tina Hornung from the University of Applied Sciences in Wiesbaden (Germany) entitled their paper The Benefits of Knowledge Management - Results from the German award _Knowledge Manager 2002_. Based on the evaluation of almost 40 companies the authors present which added-value and benefit knowledge management can generate. The benefits are grouped in the following five perspectives: learn and growth, business processes, customer satisfaction, financial results, and employee satisfaction. The results of the study revealed that knowledge management can generate the highest benefit in business processes (e.g. acceleration and higher transparency), customer satisfaction (e.g. better response times), and employee satisfaction (e.g. improved team work and increased motivation). The paper Managing Operation Knowledge for the Metal Industry written by Sheng-Tun Li and Huang-Chih Hseih from the National Kaohsiung First University of Science Technology (Taiwan) presents a three-stage life cycle for the ontology design. The application of the resulting ontology in a metal industry company proves the effectiveness and efficiency of their approach. In their paper Filters in the Strategy Formulation Process Leena Ilmola and Anna Kotsalo-Mustonen from Helsinki University of Technology (Finland) present a new software tool supporting strategy formulation processes. Based on three different types of filters that hinder effective knowledge flows in companies a software tool is introduced that helps overcome these filters. Matteo Bonifacio and Alessandra Molani from University of Trento (Italy) are the authors of the paper The Richness of Diversity in Knowledge Creation: an Interdisciplinary Overview. They propose theoretical, practical and technological arguments supporting a distributed approach to knowledge management. Knowledge diversity in theory, practice, and technology is considered an important source of value for the approach of the authors. The last paper in this category has the title SCBS Social Capital Benchmarking System Profiting from Social Capital when Building Network Organisations. Jos_ Mar_a Viedma from Polytechnic University of Catalonia (Spain) argues that the competitive advantage of a company does not only rely on a company s internal intellectual capital but also on the external intellectual capital of other companies, organisations and institutions. The author presents a social capital benchmarking system which serves as a new management method and a new management tool which identifies, audits and benchmarks the resources and capabilities existing in cluster organisations. Knowledge mining including retrieval, classification and discovery constitutes another main stream in knowledge management. The papers of this second category address the following topics: The paper Unified Access to Heterogeneous Audiovisual Archives is written by Y. Avrithis, G. Stamou, and M. Wallace from National Technical University of Athens (Greece), F. Marques, P. Salembier, X Giro from Technical University of Catalonia (Spain) and W. Haas, H. Vallant, M. Zufferey from Joanneum Research (Austria). The authors present an integrated information system that offers enhanced search and retrieval capabilities to users of heterogeneous digital audio-visual archives. The idea is to extract semantic information from audio/video and text data taking into account context information of a user. Pruning-based Identification of Domain Ontologies is the title of a paper co-authored by Raphael Volz, Rudi Studer, and Alexander Maedche from FZI Research Center for Information Technologies (Germany) and Boris Lauser from FAO of the UN (Italy). This paper introduces a new pruning-based approach of extracting a domain ontology from large-scale thesauri. In this context pruning presents a completely automatic bootstrapping approach for ontology development. The aim of pruning is to automatically extract from an existing vocabulary a subset of the conceptualization which is relevant to the target domain. In a later stage, the automatically identified initial domain ontology can easily be refined by experts. Christian Biemann, Uwe Quasthoff, Karsten Boehm from University of Leipzig (Germany) and Christian Wolff from Chemnitz University of Technology (Germany) are the authors of the paper Automatic Discovery and Aggregation of Compound Names for the Use in Knowledge Representations. They argue that the treatment of multiword terms as single semantic entities is an inherent problem of automatic acquisition of information structures (e.g. Topic Maps). As a solution to this problem the authors present a method for learning multiword terms from large text corpora. The following two papers belong to an evergreen category in knowledge management, namely knowledge representation. Kai Mertins, Peter Heisig, and Kay Alwert from Fraunhofer IPK (Germany) are the authors of the paper Process Oriented Knowledge Structuring. The paper presents three different types of knowledge structures and their visualization (e.g., Topic Maps, Knowledge Navigator) which support the structuring and maintenance of complex knowledge bases. Towards the Semantic Grid: Putting Knowledge to Work in Design Optimisation is a paper authored by Fang Tao, Liming Chen, Nigel Shadbolt, Graeme Pound and Simon Cox from the University of Southampton (UK). They present a knowledge-based approach which uses existing sources to acquire knowledge needed for engineering design search and optimization. In order to reuse this knowledge and to provide guidance at knowledge intensive points, a knowledge advisor is proposed. This advisor gives a context-aware critique to guide users through effective operations of building domain workflows. The last paper category - convergence of knowledge management with other domains - reflects signals indicating that research of other domains converges with research in knowledge management. Such converging fields include problem solving, eLearning, linguistics etc. The paper Knowledge Management for Computational Problem Solving written by D.T. Lee, G.C. Lee and Y.W. Huang from Academia Sinica (Taiwan) argues that algorithmic research is an established knowledge engineering process allowing researchers to identify significant problems, to better understand existing approaches and to obtain new, effective and efficient solutions. To support researchers in this process a problem-centred collaborative knowledge management architecture associated with computational problem solving is presented. Lilia Efimova and Janine Swaak from the Telematica Instituut (The Netherlands) discuss in their paper entitled Converging Knowledge Management, Training and e-Learning: Scenarios to Make it Work the added value of using knowledge management methods to support human resource learning management efforts and vice versa of using human resource training instruments to support knowledge management. Examples for existing practices of joint work include linking communities of practice and formal learning programmes or fostering the cooperation between a Chief Knowledge Officer and Human Resource teams. I hope that the broad variety of 13 contributions provides the reader with a comprehensive overview of the most intriguing hot spots in knowledge management in 2003. Graz, July 2003 Klaus Tochtermann Know-Center, Gra