Hybrid fuzzy multi-objective particle swarm optimization for taxonomy extraction

Ontology learning refers to an automatic extraction of ontology to produce the ontology learning layer cake which consists of five kinds of output: terms, concepts, taxonomy relations, non-taxonomy relations and axioms. Term extraction is a prerequisite for all aspects of ontology learning. It is the automatic mining of complete terms from the input document. Another important part of ontology is taxonomy, or the hierarchy of concepts. It presents a tree view of the ontology and shows the inheritance between subconcepts and superconcepts. In this research, two methods were proposed for improving the performance of the extraction result. The first method uses particle swarm optimization in order to optimize the weights of features. The advantage of particle swarm optimization is that it can calculate and adjust the weight of each feature according to the appropriate value, and here it is used to improve the performance of term and taxonomy extraction. The second method uses a hybrid technique that uses multi-objective particle swarm optimization and fuzzy systems that ensures that the membership functions and fuzzy system rule sets are optimized. The advantage of using a fuzzy system is that the imprecise and uncertain values of feature weights can be tolerated during the extraction process. This method is used to improve the performance of taxonomy extraction. In the term extraction experiment, five extracted features were used for each term from the document. These features were represented by feature vectors consisting of domain relevance, domain consensus, term cohesion, first occurrence and length of noun phrase. For taxonomy extraction, matching Hearst lexico-syntactic patterns in documents and the web, and hypernym information form WordNet were used as the features that represent each pair of terms from the texts. These two proposed methods are evaluated using a dataset that contains documents about tourism. For term extraction, the proposed method is compared with benchmark algorithms such as Term Frequency Inverse Document Frequency, Weirdness, Glossary Extraction and Term Extractor, using the precision performance evaluation measurement. For taxonomy extraction, the proposed methods are compared with benchmark methods of Feature-based and weighting by Support Vector Machine using the f-measure, precision and recall performance evaluation measurements. For the first method, the experiment results concluded that implementing particle swarm optimization in order to optimize the feature weights in terms and taxonomy extraction leads to improved accuracy of extraction result compared to the benchmark algorithms. For the second method, the results concluded that the hybrid technique that uses multi-objective particle swarm optimization and fuzzy systems leads to improved performance of taxonomy extraction results when compared to the benchmark methods, while adjusting the fuzzy membership function and keeping the number of fuzzy rules to a minimum number with a high degree of accuracy

Uma visão geral sobre ontologias: pesquisa sobre definições, tipos, aplicações, métodos de avaliação e de construção

Os estudos sobre a organização da informação tem recebido cada vez mais importância à medida que o número crescente de fontes de dados disponíveis dificulta a recuperação da informação. Nos últimos anos, vários trabalhos têm destacado o uso de ontologias como alternativa para a organização da informação. Encontram-se na literatura abordagens das mais variadas sobre o assunto. Esse artigo objetiva proporcionar uma visão geral sobre o estado-da-arte no estudo de ontologias. Apresentam-se definições para o termo, uma breve discussão sobre seu significado, tipos de ontologias, propostas para aplicações em diferentes domínios de conhecimento e propostas para a construção de ontologias (metodologias, ferramentas e linguagens)

Développement récents en matière de conception, de maintenance et d’utilisation des ontologies

Le présent article offre une synthèse des développements récents survenus dans le domaine de l’ingénierie ontologique: les bases théoriques, les ontologies les plus connues, les méthodologies et les environnements logiciels disponibles pour la création d’ontologies, ainsi que l’utilisation d’ontologies dans des applications à des fins commerciales et de recherche

Methodological guidelines for reusing general ontologies

Currently, there is a great deal of well-founded explicit knowledge formalizing general notions, such as time concepts and the part_of relation. Yet, it is often the case that instead of reusing ontologies that implement such notions (the so-called general ontologies), engineers create procedural programs that implicitly implement this knowledge. They do not save time and code by reusing explicit knowledge, and devote effort to solve problems that other people have already adequately solved. Consequently, we have developed a methodology that helps engineers to: (a) identify the type of general ontology to be reused; (b) find out which axioms and definitions should be reused; (c) make a decision, using formal concept analysis, on what general ontology is going to be reused; and (d) adapt and integrate the selected general ontology in the domain ontology to be developed. To illustrate our approach we have employed use-cases. For each use case, we provide a set of heuristics with examples. Each of these heuristics has been tested in either OWL or Prolog. Our methodology has been applied to develop a pharmaceutical product ontology. Additionally, we have carried out a controlled experiment with graduated students doing a MCs in Artificial Intelligence. This experiment has yielded some interesting findings concerning what kind of features the future extensions of the methodology should have

Object-oriented knowledge acquisition: Integrating construction of and reasoning in object-oriented knowledge bases

Päivikki Parpola presents in this research report the SeSKA (seamless structured knowledge acquisition) methodology, integrating phases of knowledge acquisition (KA) through seamless transformations between object-oriented (OO) models. This attacks the problem of disintegration, or the gap between phases. The methodology is accompanied by presentation of the SOOKAT (structured object-oriented knowledge acquisition) tool supporting it. SeSKA and SOOKAT extend the KA process to constructing knowledge bases by instantiating a series of models for inferencing. The models are constructed in SOOKAT utilizing metaobject protocols. Inferences performed in instantiations of OO models are guided by control objects (CO). Messages are sent between COs and components of the inference structure. A specific CO, possibly using subordinate COs, can be specified for each inference strategy. There exists a mutual CO for forward and backward chaining that can also be used when reasoning according to protocols. In addition, COs for problem-solving methods (PSMs), such as cover-and-differentiate or propose-and-revise, can be used.Three example applications are used for demonstrating the properties of the SeSKA methodology and SOOKAT, that is, a mineral classification "toy application", Sisyphus III rock classification and dietary management of multiple sclerosis.Mechanisms for importing problem-solving methods (PSMs) over the Internet, as well as for generating specific control objects (COs) for them, remain open to further development.  Päivikki Parpola (1965-2015) was a Ph.D. student at Aalto University. Her research interests concerned knowledge acquisition and presentation, development and reasoning in expert systems for different application fields, using the object-oriented paradigm. She received her M.Sc. in 1988 and Lic.Phil. in 1995 from the Department of Computer Science at the University of Helsinki. Her M.Sc. thesis concerned forming a formal grammar based on text samples of natural language or unknown writing. Research presented in her Lic.Phil. thesis continued in her Ph.D. studies. She worked with Nokia Research Center from 1987 to 1993. In addition to her thesis, she published multiple international and domestic conference papers and articles as well as contributed in European Union research project publications

Theory-based knowledge acquisition for ontology development

This thesis concerns the problem of knowledge acquisition in ontology development. Knowledge acquisition is essential for developing useful ontologies but it is a complex and error-prone task. When capturing specific knowledge about a particular domain of interest, the problem of knowledge acquisition occurs due to linguistic, cognitive, modelling, and methodical difficulties. For overcoming these four difficulties, this research proposes a theory-based knowledge acquisition method. By studying the knowledge base, basic terms and concepts in the areas of ontology, ontology development, and knowledge acquisition are defined. A theoretical analysis of knowledge acquisition identifies linguistic, cognitive, modelling, and methodical difficulties, for which a survey of 15 domain ontologies provides further empirical evidence. A review of existing knowledge acquisition approaches shows their insufficiencies for reducing the problem of knowledge acquisition. As the underpinning example, a description of the domain of transport chains is provided. Correspondingly, a theory in business economics, i.e. the Contingency Approach, is selected. This theory provides the key constructs, relationships, and dependencies that can guide knowledge acquisition in the business domain and, thus, theoretically substantiate knowledge acquisition. Method construction uses an approach from the field of Method Engineering, which defines how to develop a tailored method with respect to specific requirements on method design, functionality, components, and the underlying assumptions. The development of the method for theory-based knowledge acquisition covers the specification of the (method and outcome) metamodel, activity model, outcomes, roles, and techniques. The evaluation comprises two descriptive approaches to demonstrate the proposed methods utility. First, a criteria-based approach evaluates the method with respect to design-related, functional, and component-related requirements. Second, a scenario-based evaluation applies the method within a scenario from the domain of intermodal transport chains for acquiring knowledge to build a domain ontology. The contribution of this research is a theory-based knowledge acquisition method for ontology development. The application and usefulness of this method is demonstrated for a particular domain (transport chains) and uses a particular theory of business economics (the Contingency Approach).Diese Arbeit befasst sich mit dem Problem der Wissensakquisition in der Ontologieentwicklung. Die Wissensakquisition ist von zentraler Bedeutung für die Entwicklung nützlicher Ontologien. Sie stellt jedoch eine komplexe und fehleranfällige Aufgabe dar. Bei der Akquisition von spezifischem Wissen über eine bestimmte Domäne tritt das Problem der Wissensakquisition in Form von linguistischen, kognitiven, modellbildenden und methodischen Schwierigkeiten auf. Um diese vier Schwierigkeiten zu überwinden entwickelt diese Arbeit eine Methode für die theorie-basierte Wissensakquisition. Die Untersuchung des Stands der Forschung beginnt mit der Definition der grundlegenden Begriffe und Konzepte aus den Bereichen Ontologien, Ontologieentwicklung und Wissensakquisition sowie der Darlegung dazugehöriger Annahmen. Nachfolgend wird das Problem der Wissensakquisition analysiert und die identifizierten linguistischen, kognitiven, modellbildenden und methodischen Schwierigkeiten charakterisiert. Die Bedeutung dieser Schwierigkeiten wird durch eine empirische Untersuchung von 15 Ontologien für die Domäne Transportlogistik nachgewiesen. Die Offenlegung der Forschungslücke erfolgt durch die Analyse bestehender Ansätze zur Wissensakquisition und deren Bewertung hinsichtlich des Problems der Wissensakquisition. Basierend auf der identifizierten Forschungslücke wird die Theorie-basierte Wissensakquisition für die Ontologieentwicklung als Lösungsansatz vorgeschlagen. Die Erhebung der Anforderungen als Basis der Gestaltung des Lösungsansatzes wird anhand des Beispiels der Domäne der Transportlogistik vorgenommen. Hierfür werden zunächst die charakteristischen Merkmale der Transportlogistik herausgearbeitet. Auf Grundlage dessen erfolgt die Untersuchung betriebswirtschaftlicher Theorien, d.h. von Organisationstheorien, die Modelle mit Wissen über die betrachtete Domäne in Form der zentralen Konstrukte, (Wechsel-)Beziehungen und Abhängigkeiten bereitstellen. Als Ergebnis der Analyse werden der Situative Ansatz und das ihm zugrundeliegende Modell ausgewählt. Dieses Modell dient als Basis für die Ableitung der Anforderungen und ist somit als Fundament der Theorie-basierten Wissensakquisition. Die Gestaltung der Methode zur Theorie-basierten Wissensakquisition beruht auf einem Konstruktionsansatz aus dem Bereich des Method Engineering. Dieser Ansatz erlaubt es, auf bestimmte Zwecke zugeschnitte Methoden entsprechend den Anforderungen und den dazugehörigen Annahmen zu gestalten. Die Definition der Anforderungen umfasst das Design, die Funktionalität und die Komponenten der Methode. Die Spezifikation der Theorie-basierten Wissens-akquisitionsmethode beinhaltet zwei Metamodelle als Grundlage zur Definition der Methode und deren Ergebnissen, ein Aktivitätsmodell zur Festlegung der Aktivitäten, die Definition der Ergebnisse dieser Aktivitäten sowie die zur Ausführung und Unterstützung der Wissensakquisition notwendigen Rollen und Techniken. Zur Evaluation der Nützlichkeit der entwickelten Wissensakquisitionsmethode werden die in Frage kommenden Evaluationsansätze auf Eignung und Anwendbarkeit untersucht. Als Ergebnis werden zwei deskriptive Ansätze merkmals-basierte und szenario-basierte Evaluation ausgewählt. Die merkmals-basierte Evaluation erfolgt im Hinblick auf Kriterien der gestaltungsorientierten Wirtschaftsinformatik-Forschung sowie den Anforderungen an das Design, der Funktionalität und Komponenten der Methode. In der szenario-basierten Evaluation wird ein Szenario der intermodalen Transportlogistik definiert, auf welches die Wissensakquisitionsmethode für die Entwicklung der Domänen-Ontologie angewendet wird. Der Beitrag der vorliegenden Arbeit ist eine Methode zur Theorie-basierten Wissens-akquisition für die Ontologieentwicklung mit dem Ziel, das Problem der Wissensakquisition, d.h. den damit einhergehenden linguistischen, kognitiven, modellbildenden und methodischen Schwierigkeiten entgegenzuwirken. Die Gestaltung und Evaluation dieser Methode erfolgt anhand der Domäne Transportlogistik und des Situativen Ansatzes als betriebs-wirtschaftliche Theorie. Die Arbeit zeigt die Nützlichkeit der Verwendung betriebswirtschaftlicher Theorien zur Verbesserung der Wissensakquisition für die Ontologieentwicklung