An ontology is a formal, explicit specification of a shared conceptualization. Formalizing an ontology for a domain is a tedious and cumbersome process. It is constrained by the knowledge acquisition bottleneck (KAB). There exists a large number of text corpora that can be used for classification in order to create ontologies with the intention to provide better support for the intended parties. In our research we provide a novel unsupervised bottom-up ontology generation method. This method is based on lexico-semantic structures and Bayesian reasoning to expedite the ontology generation process. This process also provides evidence to domain experts to build ontologies based on top-down approaches.

Ontology learning supports ontology engineers in the complex task of creating an ontology. Updating ontologies at regular intervals greatly increases the need for expensive expert contribution. This naturally leads to endeavors to automate the process wherever applicable. This paper presents a model for automated ontology learning and a prototype which demonstrates the feasibility of the proposed approach in learning lightweight domain ontologies. The system learns ontologies from heterogeneous sources periodically and delegates all evaluation processes, eg. the verification of new concept candidates, to a crowdsourcing framework which currently relies on Games with a Purpose. Furthermore, we sketch ontology evolution experiments to trace trends and patterns facilitated by the system.(authors' abstract)

Spreading activation is a common method for searching semantic or neural networks, it iteratively propagates activation for one or more sources through a network { a process that is computationally intensive. Spectral association is a recent technique to approximate spreading activation in one go, and therefore provides very fast computation of activation levels. In this paper we evaluate the characteristics of spectral association as replacement for classic spreading activation in the domain of ontology learning. The evaluation focuses on run-time performance measures of our implementation of both methods for various network sizes. Furthermore, we investigate differences in output, i.e. the resulting ontologies, between spreading activation and spectral association. The experiments confirm an excessive speedup in the computation of activation levels, and also a fast calculation of the spectral association operator if using a variant we called brute force. The paper concludes with pros and cons and usage recommendations for the methods. (authors' abstract)

Ontology可用來組織、管理與分享知識，Ontology Engineering是一種建構Ontology的過程，建構的過程中，多數的工作需要人費時費力地去完成，因此利用機器來輔助Ontology Engineering成了一門重要的課題。使用Knowledge Discovery的方法協助Ontology Engineering建構Ontology的過程，稱為Ontology Learning，本論文中提出的Ontology Learning方法為分析使用者在搜尋引擎下關鍵字查詢時的行為，加上利用與查詢關鍵字有關的網頁資訊，以輔助建構Ontology。本論文中的Ontology由使用者所查詢的關鍵字組成，我們要learning的，則是這些關鍵字彼此之間的關係，其中有上義詞、下義詞與同義詞等等，因此，自動尋找關鍵字彼此之間的關係以輔助建構Ontology，即為我們提出本論文的目的。除此之外，本論文亦實作了完整的Ontology Learning系統，從一開始使用者查詢記錄的蒐集，關鍵字擷取與分析，關鍵字之間的關係判定，直到最後Ontology的產生，都將由系統自動完成。 / Ontology can be used to organize, manage and share knowledge. Ontology Engineering is the process of constructing Ontology. However, it’s usually a time-consuming and error-prone task. Thus, utilizing methods of Knowledge Discovery to help Ontology Engineering is called Ontology Learning. In this thesis, Ontology Learning process is done by using those pages related query terms and analyzing the querying behavior of users on search engines. The Ontology is organized by user query terms and relations among them. These relations we define are hyperonomy, hyponomy, synonymy and et al. Our goal of this thesis is to automatically learn the correct relations among these query terms. Besides, we implemented the complete system platform for Ontology Learning. The system can automatically collect logs, extract and analyze query keywords, and produce the final Ontology.

Na Grécia antiga, Aristóteles (384-322 aC) reuniu todo conhecimento de sua época para criar a Enciclopédia. Na última década surgiu a Web Semântica representando o conhecimento organizado em ontologias. Na Engenharia de Ontologias, o Aprendizado de Ontologias reúne os processos automáticos ou semi-automáticos de aquisição de conhecimento a partir de recursos existentes. Por outro lado, a Engenharia de Software faz uso de vários padrões para permitir a interoperabilidade entre diferentes ferramentas como os criados pelo Object Management Group (OMG) Model Driven Architecture (MDA), Meta Object Facility (MOF), Ontology Definition Metamodel (ODM) e XML Metadata Interchange (XMI). Já o World Wide Web Consortium (W3C) disponibilizou uma arquitetura em camadas com destaque para a Ontology Web Language (OWL). Este trabalho propõe um framework para reunir estes conceitos fundamentado no ODM, no modelo OWL, na correspondência entre metamodelos, nos requisitos de participação para as ferramentas e na seqüência de atividades a serem aplicadas até obter uma representação inicial da ontologia. Uma prova de conceito no domínio da Educação foi desenvolvida para testar esta proposta. / In ancient Greece, Aristotle (384-322 BCE) endeavored to collect all the existing science in his world to create the Encyclopedia. In the last decade, Berners-Lee and collaborators idealized the Web as a structured repository, observing an organization they called Semantic Web. Usually, domain knowledge is organized in ontologies. As a consequence, a great number of researchers are working on method and technique to build ontologies in Ontology Engineering. Ontology Learning meets automatic or semi-automatic processes which perform knowledge acquisition from existing resources. On the other hand, software engineering uses a collection of theories, methodologies and techniques to support information abstraction and several standards have been used, allowing interoperability and different tools promoted by the Object Management Group (OMG) Model Driven Architecture (MDA), Meta Object Facility (MOF), Ontology Definition Metamodel (ODM) and XML Metadata Interchange (XMI). The World Wide Web Consortium (W3C) released architecture in layers for implementing the Semantic Web with emphasis on the Web Ontology Language (OWL). A framework was developed to combine these concepts based on ODM, on OWL model, the correlation between metamodels, the requirements for the tools to participate; in it, the steps sequence was defined to be applied until initial representations of ontology were obtained. A proof of concept in the Education domain was developed to test this proposal.

The work gives overview of its three main topics: semantic web, information extraction and ontology learning. A method for identification relevant information on web pages is described and experimentally tested on pages of companies offering products and services. The method is based on analysis of a sample web pages and their position in the Open Directory catalogue. Furthermore, a modfication of association rules mining algorithm is proposed and experimentally tested. In addition to an identification of a relation between ontology concepts, it suggest possible naming of the relation.

Na Grécia antiga, Aristóteles (384-322 aC) reuniu todo conhecimento de sua época para criar a Enciclopédia. Na última década surgiu a Web Semântica representando o conhecimento organizado em ontologias. Na Engenharia de Ontologias, o Aprendizado de Ontologias reúne os processos automáticos ou semi-automáticos de aquisição de conhecimento a partir de recursos existentes. Por outro lado, a Engenharia de Software faz uso de vários padrões para permitir a interoperabilidade entre diferentes ferramentas como os criados pelo Object Management Group (OMG) Model Driven Architecture (MDA), Meta Object Facility (MOF), Ontology Definition Metamodel (ODM) e XML Metadata Interchange (XMI). Já o World Wide Web Consortium (W3C) disponibilizou uma arquitetura em camadas com destaque para a Ontology Web Language (OWL). Este trabalho propõe um framework para reunir estes conceitos fundamentado no ODM, no modelo OWL, na correspondência entre metamodelos, nos requisitos de participação para as ferramentas e na seqüência de atividades a serem aplicadas até obter uma representação inicial da ontologia. Uma prova de conceito no domínio da Educação foi desenvolvida para testar esta proposta. / In ancient Greece, Aristotle (384-322 BCE) endeavored to collect all the existing science in his world to create the Encyclopedia. In the last decade, Berners-Lee and collaborators idealized the Web as a structured repository, observing an organization they called Semantic Web. Usually, domain knowledge is organized in ontologies. As a consequence, a great number of researchers are working on method and technique to build ontologies in Ontology Engineering. Ontology Learning meets automatic or semi-automatic processes which perform knowledge acquisition from existing resources. On the other hand, software engineering uses a collection of theories, methodologies and techniques to support information abstraction and several standards have been used, allowing interoperability and different tools promoted by the Object Management Group (OMG) Model Driven Architecture (MDA), Meta Object Facility (MOF), Ontology Definition Metamodel (ODM) and XML Metadata Interchange (XMI). The World Wide Web Consortium (W3C) released architecture in layers for implementing the Semantic Web with emphasis on the Web Ontology Language (OWL). A framework was developed to combine these concepts based on ODM, on OWL model, the correlation between metamodels, the requirements for the tools to participate; in it, the steps sequence was defined to be applied until initial representations of ontology were obtained. A proof of concept in the Education domain was developed to test this proposal.

An ontology is a knowledge-representation structure, where words, terms or concepts are defined by their mutual hierarchical relations. Ontologies are becoming ever more prevalent in the world of natural language processing, where we currently see a tendency towards using semantics for solving a variety of tasks, particularly tasks related to information access. Ontologies, taxonomies and thesauri (all related notions) are also used in various variants by humans, to standardize business transactions or for finding conceptual relations between terms in, e.g., the medical domain. The acquisition of machine-readable, domain-specific semantic knowledge is time consuming and prone to inconsistencies. The field of ontology learning therefore provides tools for automating the construction of domain ontologies (ontologies describing the entities and relations within a particular field of interest), by analyzing large quantities of domain-specific texts. This thesis studies three main topics within the field of ontology learning. First, we examine which sources of information are useful within an ontology learning system and how the information sources can be combined effectively. Secondly, we do this with a special focus on cross-language text collections, to see if we can learn more from studying several languages at once, than we can from a single-language text collection. Finally, we investigate new approaches to formal and automatic evaluation of the quality of a learned ontology. We demonstrate how to combine information sources from different languages and use them to train automatic classifiers to recognize lexico-semantic relations. The cross-language data is shown to have a positive effect on the quality of the learned ontologies. We also give theoretical and experimental results, showing that our ontology evaluation method is a good complement to and in some aspects improves on the evaluation measures in use today. / För att köpa boken skicka en beställning till exp@ling.su.se/ To order the book send an e-mail to exp@ling.su.se

With the proliferation of electronic commerce and knowledge economy environments, both persons and organizations increasingly have generated and consumed large amounts of online information, typically available as textual documents. To manage this rapid growth of the number of textual documents, people often use categories or folders to organize their documents. These document grouping behaviors are intentional acts that reflect the persons¡¦ (or organizations¡¦) preferences with regard to semantic coherency, or relevant groupings between subjects. For this thesis, we design and implement an ontology-based personalized document clustering (OnPEC) technique by incorporating both an individual user¡¦s partial clustering and an ontology into the document clustering process. Our use of a target user¡¦s partial clustering supports the personalization of document categorization, whereas our use of the ontology turns document clustering from a feature-based to a concept-based approach. In addition, we combine two hierarchical agglomerative clustering (HAC) approaches (i.e., pre-cluster-based and atomic-based) in our proposed OnPEC technique. Using the clustering effectiveness achieved by a traditional content-based document clustering technique and previously proposed feature-based document clustering (PEC) techniques as performance benchmarks, we find that use of partial clusters improves document clustering effectiveness, as measured by cluster precision and cluster recall. Moreover, for both OnPEC and PEC techniques, the clustering effectiveness of pre-cluster-based HAC methods greatly outperforms that of atomic-based HAC methods.

Observations of textual document management by individuals and organizations have suggested the popularity of using categories to organize, archive and access documents. The adequacy of an existing category understandably may diminish as it includes influxes of new documents over time or retains only a part of existing documents, bringing about significant changes to its content. Thus, the existing document categories have to be evolved over time as new documents are acquired. Following an evolution-based approach for document-category management, this dissertation extends Category Evolution (CE) technique by addressing its inherent limitations. The proposed technique (namely, CE2) automatically re-organizes document categories while taking into account those previously established. Furthermore, we propose the Ontology-based Category Evolution technique (namely, ONCE) to overcome the problems of word mismatch and ambiguity encountered by the lexicon-based category evolution approach (e.g., CE and CE2). Facilitated by a domain ontology, ONCE can evolve document categories on the conceptual rather the lexical level. Finally, this dissertation further considers the evolution of category hierarchy and proposes Category Hierarchy Evolution technique (CHE) and Ontology-based Category Hierarchy Evolution technique (OCHE) to evolve from an existing category hierarchy. We empirically evaluate the effectiveness of our proposed CE2, ONCE, CHE, and OCHE in different category evolution scenarios, respectively. Our analysis results show CE2 to be more effective than CE and the category discovery approach (specifically, HAC). The ontology-based category evolution approach, ONCE, shows its advantage over CE2 which represents the lexicon-based approach. Finally, the effectiveness attained by CHE and OCHE are satisfactory; and similarly, the ontology-based approach, OCHE, also outperforms the lexicon-based one. This dissertation has contributed to the text mining, document management, and ontology learning research and practice.