Showing papers on "Suggested Upper Merged Ontology published in 2009"

What Is an Ontology

Abstract: The word “ontology” is used with different senses in different communities The most radical difference is perhaps between the philosophical sense, which has of course a well-established tradition, and the computational sense, which emerged in the recent years in the knowledge engineering community, starting from an early informal definition of (computational) ontologies as “explicit specifications of conceptualizations” In this paper we shall revisit the previous attempts to clarify and formalize such original definition, providing a detailed account of the notions of conceptualization and explicit specification, while discussing at the same time the importance of shared explicit specifications

OWL 2 Web Ontology Language: Profiles

Abstract: The OWL 2 Web Ontology Language, informally OWL 2, is an ontology language for the Semantic Web with formally defined meaning. OWL 2 ontologies provide classes, properties, individuals, and data values and are stored as Semantic Web documents. OWL 2 ontologies can be used along with information written in RDF, OWL 2 Web Ontology LanguageProfiles W3C Working Draft 21 April 2009 Page 1 of 53 http://www.w3.org/TR/2009/WD-owl2-profiles-20090421/ and OWL 2 ontologies themselves are primarily exchanged as RDF documents. The OWL 2 Document Overview describes the overall state of OWL 2, and should be read before other OWL 2 documents. This document provides a specification of several profiles of OWL 2 which can be more simply and/or efficiently implemented. In logic, profiles are often called fragments. Most profiles are defined by placing restrictions on the structure of OWL 2 ontologies. These restrictions have been specified by modifying the productions of the functional-style syntax. Status of this Document

Ontology Design Patterns

Abstract: Computational ontologies in the context of information systems are artifacts that encode a description of some world, for some purpose. Under the assumption that there exist classes of problems that can be solved by applying common solutions (as it has been experienced in software engineering), we envision small, task-oriented ontologies with explicit documentation of design rationales. In this chapter, we describe components called Ontology Design Patterns (OP), and methods that support pattern-based ontology design.

Ontology and the Lexicon

Abstract: Ontologies and lexicons enjoy a complex relationship. Although words denote concepts and concepts make up ontologies, a lexicon is at best an ersatz ontology: there is no clear mapping between the words and word relationships that it contains and the concepts and concept relationships in an ontology. The reasons for this include the following: Word senses overlap in complex ways; many concepts are not lexicalized in some or all languages; and languages make semantic distinctions that are not ontological. Nonetheless, a lexicon can sometimes be the basis for the development of a practical ontology.

Ontology Engineering Methodology

Abstract: In this chapter we present a methodology for introducing and maintaining ontology based knowledge management applications into enterprises with a focus on Knowledge Processes and Knowledge Meta Processes. While the former process circles around the usage of ontologies, the latter process guides their initial set up.We illustrate our methodology by an example from a case study on skills management. The methodology serves as a scaffold for Part B “Ontology Engineering” of the handbook. It shows where more specific concerns of ontology engineering find their place and how they are related in the overall process.

How to Write and Use the Ontology Requirements Specification Document

Abstract: The goal of the ontology requirements specification activity is to state why the ontology is being built, what its intended uses are, who the end-users are, and which requirements the ontology should fulfill. The novelty of this paper lies in the systematization of the ontology requirements specification activity since the paper proposes detailed methodological guidelines for specifying ontology requirements efficiently. These guidelines will help ontology engineers to capture ontology requirements and produce the ontology requirements specification document (ORSD). The ORSD will play a key role during the ontology development process because it facilitates, among other activities, (1) the search and reuse of existing knowledge-aware resources with the aim of re-engineering them into ontologies, (2) the search and reuse of existing ontological resources (ontologies, ontology modules, ontology statements as well as ontology design patterns), and (3) the verification of the ontology along the ontology development. In parallel to the guidelines, we present the ORSD that resulted from the ontology requirements specification activity within the SEEMP project, and how this document facilitated not only the reuse of existing knowledge-aware resources but also the verification of the SEEMP ontologies. Moreover, we present some use cases in which the methodological guidelines proposed here were applied.

eXtreme design with content ontology design patterns

Abstract: In this paper, we present eXtreme Design with Content Ontology Design Patterns (XD): a collaborative, incremental, iterative method for pattern-based ontology design. We also describe the first version of a supporting tool that has been implemented and is available as a plugin for the NeOn Toolkit. XD is defined in the context of a general approach to ontology design based on patterns, which is also briefly introduce in this work.

LKIF Core: Principled Ontology Development for the Legal Domain

Abstract: In this paper we describe a legal core ontology that is part of the Legal Knowledge Interchange Format: a knowledge representation formalism that enables the translation of legal knowledge bases written in different representation formats and formalisms A legal (core) ontology can play an important role in the translation of existing legal knowledge bases to other representation formats, in particular as the basis for articulate knowledge serving This requires that the ontology has a firm grounding in commonsense and is developed in a principled manner We describe the theory and methodology underlying the LKIF core ontology, compare it with other ontologies, introduce the concepts it defines, and discuss its use in the formalisation of an EU directive

Ontology Based Opinion Mining for Movie Reviews

Abstract: Ontology itself is an explicitly defined reference model of application domains with the purpose of improving information consistency and knowledge sharing. It describes the semantics of a domain in both human-understandable and computer-processable way. Motivated by its success in the area of Information Extraction (IE), we propose an ontology-based approach for opinion mining. In general, opinion mining is quite context-sensitive, and, at a coarser granularity, quite domain dependent. This paper introduces a fine-grain approach for opinion mining, which uses the ontology structure as an essential part of the feature extraction process, by taking account the relations between concepts. The experiment result shows the benefits of exploiting ontology structure to opinion mining.

Experiments on pattern-based ontology design

Abstract: This paper addresses the evaluation of pattern-based ontology design through experiments. An initial method for reuse of content ontology design patterns (Content ODPs) was used by the participants during the experiments. Hypotheses considered include the usefulness of Content ODPs for ontology developers, and we additionally study in what respects they are useful and what open issues remain. The main positive conclusions when using Content ODPs include: ontology developers perceived them as useful, ontology quality is improved, coverage of the task increases, usability is improved, and common modelling mistakes can be avoided.

Reasoning about sensors and compositions

Abstract: This paper discusses an OWL ontology for specifying sensors. The ontology is intended as the basis for the semantic representation of sensors and as the formal description for reasoning about sensors and observations. The paper describes the ontology, presents two example sensor descriptions and shows how standard reasoning and querying techniques can be used to perform tasks including classification and composition. In conjunction with the technical material the trade-offs required to express complex material in OWL is also discussed.

Design of product ontology architecture for collaborative enterprises

Abstract: As enterprises are subject to cope with frequently changing business environment, enterprises should integrate value chains such as supply chain and design chain. Sharing product information must precede for the integration. However, because most of the participants have different business experience and business domains, interoperability of product information among enterprises should be guaranteed for collaboration. To achieve interoperability, we suggest product ontology architecture through the investigation of generic ontology architecture. We first suggest 4-layered ontology architecture for an integrated value chain. Extending this ontology architecture, we develop product ontology architecture which facilitates building product ontologies that are referred to all related participants inbound and outbound of the enterprise for collaboration. Using a product ontology, each enterprise can have semantic interoperability across each other for collaborative works. Because product ontologies have the feature of evolving through product lifecycle, the proposed product ontology architecture reflects this evolving feature to guarantee semantic interoperability.

Semi-automatic Ontology Construction based on Patterns

Abstract: This thesis aims to improve the ontology engineering process, by providing better semiautomatic support for constructing ontologies and introducing knowledge reuse through ontology patterns. The thesis introduces a typology of patterns, a general framework of pattern-based semi-automatic ontology construction called OntoCase, and provides a set of methods to solve some specific tasks within this framework. Experimental results indicate some benefits and drawbacks of both ontology patterns, in general, and semi-automatic ontology engineering using patterns, the OntoCase framework, in particular. The general setting of this thesis is the field of information logistics, which focuses on how to provide the right information at the right moment in time to the right person or organisation, sent through the right medium. The thesis focuses on constructing enterprise ontologies to be used for structuring and retrieving information related to a certain enterprise. This means that the ontologies are quite 'light weight' in terms of logical complexity and expressiveness. Applying ontology content design patterns within semi-automatic ontology construction, i.e. ontology learning, is a novel approach. The main contributions of this thesis are a typology of patterns together with a pattern catalogue, an overall framework for semi-automatic patternbased ontology construction, specific methods for solving partial problems within this framework, and evaluation results showing the characteristics of ontologies constructed semiautomatically based on patterns. Results show that it is possible to improve the results of typical existing ontology learning methods by selecting and reusing patterns. OntoCase is able to introduce a general top-structure to the ontologies, and by exploiting background knowledge, the ontology is given a richer structure than when patterns are not applied.

A methodology for engineering ontology acquisition and validation

Abstract: When engineering content is created and applied during the product life cycle, it is often stored and forgotten. Current information retrieval approaches based on statistical methods and keyword matching are not effective in understanding the context of engineering content. They are not designed to be directly applicable to the engineering domain. Therefore, engineers have very limited means to harness and reuse past designs. The overall objective of our research is to develop an engineering ontology (EO)-based computational framework to structure unstructured engineering documents and achieve more effective information retrieval. This paper focuses on the method and process to acquire and validate the EO. The main contributions include a new, systematic, and more structured ontology development method assisted by a semiautomatic acquisition tool. This tool is integrated with Protege ontology editing environment; an engineering lexicon (EL) that represents the associated lexical knowledge of the EO to bridge the gap between the concept space of the ontology and the word space of engineering documents and queries; the first large-scale EO and EL acquired from established knowledge resources for engineering information retrieval; and a comprehensive validation strategy and its implementations to justify the quality of the acquired EO. A search system based on the EO and EL has been developed and tested. The retrieval performance test further justifies the effectiveness of the EO and EL as well as the ontology development method.

Evaluating the Generation of Domain Ontologies in the Knowledge Puzzle Project

Abstract: One of the goals of the knowledge puzzle project is to automatically generate a domain ontology from plain text documents and use this ontology as the domain model in computer-based education. This paper describes the generation procedure followed by TEXCOMON, the knowledge puzzle ontology learning tool, to extract concept maps from texts. It also explains how these concept maps are exported into a domain ontology. Data sources and techniques deployed by TEXCOMON for ontology learning from texts are briefly described herein. Then, the paper focuses on evaluating the generated domain ontology and advocates the use of a three-dimensional evaluation: structural, semantic, and comparative. Based on a set of metrics, structural evaluations consider ontologies as graphs. Semantic evaluations rely on human expert judgment, and finally, comparative evaluations are based on comparisons between the outputs of state-of-the-art tools and those of new tools such as TEXCOMON, using the very same set of documents in order to highlight the improvements of new techniques. Comparative evaluations performed in this study use the same corpus to contrast results from TEXCOMON with those of one of the most advanced tools for ontology generation from text. Results generated by such experiments show that TEXCOMON yields superior performance, especially regarding conceptual relation learning.

Just What Is an Ontology, Anyway?

Abstract: This tutorial article describes some definitions of "ontology" as it relates to computer applications and gives an overview of some common ontology-based applications.

Cross-Lingual Ontology Mapping --- An Investigation of the Impact of Machine Translation

Abstract: Ontologies are at the heart of knowledge management and make use of information that is not only written in English but also in many other natural languages. In order to enable knowledge discovery, sharing and reuse of these multilingual ontologies, it is necessary to support ontology mapping despite natural language barriers. This paper examines the soundness of a generic approach that involves machine translation tools and monolingual ontology matching techniques in cross-lingual ontology mapping scenarios. In particular, experimental results collected from case studies which engage mappings of independent ontologies that are labeled in English and Chinese are presented. Based on findings derived from these studies, limitations of this generic approach are discussed. It is shown with evidence that appropriate translations of conceptual labels in ontologies are of crucial importance when applying monolingual matching techniques in cross-lingual ontology mapping. Finally, to address the identified challenges, a semantic-oriented cross-lingual ontology mapping (SOCOM) framework is proposed and discussed.

Extracting Modules from Ontologies: A Logic-Based Approach

Abstract: The ability to extract meaningful fragments from an ontology is essential for ontology reuse. We propose a definition of a module that guarantees to completely capture the meaning of a given set of terms, i.e., to include all axioms relevant to the meaning of these terms. We show that the problem of determining whether a subset of an ontology is a module for a given vocabulary is undecidable even for OWL DL. Given these negative results, we propose sufficient conditions for a for a fragment of an ontology to be a module. We propose an algorithm for computing modules based on those conditions and present our experimental results on a set of real-world ontologies of varying size and complexity.

Ontology Engineering Environments

Abstract: Ontology engineering is a successor of knowledge engineering and is expected to play a critical role in the next generation knowledge processing by contributing to knowledge sharing/reuse and semantic interoperability of metadata. Although the importance of ontology is well-understood, building a good ontology is a hard task. This paper discusses ontology engineering environments with comparison between them. Because of the space limitation, four environments are selected: OntoEdit, WebODE, Protege and Hozo each of which covers a wide rage of ontology development process rather than being a single-purpose tool which should be covered elsewhere. First, several key factors to evaluate ontology development environments are discussed. The stress is laid on development process-related aspects rather than static characteristics of an environment. According to the factors, each environment is briefly overviewed followed by comparison between them with a summary table.

A Model-Based Tool for Conceptual Modeling and Domain Ontology Engineering in OntoUML

Abstract: This paper presents a Model-Based graphical editor for supporting the creation of conceptual models and domain ontologies in a philosophically and cognitively well-founded modeling language named OntoUML. The Editor is designed in a way that, on one hand, it shields the user from the complexity of the ontological principles underlying this language. On the other hand, it reinforces these principles in the produced models by providing a mechanism for automatic formal constraint verification.

What Makes a Good Ontology? A Case-Study in Fine-Grained Knowledge Reuse

Abstract: Understanding which ontology characteristics can predict a "good" quality ontology, is a core and ongoing task in the Semantic Web. In this paper, we provide our findings on which structural ontology characteristics are usually observed in high-quality ontologies. We obtain these findings through a task-based evaluation, where the task is the assessment of the correctness of semantic relations. This task is of increasing importance for a set of novel Semantic Web tools, which perform fine-grained knowledge reuse (i.e., they reuse only appropriate parts of a given ontology instead of the entire ontology). We conclude that, while structural ontology characteristics do not provide statistically significant information to ensure that an ontology is reliable ("good"), in general, richly populated ontologies, with higher depth and breadth variance are more likely to provide reliable semantic content.

Embedding Knowledge Patterns into OWL

Abstract: We describe the design and use of the Ontology Pre-Processor Language (OPPL) as a means of embedding the use of Knowledge Patterns in OWL ontologies. We illustrate the specification of patterns in OPPL and discuss the advantages of its adoption by Ontology Engineers with respect to ontology generation, transformation, and maintainability. The consequence of the declarative specification of patterns will be their unambiguous description inside an ontology in OWL. Thus, OPPL enables an ontology engineer to work at the level of the pattern, rather than of the raw OWL axioms. Moreover, patterns can be analysed rigorously, so that the repercussions of their reuse can be better understood by ontology engineers and tools implementers. Thus the delivery of patterns with OPPL can provide a means of addressing the opacity and sustainability of OWL ontologies.