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Handbook on Ontologies

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An ontology is a formal description of concepts and relationships that can exist for a community of human and/or machine agents. The notion of ontologies is crucial for the purpose of enabling knowledge sharing and reuse. The Handbook on Ontologies provides a comprehensive overview of the current status and future prospectives of the field of ontologies considering ontology languages, ontology engineering methods, example ontologies, infrastructures and technologies for ontologies, and how to bring this all into ontology-based infrastructures and applications that are among the best of their kind. The field of ontologies has tremendously developed and grown in the five years since the first edition of the "Handbook on Ontologies". Therefore, its revision includes 21 completely new chapters as well as a major re-working of 15 chapters transferred to this second edition.

Inhaltsverzeichnis

Frontmatter

Ontology Representation Languages

Frontmatter
What Is an Ontology?

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.

Nicola Guarino, Daniel Oberle, Steffen Staab
Description Logics

In this chapter, we explain what description logics are and why they make good ontology languages. In particular, we introduce the description logic

$$SHIQ$$

, which has formed the basis of several well-known ontology languages, including OWL. We argue that, without the last decade of basic research in description logics, this family of knowledge representation languages could not have played such an important rôle in this context.

Description logic reasoning can be used both during the design phase, in order to improve the quality of ontologies, and in the deployment phase, in order to exploit the rich structure of ontologies and ontology based information. We discuss the extensions to

$$SHIQ$$

that are required for languages such as OWL and, finally, we sketch how novel reasoning services can support building ontologies.

Franz Baader, Ian Horrocks, Ulrike Sattler
Ontologies in F-Logic

Frame Logic

(

F-logic

) combines the advantages of conceptual modeling that come from object-oriented frame-based languages with the declarative style, compact and simple syntax, and the well defined semantics of logic-based languages. F-logic supports typing, meta-reasoning, complex objects, methods, classes, inheritance, rules, queries, modularization, and scoped inference. In this paper we describe the capabilities of knowledge representation systems based on F-logic and illustrate the use of this logic for ontology specification. We give an overview of the syntax and semantics of the language and discuss the main ideas behind the various implementations. Finally, we present a concrete application deployed in the automotive industry.

Jürgen Angele, Michael Kifer, Georg Lausen
Resource Description Framework

This chapter introduces Resource Description Framework (RDF), the W3C recommendation for semantic annotations in the Semantic Web. It will cover the syntax and semantics of RDF, as well as its relation with the W3C OWL Web Ontology Language. To address the mismatch between RDF and OWL-DL, the most expressive decidable fragment of the OWL standard, we introduce a novel variant of RDF(S), called RDFS-FA, which provides a solid semantic foundation for many of the latest Description Logic-based SW ontology languages, such as OWL-DL and OWL2-DL.

Jeff Z. Pan
Web Ontology Language: OWL

The expressivity of RDF and RDF Schema that was described in [

12

] is deliberately very limited: RDF is (roughly) limited to binary ground predicates, and RDF Schema is (again roughly) limited to a subclass hierarchy and a property hierarchy, with domain and range definitions of these properties.

However, the Web Ontology Working Group of W3C [

10

] identified a number of characteristic use-cases for Ontologies on the Web which would require much more expressiveness than RDF and RDF Schema. It proceeded to define OWL, the language that is aimed to be the standardised and broadly accepted ontology language of the Semantic Web.

In this chapter, we first describe the motivation for OWL in terms of its requirements, and the resulting non-trivial relation with RDF Schema. We then describe the various language elements of OWL in some detail.

Grigoris Antoniou, Frank van Harmelen
Ontologies and Rules

Ontologies and rules are two established paradigms in knowledge modelling, and play an important role for the Semantic Web. In this chapter, we present an introduction to common approaches for combining OWL ontologies and rules. In particular, we cover the Semantic Web Rules Language SWRL and Description Logic Programs DLP, and give pointers to the literature.

Pascal Hitzler, Bijan Parsia

Ontology Engineering

Frontmatter
Ontology Engineering Methodology

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.

York Sure, Steffen Staab, Rudi Studer
Ontology Engineering and Evolution in a Distributed World Using DILIGENT

Existing mature ontology engineering approaches are based on some basic assumptions that are often neglected in practice.

Ontologies often need to be built in a decentralized way, ontologies must be given to a community in a way such that individuals have partial autonomy over them, ontologies have a life cycle that involves an iteration back and forth between construction/modification and use and ontologies should support the participation of non-expert users in ontology engineering processes.

While recently there have been some initial proposals to consider these issues, they lack the appropriate rigor of mature approaches. i.e. these recent proposals lack the appropriate depth of methodological description, which makes the methodology usable, and they lack a proof of concept by concrete cases studies. In this paper, we describe the DILIGENT methodology that takes decentralization, partial autonomy, iteration and non-expert builders into account and we demonstrate its proof-ofconcept in two real-world organizational case studies.

H. Sofia Pinto, C. Tempich, Steffen Staab
Formal Concept Analysis

Formal concept analysis (FCA) is a mathematical theory about concepts and concept hierarchies. Based on lattice theory, it allows to derive concept hierarchies from datasets. In this survey, we recall the basic notions of FCA, including its relationship to folksonomies. The survey is concluded by a list of FCA based knowledge engineering solutions.

Gerd Stumme
An Overview of OntoClean

OntoClean is a methodology for validating the ontological adequacy and logical consistency of taxonomic relationships. It is based on highly general ontological notions drawn from philosophy, like

essence

,

identity

, and

unity

, which are used to elicit and characterize the intended meaning of properties, classes, and relations making up an ontology. These aspects are represented by formal metaproperties, which impose several constraints on the taxonomic relationships between concepts. The analysis of these constraints helps in evaluating and validating the choices made. In this chapter we present an informal overview of the philosophical notions involved and their role in OntoClean, review some common ontological pitfalls, and walk through the example that has appeared in pieces in previous papers and has been the basis of numerous tutorials and talks.

Nicola Guarino, Christopher A. Welty
Ontology Design Patterns

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.

We present a typology of OPs, and then focus on Content Ontology Design Patterns in terms of their background, definition, communication means, related work beyond ontology engineering, exemplification, creation, and usage principles. At the time of chapter’s final version, recently performed experiments of patternbased ontology design show remarkable quality improvement within some sample ontology design projects, specially in terms of compliance to tasks expressed as competency questions or scenarios.

Aldo Gangemi, Valentina Presutti
Ontology Learning

Ontology learning techniques serve the purpose of supporting an ontology engineer in the task of creating and maintaining an ontology. In this chapter, we present a comprehensive and concise introduction to the field of ontology learning. We present a generic architecture for ontology learning systems and discuss its main components. In addition, we introduce the main problems and challenges addressed in the field and give an overview of the most important methods applied. We conclude with a brief discussion of advanced issues which pose interesting challenges to the state-of-the-art.

Philipp Cimiano, Alexander Mädche, Steffen Staab, Johanna Völker
Ontology and the Lexicon

A lexicon is a linguistic object and hence is not the same thing as an ontology, which is non-linguistic. Nonetheless, word senses are in many ways similar to ontological concepts and the relationships found between word senses resemble the relationships found between concepts. Although the arbitrary and semi-arbitrary distinctions made by natural languages limit the degree to which these similarities can be exploited, a lexicon can nonetheless serve in the development of an ontology, especially in a technical domain.

Graeme Hirst
Ontology Evaluation

The evaluation of ontologies is still an emerging field. A set of preliminary ideas and frameworks have been suggested in the literature. This chapter collects ontology quality criteria and lays out a common framework for aspects of ontology evaluation. It will present in depth descriptions of these ontology aspects and how to evaluate them. The techniques and ideas collected and presented here will help to uncover errors in ontologies. This chapter concentrates on the automatic, domain- and task-independent evaluation of an ontology.

Denny Vrandečić
Ontology Engineering Environments

In this chapter we discuss trends of ontology engineering environments and their characteristics through comparison between some tools. After a summarization of the recent trends of them, the authors enumerate factors which characterize those environments. Then we take up OntoEdit, Hozo, WebODE, SWOOP and Protégé, and compare them according to the factors.

Riichiro Mizoguchi, Kouji Kozaki
Exploring the economical aspects of ontology engineering

A core requirement for the usage of ontologies within enterprizes is the availability of proved and tested techniques which guarantee an efficient engineering of high-quality ontologies, be that by reuse, manual building or automatic knowledge acquisition. Besides feasible technological support this includes in equal measure integrating ontology engineering within the more general framework of enterprize information architectures, and taking into account the economics of ontology engineering projects, in particular issues of cost effectiveness and profitability. This chapter addresses these two aspects. We discuss the role of ontology engineering in the context of enterprize architectures, arguing for the importance of cost-related measures as decision support in planning and controlling. Then we analyze approaches for reliably assessing the costs of building ontologies, and the usage of cost-related information to quantifiably support decisions arising during the life cycle of an ontology and to optimize the operation of associated processes. We account for the similarities and differences between software and ontology engineering in order to establish the appropriateness of applying methods with a long-standing tradition in this adjacent engineering field to ontologies. Building upon the results of this analysis we introduce

ONTOCOM

as the first cost model for ontologies and discuss different methods to improve its accuracy for a wide range of ontology engineering projects at public and corporate level.

Elena Simperl, Christoph Tempich

Ontologies

Frontmatter
Foundational Choices in DOLCE

Foundational ontologies are ontologies that have a large scope, can be highly reusable in different modeling scenarios, are philosophically and conceptually well founded, and are semantically transparent.

After the analysis and comparison of alternative theories on general notions like ‘having a property’, ‘being in time’ and ‘change through time’, this paper shows how specific elements of these theories can be coherently integrated into a foundational ontology. The ontology is here proposed as an improvement of the core elements of the ontology

dolce

and is thus called

dolce-core

.

Stefano Borgo, Claudio Masolo
An Ontology for Software
Daniel Oberle, Stephan Grimm, Steffen Staab
COMM: A Core Ontology for MultimediaAnnotation

In order to retrieve and reuse non-textual media, media annotations must explain how a media object is composed of its parts and what the parts represent. Annotations need to link to background knowledge found in existing knowledge sources and to the creation and use of the media object. The representation and understanding of such facets of the media semantics is only possible through a formal language and a corresponding ontology. In this chapter, we analyze the requirements underlying the semantic representation of media objects, explain why the requirements are not fulfilled by most semantic multimedia ontologies and present COMM, a core ontology for multimedia, that has been built re-engineering the current de-facto standard for multimedia annotation, i.e. MPEG-7, and using DOLCE as its underlying foundational ontology to support conceptual clarity and soundness as well as extensibility towards new annotation requirements.

Richard Arndt, Raphaël Troncy, Steffen Staab, Lynda Hardman
Using the PSL Ontology
Michael Grüninger
Ontologies for Formal Representation of Biological Systems

This chapter provides an overview of how the use of ontologies may enhance biomedical research by providing a basis for a formalized, and shareable descriptions, of

models

of biological systems.

A wide variety of artifacts are labeled as “ontologies” in the Biomedical domain, leading to much debate and confusion. The most widely used ontological artifact are controlled vocabularies (CVs). A CV provides a list of terms whose meanings are specifically defined. Terms from a CV are usually used for indexing records in a database. The Gene Ontology (GO) is the most widely used CV in databases serving biomedical researchers. The GO provides term for declaring the molecular function (MF), biological process (BP) and cellular component (CC) of gene products. The statements comprising these MF, BP and CC declaration are called annotations [

51

], which are predominantly used to interpret results from high throughput gene expression experiments [

27

,

53

]. Arguably, CVs provide the most value for effort in terms of facilitating database search and interoperability.

Nigam Shah, Mark Musen
Ontologies for Cultural Heritage

In the cultural heritage domain information systems are increasingly deployed, digital representations of physical objects are produced in immense numbers and there is a strong political pressure on memory institutions to make their holdings accessible to the public in digital form. The sector splits into a set of disciplines with highly specialized fields. Due to the resulting diversity, one can hardly speak about a “domain” in the sense of “domain ontologies” [33]. On the other side, study and research of the past is highly interdisciplinary. Characteristically, archaeology employs a series of “auxiliary” disciplines, such as

archaeometry, archaeomedicine, archaeobotany, archaeometallurgy, archaeoastronomy

, etc., but also historical sources and social theories.

Martin Doerr

Ontologies

Frontmatter
RDF Storage and Retrieval Systems

Ontologies are often used to improve data access. For this purpose, existing data has to be linked to an ontology and appropriate access mechanisms have to be provided. In this chapter, we review RDF storage and retrieval technologies as a common approach for accessing ontology-based data. We discuss different storage models, typical functionalities of RDF middleware such as data model support and reasoning capabilities and RDF query languages with a special focus on SPARQL as an emerging standard. We also discuss some trends such as support for expressive ontology and rule languages.

Alice Hertel, Jeen Broekstra, Heiner Stuckenschmidt
Tableau-Based Reasoning

Tableau-based methods for satisfiability checking build the backbone of major contemporary ontology reasoning sytems. The main idea of tableau-based methods for satisfiability checking is to systematically construct a representation for a model of the input formulae. If all representations that are considered by the procedure turn out to contain an obvious contradiction, a model representation cannot be found and it is concluded that the set of formulae is unsatisfiable.

In this chapter, tableau-based reasoning methods are formally introduced. We start with a nondeterministic basic version which subsequently will be extended with optimization techniques in order to demonstrate how practical systems can be built. We also demonstrate how computed tableau structures can be exploited for other inference problems in an ontology reasoning system.

Ralf Möller, Volker Haarslev
Resolution-Based Reasoning for Ontologies

We overview the algorithms for reasoning with description logic (DL) ontologies based on resolution. These algorithms often have worst-case optimal complexity, and, by relying on vast experience in building resolution theorem provers, they can be implemented efficiently. Furthermore, we present a resolution-based algorithm that reduces a DL knowledge base into a disjunctive datalog program, while preserving the set of entailed facts. This reduction enables the application of optimization techniques from deductive databases, such as magic sets, to reasoning in DLs. This approach has proven itself in practice on ontologies with relatively small and simple TBoxes, but large ABoxes.

Boris Motik
Ontology Repositories

The growing use and application of ontologies in the last years has led to an increased interest of researchers and practitioners in the development of ontologies, either from scratch or by reusing existing ones. Reusing existing ontologies instead of creating new ones from scratch has many benefits: It lowers the time and cost of development, avoids duplicate efforts, ensures interoperability, etc. In fact, ontology reuse is one of the key enablers for the realization of the Semantic Web. However, currently, ontologies are mostly developed from scratch, due to several reasons. First, ontologies are usually tailored to work for specific applications, restricting its potential reusability. Second, developers usually follow a monolithic approach when developing ontologies, usually covering different domains, hampering the reusability of relevant parts for other applications. Third, ontologies are rather difficult to find due to the lack of standards for documenting them and appropriate tools supporting intelligent ontology discovery and selection by end users. In this chapter, we define a generic ontology repository framework that enables the implementation of fully-fledged ontology repositories providing the technological support to the aforementioned issues. We distinguish between the ontology repository itself and the software to manage the repository, and describe their main aspects and services. Finally, we present two exemplary systems based on this framework.

Jens Hartmann, Raúl Palma, Asunción Gómez-Pérez
Ontology Mapping

However, if we want to have the applications using different ontologies to “talk” to one another, or if we want to integrate data that is annotated with or structured according to different ontologies, we must first find the

correspondences

between concepts in these ontologies. The process of finding such correspondences is called

ontology mapping

. Ontology mapping (also referred to as ontology matching, or ontology alignment) is one of the most active areas of ontology research. Creating high-quality ontology mappings automatically is the holy grail of the Semantic Web research. Ontologies have gained popularity in the AI community as a means for establishing explicit formal vocabulary to share between applications. Therefore, one can say that one of the goals of using ontologies is not to have the problem of heterogeneity at all. It is of course unrealistic to hope that there will be an agreement on one or even a small set of ontologies. While having some common ground either within an application area or for some high-level general concepts could alleviate the problem of semantic heterogeneity, we will still need to map between ontologies, whether they extend the same foundational ontology or are developed independently.

Natalya F. Noy

Ontology based Infrastructure and Methods

Frontmatter
Ontologies and Software Engineering

The chapter analyzes the state of the art in the use of ontologies for various software engineering tasks. The chapter starts from defining software engineering as an application context for ontologies. Next, it introduces a framework that identifies places in software lifecycle where ontologies can contribute to improvethe current state of software engineering.

Dragan Gašević, Nima Kaviani, Milan Milanović
Semantic Web Services

Semantic Web services are a prominent application area for ontologies, and Semantic Web technologies in general. Using Semantic technologies such as ontologies for describing Web services enables automating tasks such as discovering, combining, and executing services. In this chapter we survey the aspects relevant to the description of Semantic Web services through an overview of the Web Service Modeling Ontology (WSMO), which provides a conceptual model for describing services.

We then survey in more detail the various uses of ontologies in Web service descriptions. Finally, we describe other prominent Web service description frameworks and contrast them with WSMO, in particular WSDL-S, OWL-S, and SWSF.

Jos de Bruijn, Mick Kerrigan, Michal Zaremba, Dieter Fensel
Ontologies for Machine Learning

The growing amounts of ontologies and semantically annotated data has led to considerable interest in mining these richly structured data sources. While research has actively addressed the issue of inducing semantic structures from conventional types of data, approaches for mining semantically annotated data still constitute an emerging field of research. Approaches in this direction either investigate how semantic structures can help to advance classical Machine Learning tasks or how semantic structures can themselves become the objects of interest. In this chapter, we review some of the main topics at the intersection of Machine Learning and Semantic Web research.

Stephan Bloehdorn, Andreas Hotho
Information Extraction

Information Extraction (IE) addresses the intelligent access to document contents by automatically extracting information relevant to a given task. This chapter focuses on how ontologies can be exploited to interpret the textual document content for IE purposes. It makes a state of the art of IE systems from the point of view of IE as a knowledge-based NLP process. It reviews the different steps of NLP necessary for IE tasks: named entity recognition, term analysis, semantic typing and identification specific relations. It stresses on the importance of ontological knowledge for performing each step and presents corpus-based methods for the acquisition of the required knowlege.

This chapter shows that IE is an ontology-based activity and argues that future effort in IE should focus on formalizing and reinforcing the relation between the text extraction and the ontology model. The discussion gives authors’ insights on the integration of ontological knowledge in IE systems from a formal and pragmatic point of view.

Examples in this chapter are taken from IE tasks for biology since this domain attracts a large community of IE specialists and provides a large number of ontological resources.

Claire Nédellec, Adeline Nazarenko, Robert Bossy
Browsing and Navigation in Semantically Rich Spaces
Experiences with Magpie Applications

Semantic Web is a medium for knowledge exchange, where knowledge produced by one agent is consumed by another agent who may extend or modify it. Semantic Web also affords novel opportunities for acquiring knowledge – including approaches favoring automated selection, reuse and integration of external, just-intimegathered semantic resources. As semantic resources are no longer specifically developed for a single purpose, their re-contextualization within other web resources (e.g., web pages) is becoming a more pressing challenge. In this chapter, we look at the case when external semantic resources discovered in the web-sized corpus arere-contextualized to enhance the user experience of an arbitrary web content visited by a particular user. We first review different approaches showcasing different facets of semantic browsing and define the notion of ‘semantic browsing’ in general terms. Next, we share our experiences with Magpie, an in-house semantic web browsing framework, and illustrate new functional features such a semantically-enriched browsing tool may offer on the example of introducing additional user interaction modalities and developing a capability to work with multiple background knowledge models simultaneously. In the discussion we re-visit the defining tenets of ‘semantic browsing’ and look at how the reuse of just-in-time discovered and applied semantic resources really addresses the issue of enabling the user to re-contextualize semantic data for the purposes of text analysis, data interpretation, relationship discovery, and knowledge validation.

Martin Dzbor, Enrico Motta, Laurian Gridinoc

Ontology based Applications

Frontmatter
Ontologies for Knowledge Management

Within Computer Science and Artificial Intelligence, the term

ontologies

was coined in the

Knowledge Sharing and Reuse Effort

, for efficient engineering of (distributed, cooperating) knowledge-based systems. It is not surprising that it soon entered the Knowledge Management (KM) area:

Sharing

and

reuse

of personal, group, and organizational knowledge are among the central goals aimed at in most KM projects. In this chapter we introduce the main ideas of KM, as well as the role of and requirements for information technology (IT) in KM. We discuss the potential of ontologies as elements in IT support for KM. We characterize their current role in research and practice, derive a working focus for the near future, and conclude with an outlook on trends in KM software and their implications on ontologies.

Andreas Abecker, Ludger van Elst
Application of Ontologies in Bioinformatics

The use of ontologies has become a mainstream activity within bioinformatics. In a largely descriptive science such as biology, the need to have a common understanding of things described is obvious. The need to be able to apply computational methods to the large quantities of data being produced also suggests a computational requirement to standardise descriptions in biology.

As a mechanism for describing the categories of entities and their characteristics, ontologies offer many of the features that can support a descriptive science. The main use of ontologies in bioinformatics has been the delivery of controlled vocabularies. In this chapter we explore this use of ontology, but also other uses, especially those that have a deeper computational aspect. We take a broad view of ontology to include many ontology-like resources and classify the uses of ontology and ontology-like artifacts. We present a series of case studies and conclude by describing the current state and future directions for bio-ontologies.

Robert Stevens, Phillip Lord
Semantic Portals for Cultural Heritage

Cultural heritage is a promising application domain for semantic web technologies due the semantic richness and heterogeneity of cultural content, and the distributed ways in which the content is created in memory organizations and by citizens. This chapter overviews issues and research related to creating semantic portals for publishing cultural heritage collections and other content on the web.

Eero Hyvönen
Ontology-Based Recommender Systems

We present an overview of the latest approaches to using ontologies in recommender systems and our work on the problem of recommending on-line academic research papers. Our two experimental systems, Quickstep and Foxtrot, create user profiles from unobtrusively monitored behaviour and relevance feedback, representing the profiles in terms of a research paper topic ontology. A novel profile visualization approach is taken to acquire profile feedback. Research papers are classified using ontological classes and collaborative recommendation algorithms used to recommend papers seen by similar people on their current topics of interest. Ontological inference is shown to improve user profiling, external ontological knowledge used to successfully bootstrap a recommender system and profile visualization employed to improve profiling accuracy.

In a specific case study we report results from two small-scale experiments, with 24 subjects over 3 months, and a large-scale experiment, with 260 subjects over an academic year, are conducted to evaluate different aspects of our approach. The overall performance of our ontological recommender systems are favourably compared to other systems in the literature.

Stuart E. Middleton, David De Roure, Nigel R. Shadbolt
Backmatter
Metadaten
Titel
Handbook on Ontologies
herausgegeben von
Steffen Staab
Rudi Studer
Copyright-Jahr
2009
Verlag
Springer Berlin Heidelberg
Electronic ISBN
978-3-540-92673-3
Print ISBN
978-3-540-70999-2
DOI
https://doi.org/10.1007/978-3-540-92673-3