Applied Semantic Web Technologies  ◾  13
Similarly, security and privacy of sensitive information on the Semantic Web
must be ensured. While initial research has yielded interesting results, much work
remains in developing comprehensive solutions and techniques to assess and ensure
the trustworthiness, security, and privacy of Semantic Web content.
ese and other areas of research will be central to the industrial adoption of
semantic technologies in the years to come.
1.5 Organization of Book
is book is divided into four parts. e rst contains this introductory chapter by
the book editors. e second part, titled “Ontologies,” covers the fundamentals of
ontologies, ontology languages, and research related to ontology alignment, media-
tion, and mapping. “Ontology Engineering and Evaluation” is the third part dedi-
cated to the issues and tools related to ontology engineering and some methodologies
and processes used to create ontologies. It also covers several aspects of ontology
evaluation and social ontologies. e fourth and nal “Semantic Applications” part
highlights the use of semantics in several applications and the employment of ontolo-
gies and other semantic technologies in various domains. Examples of real-life appli-
cations of semantic technologies in areas such as logistics, smart home environments,
business process intelligence, and decision making are included. e following sec-
tion is a brief summary of the salient aspects and contributions of each chapter.
1.5.1 Part I: Introduction
Part I contains this single introductory chapter. We briey outline the history and
the state of the art in the Semantic Web technologies arena and point out future
research directions.
1.5.2 Part II: Ontologies
Part II contains four chapters. In Chapter 2, Akerkar provides an introduction
to ontology fundamentals and languages. Specically, he discusses Web Ontology
Language (OWL) in detail and reveals that ontology creation consists of den-
ing all ontology components through an ontology denition language. Ontology
creation is initially informal through the use of either natural language or diagram
technique, and is then encoded in a formal knowledge representation language
such as RDF Schema or OWL. Chapter 2 also discusses dierent types of exist-
ing ontologies, parameters for constructing an ontology, interoperability, reason-
ing issues, and ontology representation languages such as XML Schema and RDF
Schema.
Chapter 3 explores ways to provide semantic interoperability among information
systems. Specically, Rico et al. present a method for enriching the representations
14  ◾  Vijayan Sugumaran and Jon Atle Gulla
of entity semantics in an ontology by making contextual features explicit with the
aim of improving the matching of heterogeneous ontologies. is ontology match-
ing is used to establish meaningful information exchange among peers on a net-
work. is chapter also presents a case study based on a peer-to-peer information
sharing scenario in which each peer belongs to a dierent context.
In Chapter 4, Lanzenberger et al. present AlViz, a tool for ontology alignment
that uses information visualization techniques. ey argue that the use of these
techniques to graphically display data from ontology mappings can facilitate user
understanding of the meaning of the ontology alignment. Based on similarity mea-
sures of an ontology matching algorithm, AlViz helps assess and optimize align-
ment results at dierent levels of detail. Clustered graphs enable the user to examine
and manipulate the mappings of large ontologies.
Chapter 5 by Muthaiyah and Kerschberg introduces a hybrid ontology medi-
ation and mapping approach called the Semantic Relatedness Score (SRS) that
combines both semantic and syntactic matching algorithms. ey show that SRS
provides better results in terms of reliability and precision when compared to purely
syntactic matching algorithms. SRS has been developed through a process of rigor-
ously testing 13 well-established matching algorithms and producing a composite
measure from 5 of the best combinations of the 13. e authors contend that the
workloads of ontologists may be signicantly reduced by SRS measures since they
select from fewer concepts; hence their productivity improves drastically.
1.5.3 Part III: Ontology Engineering and Evaluation
Part III consists of ve chapters. Chapter 6 presents a collaborative ontology engi-
neering tool that extends Semantic MediaWiki (SMW). Simperl et al. argue that
unlike other wiki-based ontology editors, their tool focuses on light-weight ontol-
ogy modeling that can be carried out through appropriate interfaces by techni-
cally savvy users who have no knowledge engineering background. eir method
leverages existing knowledge structures into ontologies, and improves the results of
the modeling process through knowledge repair techniques that identify potential
problems and make suggestions to users.
In Chapter 7, Kotis, and Papasalouros discuss key issues, experiences, lessons
learned, and future directions in creating social ontologies from the World Wide
Web. is chapter reports on experiences and challenges related to automated learn-
ing of useful social ontologies based on a holistic approach in terms of the dierent
types of content that may be involved in the learning process, i.e., Web, Web 2.0,
and even Semantic Web content. e authors also address some of the challenges
in resolving the Semantic Web content creation bottleneck.
Chapter 8 by Blohm et al. discusses relation extraction for the Semantic Web. It
introduces Taxonomic Sequential Patterns (TSPs) as generalizations of many pat-
tern classes adopted in the literature. e authors explore whether TSPs are superior
to other types of patterns by looking at the precision–recall trade-o. ey also
Applied Semantic Web Technologies  ◾  15
present a principled mining algorithm as an extension of the well known ECLAT
algorithm that allows mining of taxonomic sequential patterns.
Kang’s Chapter 9 discusses data-driven evaluation of ontologies using machine
learning algorithms. He introduces a few cutting-edge taxonomy-aware algorithms
for automated construction of taxonomies inductively from both structured and
unstructured data. ese algorithms recursively group values based on a suitable
measure of divergence among the class distributions associated with the values to
construct taxonomies. ey generate hierarchical taxonomies of nominal, ordinal,
and continuous valued attributes.
In Chapter 10, Elhadad et al. present a method for functional evaluation of
search ontologies in the entertainment domain using natural language processing.
eir methodology evaluates the functional adequacy of an ontology by investigat-
ing a corpus of textual documents anchored to the ontology.
1.5.4 Part IV: Semantic Applications
Part IV contains six chapters. Tang et al. examine the addition of semantics to
decision tables in Chapter 11. ey propose a new approach to data and knowledge
engineering using a Semantic Decision Table (SDT) dened as a semantically rich
decision table supported by ontology engineering. ey also discuss applications
of SDT to demonstrate its usefulness and show how it can assist in ontology-based
data matching processes.
In Chapter 12, Barbagallo et al. explore semantic sentiment analysis based on
the reputation of Web information sources. ey propose a platform for analyzing
the Web reputation of a company’s products and services. eir approach oers a
self-service environment for the construction of personalized dashboards. e key
ingredients are the selection and composition of trustworthy services for informa-
tion access and processing.
Gulla et al. examine semantics and search in Chapter 13. ey provide a sum-
mary of prominent approaches to semantic search and explain the principles behind
them. ey also discuss semantic indexing techniques, the use of semantics on
search result pages, and techniques for semantic navigation of the result set. e
temporal or evolutionary dimension of search is also delineated.
In Chapter 14, Leukel and Kirn discuss semantics-based service composition
in transport logistics. Specically, they propose a semantic model for transport ser-
vices and demonstrate its usefulness in the domain of distribution logistics. ey
dene the problem of nding the best solution for a given set of customer require-
ments as a subclass of service composition, thus combining and linking (logistics)
services. ey contend that a key prerequisite for determining compositions is a
rich conceptualization that allows specication of relevant constraints that must
be fullled.
Ingvaldsen examines various aspects of ontology-driven business process intel-
ligence in Chapter 15. He highlights the importance of ontologies in the process
16  ◾  Vijayan Sugumaran and Jon Atle Gulla
analysis approach and demonstrates how ontologies and search are fundamental
for structuring process mining models and analysis perspectives and providing an
explorative analysis environment.
Finally, in Chapter 16, Tomic et al. discuss the use of semantics for energy
eciency in smart home environments. ey use ontology-based modeling and
service-oriented design for the integration of the building automation and advanced
metering in a truly exible system controlled by user-generated policies. eir sys-
tem is designed to operate on a common semantically described framework of
multimodal factors including preferences and policies of users; operational factors
of peripheral devices, sensors, and actuators; and external information character-
izing the availability and cost of energy.
Acknowledgment
e work of Vijayan Sugumaran has been partially supported by Sogang Business
School’s World Class University Program (R31-20002) funded by the Korea
Research Foundation.
References
Albertoni, R., Bertone, A., and De Martino, M. 2004. Semantic Web and Information
Visualization. In First Italian Workshop on Semantic Web Application and Perspective,
Dipartimento di Elettronica, Intelligenza Articiale e Telecomunicazioni, Ancona,
December 10, pp. 108–114.
Baeza-Yates, R. A. and Ribeiro-Neto, B. 1999. Modern Information Retrieval. Addison-Wesley
Longman, Boston.
Baldoni, M., Baroglio, C., and Henze, N. 2005. Personalization for the Semantic Web. In
REWERSE 2005: Lecture Notes on Computer Science 3564, pp. 173–212.
Berners-Lee, T., Hendler, J., and Lassila O. 2001. e Semantic Web. Scientic American,
May 2001, pp. 34–43.
Bizer, C., Heath, T., and Berners-Lee, T. 2009. Linked Data: e Story So Far. International
Journal of Semantic Web and Information Systems, Special Issue on Linked Data, 5,
1–22.
Breitman, K. K, Casanova, M. A., and Truszkowski, W. 2007. Semantic Web: Concepts,
Technologies and Applications. Springer Verlag, London.
Cimiano, P. 2006. Ontology Learning and Population from Text: Algorithms, Evaluations, and
Applications. Springer Verlag, Berlin. Originally published as PhD thesis, Universitat
Karlsruhe, Germany.
Daconta, M. C., Obrst, L. J., and Smith K. T. 2003. e Semantic Web: A Guide to the Future
of XML, Web Services, and Knowledge Management. Wiley, Indianapolis.
Davies, J., Studer, R., and Warren, P. 2006. Semantic Web Technologies: Trends and Research in
Ontology-Based Systems. Wiley. DOI: 10.3395/reciis.v3i1.245pt
Applied Semantic Web Technologies  ◾  17
Gibbins, N., Harris, S., and Shadbolt, N. 2004. Agent-Based Semantic Web Services. Web
Semantics: Science, Services and Agents on the World Wide Web, 1, 141–154.
Gruber, T.S. 1995. Toward Principles for the Design of Ontologies Used for Knowledge
Sharing. International Journal of Human–Computer Studies, 43, 907–928.
Gulla, J. A., Brasethvik, T., and Sveia-Kvarv, G. 2009. Association Rules and Cosine
Similarities in Ontology Relationship Learning. In Enterprise Information Systems:
Lecture Notes in Business Information Processing, Vol. 19, Springer Verlag, Berlin.
Hitzler, P., Krtzsch, M., and Rudolph, S. 2009. Foundations of Semantic Web Technologies.
Chapman & Hall/CRC, Boca Raton, FL.
Klapiscak, T. and Bordini, R. H. 2009. JASDL: A Practical Programming Approach
Combining Agent and Semantic Web Technologies. In DALT 2008: Lecture Notes in
Articial Intelligence 5397, pp. 91–110.
Langer, S. 2001. Natural Languages and the World Wide Web. Bulletin de linguistique
appliquée et générale, 26, 89–100. <http://www.cis.unimuenchen.de/people/langer/
veroeentlichungen/bulag.pdf>
Mutton, P. and Golbeck, J. 2003. Visualization of Semantic Metadata and Ontologies. In
Proceedings of Information Visualization, July 16–18, London.
Olmedilla, D. 2007. Security and Privacy on the Semantic Web. In Security, Privacy, and
Trust in Modern Data Management, Part V, Springer Verlag, Berlin, pp. 399415.
DOI: 10.1007/978-3-540-69861-6_26.
Richardson, M., Agrawal, R., and Domingos, P. 2003. Trust Management for the Semantic
Web. In ISWC 2003, Lecture Notes in Computer Science 2870, pp. 351–368.
..................Content has been hidden....................

You can't read the all page of ebook, please click here login for view all page.
Reset