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International Review on Computers and Software (I.RE.CO.S.), Vol. 2, n. 5 September 2007

Semantic Web: A state of the art survey Dimitris N. Kanellopoulos1, Sotiris B. Kotsiantis2

Abstract –The semantic web is an extension of the current web in which information is given well-defined meaning. It is a concept that enables better machine processing of information on the web, by structuring documents written for the web in such a way that they become understandable by computers. This can be used for creating complex applications such as intelligent browsers, intelligent software agents, global databases with data from the web, reuse of information, etc. Central to the vision of the semantic web are ontologies. Ontologies provide a shared understanding of a domain of interest to support communication among human and software agents, typically being represented in a machine-processable representation language. Web ontology languages like OWL provide a technological basis to enable the semantic web. This paper considers the basic principles of the semantic web, and reviews important tools for creating and maintaining ontologies in various frameworks. Copyright © 2007 Praise Worthy Prize S.r.l. All rights reserved. Keywords: metadata, ontology, semantic web

I.

use information more efficiently. It is a concept that enables better machine processing of information on the web, by structuring documents written for the web in such a way that they become understandable by machines. The semantic web allows content to become aware of it. This awareness allows humans and software agents (viz. Internet-based programs that are created to act autonomously) to query and infer knowledge from information quickly and in many cases automatically. The semantic web enables more advanced automated processing on the web and the use of large amount of information more effectively. Using the semantic web new information can be derived (reasoning) from existing information, while intelligent browsers and services become possible. This paper presents fundamentals of the semantic web and considers the new possibilities afforded by the semantic web in the area of knowledge management. The rest of the paper is organized as follows: Section II presents the semantic web initiative and Section III discusses the role of ontologies. Section IV discusses ontology applications on the web. Section V describes ontology-engineering issues. Section VI presents ontology reasoning tools, while Section VII analyzes semantic portals. Section VIII presents semantic web services and their ontological description. Section IX discusses ontology-based applications and projects on the semantic web. Section X concludes the paper and gives guidelines for future work. Finally, Appendix A provides additional references for useful websites, journals, books, ontologies and commercial activity.

Introduction

Current web has a number of limitations. A typical example is the use of search engines. The most common way to find information on the web is by using search engines such as Google. In some cases the results of a query are not correct. For example, we found web pages that were unrelated to what we were looking for or we failed to find some particularly relevant pages. SanchezFernadez and Fernadez-Garcva [1] state that there are at least three kinds of situations that can lead to such errors: • Polysemy: we search for a term and find web pages containing that term but with a different meaning from the one we are interested in. • Synonymy: we search for a term and instead of finding pages related to what we are interested in we find web pages containing a synonym of the term. • Multilingualism: we search for a term in English and we find pages related to what we are interested in but they are written in another language. In all these cases, the problem is that queries are based on syntax search. If formal annotations had identified the main concepts and entities contained in web pages, a semantic search engine could avoid making the three types of errors described above. Semantically isolated pieces of information could be connected, and the user could find the information sources more easily. The semantic web was ideated by Tim Berners-Lee et al. [2] and enriches the web with semantic information to enable systems to access and Manuscript received and revised August 2007, accepted September 2007

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II.

The Semantic Web Initiative

The semantic web is a visionary project that aims to enhance the usability and usefulness of the web by enabling computers to find, read, understand and use the content of web documents to accomplish tasks via automated software agents and web-based services. The semantic web forms a platform for search engines, information brokers and ultimately the ‘intelligent’ software agents. It integrates data, content, applications, and processes via a shared ontology of concepts, properties, constraints, logic and rules. In particular, it propagates interoperability, reusability and shareability, all grounded over an extensive expression of semantics with a standardized communication among intelligent information systems. The development of the semantic web proceeds in steps, each step building a layer on top of another. The layered design is shown in Fig. 1.





Fig. 1. The semantic web architecture [2]



The URI (Uniform Resource Identifier) is the format used in the semantic web to assign identifiers to resources. More information about URI can be found at RFC http://www.ietf.org/rfc/rfc2396.txt.



At the bottom layer we find XML (eXtensible Markup Language) (http://www.w3.org/XML/). It is a language that lets one write structured web documents with a user-defined vocabulary. XML is particularly suitable for sending documents across the web, thus supporting syntactic interoperability. Unfortunately, XML lacks semantics and software agents cannot be guaranteed to determine the interpretation of its tags. XML is designed to describe the structure of a document, not the content.



The RDF (Resource Description Framework) (http://www.w3.org/RDF/) description model uses “object-attribute-value” triples, called statements. Its goal is to add formal semantics to the web and provide a data model and syntax convention for representing the semantics of the data in a standardized manner. It is a basic data model for writing simple statements about web objects (web Copyright © 2007 Praise Worthy Prize S.r.l. - All rights reserved

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resources). The RDF data model does not rely on XML, but RDF has an XML-based syntax. Therefore, it is located on top of the XML layer. RDF schema provides modeling primitives for organizing web objects into hierarchies. It has been introduced as a layer on the top of the basic RDF model. RDF schema is based on RDF and can be viewed as a primitive language for writing ontologies. However, there is a need for more powerful languages that expand RDF schema and allow the representation of more complex relationships between web objects. For this reason, ontology languages, such as OWL (Ontology Web Language) (http://www.w3.org/2004/OWL/) by W3C (World Wide Web Consortium) are built on the top of RDF and RDF schema. Ontology languages have a compact syntax and are highly intuitive to humans. They have a well-defined formal semantics and are able to represent human knowledge. They include reasoning properties and have the potential for building knowledge bases. Finally, they have a proper link with existing web standards to ensure interoperability. It is noteworthy that Pulido et al. [3] describe in details recent ontology languages, particularly useful for the semantic web. The logic layer enhances the ontology language further, and allows writing application-specific declarative knowledge using rules. Rules model logical sentences that are always true. They are commonly used for modelling knowledge and can be used for three purposes: o Knowledge creation: to obtain new logical sentences based on information stored in the knowledge base. o Constraint definition: define properties that the model should meet. They are used to detect inconsistencies. o Reactive rules: determine actions to be taken by a knowledge-based system as a result of certain conditions being met. By applying logical deduction, one can infer new knowledge from the information, which is stated explicitly. For example, the axiom: “A dog drinks water” and the statement: “Bob is a dog” allows one to logically infer that “Bob drinks water”. Rules and constraints are needed in addition to factual knowledge to capture the semantics for inference. The RuleML (Rule Markup Language) (http://www.ruleml.org/) is an XML language for defining rules intended for the semantic web. It contains aspects logical programming, functional programming and object orientation. Recently, the designers of RuleML have submitted a language called SWRL (Semantic Web Rule Language) for consideration as a W3C standard(http://www.w3.org/Submission/2004/SUB International Review on Computers and Software, Vol. 2, n. 5

D. N. Kanellopoulos, S. B. Kotsiantis

M-SWRL-20040521/). In addition, the REWERSE Working Group I1 developed the R2ML (REWERSE Rule Markup Language) for the purpose of rules interchange between different systems and tools. An ontology enables logical inference on facts through axiomatization. Therefore, ontologies provide constructs for effective binding with logical inference primitives and options to support a variety of expressiveness and computational complexity requirements. Description Logics (DLs) describe knowledge in terms of concepts and role restrictions that automatically derive classification taxonomies. Currently, there are various efficient implementations for DL languages. •

The proof layer [4] involves the actual deductive process, as well as the representation of proofs in web languages and proof validation. Digital signatures will play an important role in proof. The source has to sign the statement s(h)e makes so that software agents can check if information really comes from the source it claims to be. Other security technologies like encryption and access control can be used to ensure confidentiality of information.



Trust [5] will emerge through the use of digital signatures, and other kind of knowledge, which is based on recommendations by software agents we trust, or rating and certification agencies and consumer bodies. Everybody should be able to define a trust model for himself, i.e., one can define how much trust he would put on each source on the semantic web. Since it is unrealistic to define the extent of trust for each source, a mechanism is necessary to derive the degree of trust for each new source. One solution is the notion of “web of trust”: when one trusts a source A, s(he) also trusts all other sources that are trusted by source A, but to a lower extent. By this way, a huge and hierarchical network is created which facilitates agents infer information based on their trusted knowledge.

semantic interconnections, and some simple rules of inference and logic for some particular topic”. Existing ontologies can be classified into the following major categories: (1) meta-ontologies, (2) upper ontologies, (3) domain, and (4) specialized ontologies. The ontology languages (RDF, RDFS, DAML+OIL, OWL) are actually meta-ontologies themselves; and their instances are semantic web ontologies. Upper ontologies provide a high level model about the world using the ontology constructs provided by metaontologies. Specialized ontologies concentrate on a set of basic and commonly used concepts. For example, the RSS news digest ontology is driven by the blogging community and has now become a very popular ontology. Domain ontologies (the main classification) refer to the detailed structuring of a context of analysis with respect to the sub-domains, which it is composed of; i.e. they define domain specific conceptualizations (e.g., the gene ontology). An ontology comprises the classes of entities, relations between entities, and the axioms which apply to the entities of that domain [8]. An ontology is made up of the following parts: ƒ Classes and instances: For example, an ontology modelling the tourism domain may contain classes such as “tourism destination” or “attraction”. Usually instances are used to model elements and belong to classes. For example, the instance “Parthenon” belongs to the class “attraction”. Classes are usually organized in a hierarchy of subclasses. For example, the concept “man” can be defined as a sub-class of an existing concept “person” in WordNet vocabulary (http://wordnet.princeton.edu/). If class A is a subclass of class B, instances of class A also belong to class B. ƒ Properties: they establish relationships between the concepts of an ontology. For example, the property “BelongTo” associates an object with its owner it belongs to. The simplest type of ontologies is called taxonomies and they are made up of a hierarchy of classes representing the relevant concepts in the domain. Fig. 2 presents a taxonomy for “Flight”. Remark that a flight of lowest price IS_A economy flight which in turn IS_A flight.

III. The Role of Ontologies The semantic web requires methodologies for extracting and defining the knowledge that is to be represented. The development of ontologies is fundamental to allow machine-supported data interpretation and integration. The success of the semantic web depends heavily upon the creation of suitable ontologies. The term ‘ontology’ is derived from the Greek words “onto”, which means being, and “logia”, which means written or spoken discourse. Tom Gruber [6] defines an ontology as an explicit specification of a conceptualization. According to Hendler [7] (p.30), an ontology is “a set of knowledge terms, including the vocabulary, the

Fig. 2. Flight class taxonomy

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III.1. Ontology-based tools

sources of services and in documenting and archiving them. Sycara et al. [13] described a comprehensive software agent framework that allows the set up of semantic-based e-markets, in which intelligent software agents can exploit semantics on the web. Dietrich et al. [14] discussed a general architecture for rule-based agents and analyzed how it can be realized with the help of semantic web languages.

The ontological analysis clarifies knowledge structure, as ontologies are essential for obtaining vocabularies for representing knowledge. The characterization of a domain in ontological terms facilitates an intelligent access to the information. Ontology-based knowledge management includes mainly: knowledge acquisition, knowledge representation, and knowledge use.

IV.

Knowledge acquisition. Automatic knowledge extraction from unstructured and semi-structured data in external data repositories is required as large amounts of unstructured and semi-structured information held on organizational intranets. This is the function of knowledge acquisition tools. For example, the OntoWrapper tool can extract knowledge from semistructured information, and the OntoExtract can extract knowledge from unstructured information. Both tools are modules of the OntoBuilder [9].

Ontology Applications on the Web

The success of the semantic web depends on the availability of ontologies, as well as the proliferation of web pages annotated with metadata conforming to these ontologies. IV.1. Semantic Annotation Semantic annotation is the process of inserting tags in web resources in order to assign semantics. The requirements of semantic annotation for knowledge management are considered successfully in the survey [15]. There are many semantic annotation approaches that can be categorized according to certain criteria, such as the degree of automation of annotation tasks, the type of web resources that can be annotated, etc.

Knowledge representation. Once knowledge has been acquired from human sources or automatically extracted, it is then required to represent the knowledge in an ontology language (and to provide a query language to provide access to the knowledge so stored). This is the function of the ontology repository. Well known ontology repositories are: ƒ DAML Ontology Library (http://www.daml.org/ontologies) ƒ SchemaWeb (http://www.schemaweb.info/) ƒ W3C’s Ontaria http://www.w3.org/2004/ontaria ƒ Semantic Web Search (http://www.semwebcentral.org) ƒ Swoogle (http://swoogle.umbc.edu/)

Manual annotation systems: These systems provide a user interface allowing human annotators to view and browse both ontologies and web resources simultaneously, using the knowledge modelled in the ontologies to add annotations to web resources. Such systems are Annotea [16], SHOE Knowledge Annotator (http://www.cs.umd.edu/projects/plus/SHOE/Knowledg eAnotator.html), SMORE [17] and CREAM [18]. Annotea provides RDF-based markup and does not support information extraction nor it is linked to an ontology server. The SHOE (Simple HTML Ontology Extensions) [19] project indented to annotate web documents with machine-readable knowledge. SHOE is a set of tools including: a) a knowledge annotator, b) the crawler expose, c) the knowledge representation system PARKA, d) the PIQ (PARKA Interface for Queries) and e) the SHOE search engine.

Knowledge use. Information access tools are required to allow users to exploit the knowledge represented in the system. Such tools include facilities for finding, sharing, summarizing, visualizing, browsing and organizing knowledge. For example, QuizRDF [10] is a semantic search engine for browsing and querying RDF-annotated information resources. Spectacle [11] is a visualization and browsing tool for ontology-based information. OntoShare [12] is an RDF-based system, which supports knowledge sharing between users, using semantic web technology to create an ontology-based information resource automatically from the information so shared.

(Semi-) automatic annotation systems: These include systems such as AeroDAML [20], SemTag [21], SCREAM [22], PANKOW [23], C-PANKOW [24], KIM [25] and MnM [26]. These systems fundamentally make use of NLP (natural language processing) techniques to extract references in the text to certain concepts described in ontologies. They generally require the input of seed patterns or corpuses of documents in order to train the system. With regard to the kind of resources that can be annotated, most of the approaches are centred on the annotation of text resources.

III.2. Software agents exploiting ontologies The semantic web can utilize a variety of software agents to enhance various processes [7]. For example, an agent operating on the semantic web might undertake many of the routine (e.g., administrative) tasks that currently consume large amounts of user’s time. On the other hand, intelligent agents can assist users in finding Copyright © 2007 Praise Worthy Prize S.r.l. - All rights reserved

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Recently, there has been a growing interest in the annotation of non-textual resources. In particular, semantic annotation approaches have been propose for the annotation of images [27], audio [28] and multimedia [29]). The W3C has started a Multimedia Annotation on the Semantic Web Task Force as (http://www.w3.org/2001/sw/BestPractices/MM/) part of the Semantic Web Best Practices and Deployment Working Group. The task force’s target audience includes institutions and organizations with research and standardization activities in multimedia, including professional (museums, libraries, audiovisual archives, media production and broadcast industry, and image and video banks) and nonprofessional (users) multimedia annotators [30]. On the other hand, researchers have proposed approaches where the information to be annotated is a service [31]. Therefore, semantic annotation is following the trends in semantic web research by not only covering aspects of the classical web but also more innovative aspects such as Web Services or GRID (Global Resource Information Database) Services.

namespace of WordNet (http://wordnet.princeton.edu/). ƒ

1.6.

indexes Swoogle (http://swoogle.umbc.edu) millions of semantic web documents (including tens of thousand of ontologies). It enables users to search ontologies by specifying constraints on document metadata such as document URLs, defined classes/properties, used namespaces, and RDF encoding. Moreover, it provides detailed metadata about ontologies and classes/properties in an object oriented fashion. It has an ontology dictionary that enables users to browse the vocabulary (i.e. over 150KB URI refs of defined/used classes and properties) used by semantic web documents, and to navigate the semantic web by following links among classes/properties, namespace and RDF documents. In addition, it is powered by automatic and incremental semantic web document discovery mechanisms and updates statistics about the use of ontologies in the semantic web on a daily basis.

IV.2. Semantic browsing, querying and searching

IV.3. Semantic Web mining

Semantic browsing locates metadata and assembles point-and-click interfaces from a combination of relevant information: It allows easy navigation through resources, since users with any level of computing knowledge may use it. Semantic browsers such as Magpie [32] use domain ontologies to identify important concepts in a document and provide access to relevant material. Semantic search [33] enhances current search engines with semantics: It goes beyond keyword matching by adding semantic information, thus allowing easy removal of non-relevant information from the result set. Besides, semantic ranking is useful in those cases when too many results are returned. Semantic search is provided by tools, such as the Ontobroker that provides an ontology-based crawling and answering service [34], [35]. Ontobroker comprises languages and tools that allow to semantically mark-up content on web pages and let the user semantically query the web taking advantages of semantic inferences. It is based on: a) the use of ontologies that guide the semantic mark-up of web documents; b) the querying interface and c) the semantic rules for the domain. Hereafter, we present two other powerful tools for semantic search. ƒ SemanticWeb Search provides an (http://www.semwebcentral.org/) object oriented view of the semantic web, i.e. it indexes instances of well-known classes including rdfs:Class, rdf:Property, foaf:Person, and rss:Item. It partially supports ontology search by finding instances of rdfs:Class and rdf:Property. However, its search results are biased to terms from the

The application of data mining techniques to the content, structure, and usage of web resources is called web mining. It is the nontrivial process of identifying valid, previously unknown, and potentially useful patterns in the huge amount of these web data, patterns that describe them in concise form and manageable orders of magnitude. Semantics can be utilized for web mining for different purposes. The major application area is content mining, i.e., the explicit encoding of semantics for mining the web content. An important group of techniques, which can be easily adapted to semantic web content/structure mining, are the approaches discussed as (Multi-) Relational Data Mining (formerly called Inductive Logic Programming/ILP) [36]. A state of the art survey for semantic web mining is given in [37].

V.

Ontology Engineering

The development and maintenance of complex ontologies requires new techniques and tools to be developed. This is the purpose of what is known as “ontology engineering”. There are four typical steps (shown in Fig. 3) in managing ontologies: (1) create, (2) publish, (3) extend, and (4) reason. The steps “publish” and “extend” are optional. We have also two common scenarios (Fig. 3) in applying ontologies: populating instances of ontologies, and integrating information encoded by different ontologies. There are several different proposals for defining appropriate methods and methodologies for the development of ontologies. Among the most important

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are: METHONTOLOGY Knowledge [39].

[38],

and

based applications will evolve over time, as will the ontologies that they use.

On-To-

V.1.

Ontology mapping

Multiple ontologies need to be accessed from several applications. Mapping could provide a common layer from which several ontologies could be accessed and hence could exchange information in semantically sound manners. Ontologies offer a promising infrastructure to cope with heterogeneous representations of web resources. Data heterogeneity is solved, if semantic reconciliation with respect to the ontology is provided between the different information systems. This kind of interoperability can be addressed through ontology mapping [43]. This is a process whereby two ontologies are semantically related at conceptual level, and the source ontology instances are transformed into the target ontology entities according to those semantic relations. An interesting approach to ontology mapping has been taken in the GLUE system [44]. Madhavan et al. [45] developed a framework that enables mapping between models in different representation languages without first translating the models into a common language. Their framework uses a helper model, when it is not possible to map directly between a pair of models. The models represented in their framework are representations of a domain in a formal language, and the mapping between models consists of a set of relationships between expressions over the given model. Their framework was applied in an example case with relational database model. They also defined a typology of mapping properties: query answerability, mapping inference, and mapping composition.

Fig. 3. Typical steps in managing and applying ontologies

In ontology engineering, there are research activities in progress, among which are: • Tools supporting the development of ontologies. Most of these tools include an ontology editor with a user-friendly GUI that allows users to view the class hierarchy of the ontology and insert/modify/delete classes, properties, instances and/or axioms. Some of them also provide support to other phases of the ontology life cycle, such as evolution, documentation, evaluation, etc. Among the most widely used ontology editors that support RDF, RDF Schema and/or OWL are Protégé (http://protege.stanford.edu/publications/UserGuide .pdf), InferEd (http://www.intellidimension.com/pages/site/produ cts/infered/default.rsp), WebODE (http://webode.dia.fi.upm.es/WebODEWeb/index.h tml) and OilEd (http://oiled.man.ac.uk/). A comprehensive survey on ontology tools is given in [40]. • Evaluation and quality measures of ontologies. In ontology engineering, we need mechanisms to evaluate the ontologies that we are going to use. Ontology evaluation includes a number of different aspects, such as checking that the ontology is consistent (that it cannot reach contradictory conclusions), that it models the domain properly, that it contains no redundancies, that it is easy to maintain, etc. Some relevant approaches to ontology evaluation can be found at [41], [42]. • Support for ontology maintenance and evolution. As in any other software system, semantic web-

V.2.

Ontology alignment and merging

Ontology alignment is the task of establishing a collection of binary relations between the vocabularies of two ontologies. Since a binary relation can itself be decomposed into a pair of total functions from a common intermediate source, we may describe the alignment of two ontologies O1 and O2 by means of a pair of ontology mappings from an intermediate source ontology O0. Ontology merging defines the act of bringing together two conceptually different ontologies or the instance data associated to two ontologies (see Fig. 4). This merging process can be performed manually, semiautomatically, or automatically. Manual ontology merging although ideal is extremely labour intensive and current research attempts to find semi or entirely automated techniques to merge ontologies. These techniques are statistically driven often taking into account similarity of concepts and raw similarity of

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ƒ

instances through textual string metrics and semantic knowledge.

ƒ

Populating an existing ontology/structure. Dealing with dynamic data streams.

VI.

Inference engines support the languages being defined for the semantic web and allow for the processing of knowledge available in the semantic web. They deduce new knowledge from already specified knowledge. Inference engines are categorized to general logic based inference engines, and specialized algorithms (Problem Solving Methods). Using the first approach one can distinguish between different kinds of representation languages such as Higher Order Logic, Full First Order Logic, Description Logic, Datalog and Logic Programming. The most prominent and capable commercial inference engine is Ontobroker that is based on Frame Logic. Ontobroker is tightly integrated with the ontology-engineering environment OntoEdit and provides connectors to typical databases. FaCT (Fast Classification of Terminologies) is one of the most prominent Description Logics based inference engines that can be used for modal logic satisfiability testing. A famous RDF development environment that incorporates an inference engine is SESAME (http://www.openrdf.org/) [50]. Jena (http://jena.sourceforge.net/index.html) is another semantic web development environment for applications that includes RDF Schema and OWL Lite reasoners (OWL Lite is an OWL subset). A very important subset of OWL is OWL-DL, in which DL means Description Logics, a type of logic with less expressiveness than first-order predicate logic, but one over which it is possible to perform reasoning tasks more efficiently. Pellet is an example of an OWL-DL reasoner (http://www.mindswap.org/2003/pellet/index.shtml). It is worth mentioning that the RDF schema and OWL ontology languages have limited capabilities for logical reasoning. For this reason, a number of proposals for rule languages for the semantic web have been put forward. One of the first proposals was TRIPLE [51], which is a rule language based on Horn clauses on top of RDF and an inference engine capable of reasoning about models defined in TRIPLE. Another significant initiative is RuleML (Rule Markup Language), which is an XML language for defining rules intended for the semantic web. It contains aspects logical programming, functional programming and object orientation. The designers of RuleML have submitted a language called SWRL (Semantic Web Rule Language) for consideration as a W3C standard. SWRL is a combination of OWL-DL and a subset of RuleML. An important issue when designing a system based on semantic web technologies is exactly how logical reasoning capabilities are to be used. Most logical reasoning languages with high expressiveness are

Fig. 4. The result of ontology merging

Noy and Musen [46] have developed a series of tools over the past three years for performing ontology mapping, alignment and versioning. These tools are SMART [46] and PROMPT [47]. In the OntoMerge system [48] (which was developed for semantic integration on the semantic web) Dou et al. [48] use a general-purpose inference engine to enable translation between mapped ontologies. In OntoMerge the correspondence between two ontologies is expressed as a set of bridging axioms relating classes and properties of the two source ontologies (Fig. 5).

Fig. 5. Bridge ontology

V.3.

Ontology Reasoning Tools

Ontology learning

Extracting an ontology from the web is a challenging task. In machine learning terminology we refer to the ontology construction task as “ontology learning” [49]. In ontology learning, ontologies are built (semi) automatically using various (e.g., NLP) techniques. The results obtained by these systems normally need to be debugged manually before they can be used. Ontology learning exploits many existing resources including texts, thesauri, dictionaries, and databases. It builds on techniques from web content mining, and it combines machine-learning techniques with methods from fields like information retrieval and agents, applying them to discover the ‘semantics’ in the data and to make them explicit. We define an ontology just as another class of models (slightly more complex compared to usual Machine Learning models), which needs to be expressed in some kind of hypothesis language. This definition of ontology learning includes the following sub-problems, which are relevant in different contexts: ƒ Learning just the concepts. ƒ Learning just the relationships between the existing concepts. ƒ Learning both the concepts and relations at the same time. Copyright © 2007 Praise Worthy Prize S.r.l. - All rights reserved

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characterized by exponential complexity (as is the case of OWL-DL) or are undecidable (as in the case of FirstOrder Predicate Logic). Consequently, the use of inference engines for large-scale knowledge bases is not feasible today. To overcome this problem a number of approaches are open to us, all of which involve limiting the use of inference engines in our systems. One frequently adopted solution is to use simple ontologies combined with a reasoner to expand class hierarchy queries. The expanded query is finally processed by a conventional relational database, which stores the instances. This type of reasoning can be implemented efficiently and therefore has no scalability issues. Other approaches are based on “approximate reasoning” i.e., reasoning algorithms that do not return 100% of the results but perform much better. Efficient ontology language processors (reasoners) are: ƒ OWLJessKB(http://edge.cs.drexel.edu/assemblies/ software/owljesskb/) ƒ Java Theorem Prover (JTP) that (http://www.ksl.stanford.edu/software/JTP/) supports both forward and backward chaining inference using RDF/RDFS and OWL semantics. ƒ Jena (http://jena.sourceforge.net/) is a popular open-source project. It provides sound and almost complete (except for blank node types) inference support for RDFS. ƒ F-OWL (http://fowl.sourceforge.net/) is an inference engine, which is based on Flora-2. ƒ FaCT++ (http://owl.man.ac.uk/factplusplus/) is the descendent of FaCT reasoning system and it provides full support for OWL-Lite. ƒ Racer(http://www.sts.turburg.de/r.f.moeller/racer/) is a description logic based reasoner that supports inference over RDFS/DAML/OWL ontologies through rules explicitly specified by the user. ƒ Pellet (http://www.mindswap.org/2003/pellet/) is a ‘hybrid’ DL reasoner that can deal both TBox reasoning as well as non-empty ABox reasoning. It is used as the underlying OWL reasoner for SWOOP ontology editor and provides in-depth ontology consistency analysis. ƒ TRIPLE (http://triple.semanticweb.org/) is a Horn Logic based reasoning engine and a language that uses many features from F-logic. ƒ SweetRules is a rule toolkit for RuleML. (http://sweetrules.projects.semwebcentral.org/)

carry out intelligent reasoning behind the scenes. They should offer semantic services including semantic-based browsing, semantic search and smart question answering. Knowledge portals provide views onto information on the web, thus facilitating their users to find relevant specific information. There are various approaches for using ontologies to guide the development of semantic web portals. However, two are the main different approaches: • The knowledge annotation initiative of knowledge acquisition [also known as (KA)2 project [39] initiative] is an example of a semantic portal guided by ontologies. In (KA)2 project, a community web portal was deployed. It was based on ontology as a semantic backbone for accessing information on the portal, for contributing information, as well as for developing and maintaining the portal. • The Ontoportal (http://ontoportal.org/uk) uses ontological hypermedia principles to enrich the linking between concepts within a research community, allowing researchers to not only position a concept within the context of the entire community in which they work but more importantly, pose intricate research queries. portal (http://km.aifb.uniThe KAON Karlsruhe.de/kaon/Members/rvo/kaon_portal) is a tool for generating ontology-based web portals. To create the portal, the user needs to create an ontology containing the information, which will be presented on the web. Then, the KAON portal is used to provide default visualization and navigation through this ontology. There is also the SEAL (SEmantic portAL) portal that exploits semantics for providing and accessing information at a portal as well as constructing and maintaining the portal [52]. Lausen et al. [53] give a state of the art survey for semantic web portals.

VIII. Semantic Web Services Semantics can be used in the discovery, composition and monitoring of web services [54]. The semantic web services purpose is to describe web services’ capabilities and content in a computer-interpretable language and improve the quality of existing tasks, including web services discovery, invocation, composition, monitoring, and recovery. They have major impact on industries as they allow the automatic discovery, composition, integration, orchestration, and execution of inter-organization business logic, making the Internet become a global common platform [55]. It is worth noticing that Sakkopoulos et al. [56] proposed techniques to facilitate semantic discovery and interoperability of web services that manage and deliver web media content.

VII. Semantic Web Portals The development of the semantic web is facilitated by the development of web portals guided by ontologies. A knowledge (semantic web) portal can be seen as a web application providing access to data in a semantically meaningful way, making available a variety of resources for diverse target audiences. Differently from “dumb” web portals, semantic portals are “smarter” and Copyright © 2007 Praise Worthy Prize S.r.l. - All rights reserved

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VIII.1. Ontological description of Web Services

areas for semantic web technologies: knowledge management, B2C Web commerce, and B2B ebusiness. This section provides some prototypical examples for such applications.

Web Service Modeling Ontology (WSMO) [31] and OWL-S (http://www.daml.org/services/owls/1.1B/) can provide the infrastructure for ontological description of web services. Especially, WSMO can describe semantic web services to solve the integration problem. The WSMO describes all relevant aspects related to services with the ultimate goal of enabling the automation of the tasks involved in integration of web services. These tasks are: discovery, selection, composition, mediation, execution, monitoring, etc. WSMO has its conceptual basis in the Web Service Modeling Framework (WSMF) [57] refining and extending this framework and developing a formal ontology and set of languages. The OWL-S is an ontology for service description based on the OWL. It can facilitate the design of semantic web services and can be considered as “a language for describing services, reflecting the fact that it provides a standard vocabulary that can be used together with the other aspects of the OWL description language to create service descriptions”. The OWL-S ontology consists of the following three parts: ƒ A service profile for advertising and discovering services; ƒ A process model that describes the operation of a service in detail; ƒ The grounding that provides details on how to interoperate with a service, via messages.

The On-To-Knowledge (www.ontoknowledge.org) builds an environment for knowledge management in large intranets and web sites. Unstructured and semi structured data are automatically annotated, and agentbased user interface techniques and visualization tools help the user navigate and query the information space. On-To-Knowledge continues a line of research that was initiated with SHOE and Ontobroker [35]: using ontologies to model and annotate the semantics of information resources in a machine-processable manner. The developers of On-To-Knowledge are carrying out three industrial case studies—with SwissLife British Telecom (http://www.swisslife.ch), (http://www.bt.com/innovations), and Enersearch—to evaluate the tool environment for ontology-based knowledge management. In this context, CognIT(http://www.cognit.no) extended its information extraction tool Corporum to generate ontologies from semi-structured or unstructured natural-language documents. Important concepts and their relationships are extracted from these documents and used to build up initial ontologies. An application of the semantic web technology in the B2C area has been developed by Semantic Edge (http://www.semanticedge.com) that offers front-end voice-based and natural language access to distributed and heterogeneous product information. The technology will enable the human user, instead of manually browsing large volumes of product information, to ask simple questions. Finally, the B2B area may become the most important application area of Semantic Web in terms of the market volume. Companies like VerticalNet (http://www.verticalnet.com) which builds many vertical marketplaces, or ContentEurope which provides (http://www.contenteurope.com) content management solutions for B2B electronic commerce, all face the same problem: integrating heterogeneous and distributed product information. Obviously such companies make use of ontology-based integration techniques to reduce the level of effort required to provide integrated solutions for B2B marketplaces. Finally, in other application domains there are research projects, which combine semantic web with P2P technologies. For example, in the tourism domain the LA_DMS project [62] provides semanticbased information for tourism destinations by combining the P2P paradigm with semantic web technologies.

The vocabulary defined by OWL-S may be used to provide semantic annotations of services, and automatic agents may process this information. Other major initiatives are METEOR-S, and IRS-II. METEOR-S (http://lsdis.cs.uga.edu/Projects/METEOR-S/) aims at integrating WS technologies, such as Business Process Execution Language for Web services (BPEL4WS) [58], WSDL and UDDI with semantic web technologies, in order to automate the tasks of publication, discovery, description, and control flow of Web services. The Internet Reasoning Service II (IRSII) [59] is a Semantic Web Services framework, which allows applications to semantically describe and execute Web Services. Compared to IRS-II, WSMO focuses more on the description elements that are needed to deal with Semantic Web Service. Conceptually, WSMO and IRS-II are not too different in the sense that both have common roots in UPML [60]. IRS-II and WSMO are expected to converge as future versions of IRS, which are planned to be WSMO compliant.

IX.

Ontology-based Applications and Projects

According to Gargantilla and Gómez-Pérez [61], there are many ontology-based applications in e-commerce, knowledge management, information sharing and elearning. However, there are three major application

IX.1. Personalized applications In the semantic web, personalized information delivery based on user and document profiling is an

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important step in providing relevant and timely knowledge to the right people. User profiles, which model the user’s information requirements, are central to personalized information delivery. User profiles can be automatically constructed from different data sources using a variety of techniques including content-based user profiling and collaborative user profiling. Contentbased user profiling is applied on problems involving text documents where the content analysis of the document text is performed in order to construct a profile. Collaborative user profiling is based on the assumption that “similar users have similar preferences”. In other words, by finding users that are similar to the active user and by examining their preferences, the recommender system can predict the active user’s preferences for certain items a provide a ranked list of items which the active user will most probably like. There are various methods for automatic creation of user profiles and their representation in ontologies. These methods are applied to personalized applications such as ontology-based search and browse. The “Friend-of-A-Friend” (FOAF) project (http://www.foaf-project.org) represents social networks and information about user profiles (people) in a machine processable way. The FOAF project is highlighted by the following features: ƒ Publishing personal profile with better visibility. ƒ Enforcing unique person identity reference on the Web and thus supporting the merge of partial data from different sources. ƒ Representing and facilitating large scale social networks on the Web. The Flink system is used for the extraction and analysis of online social networks [63]. It employs semantic web technology for reasoning with “personal” information extracted from a number of information sources including web pages, emails, etc. In e-learning, Henze et al. [64] propose a framework for personalized e-learning and show how the semantic web resource description formats can be utilized for automatic generation of hypermedia structures. A methodology for developing learning ontologies and representative examples of ontology-based applications or projects in e-learning are provided in [65]. In addition, Kanellopoulos et al. [66] provide a state-ofthe-art literature review in the educational semantic web and consider what the semantic web can do for adaptive web-based educational systems. In e-tourism, a semantic-based architecture [67] has been proposed. In this architecture a semantic web ontology is used to model the tourism destinations, user profiles, and contexts. The semantic web service ontology (OWL-S) is extended for matching user requirements with tourism destination specifications at the semantic level, with context information taken into account. The SWRL is used for inferencing with context and user profile descriptions. This architecture

enables tourism destination management systems (DMS) to become fully adaptable to user’s requirements concerning tourism destinations. Finally in the e-Airlines domain, an intelligent web portal [68] has been designed and developed that helps people to find airline seats that match their personal travelling preferences. For this purpose, the knowledge of the airlines travelling domain has been represented by means of ontology, which has been used to guide the design of the application and to supply the system with semantic capabilities. Hereafter, we provide a short summary of the IST projects and excellence networks on the semantic web. IX.2. Major research projects and excellence networks

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Common Multimedia Ontology Framework, http://www.acemedia.org/acemedia/reference/multi media_ontology/

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COG Corporate ontology grid (CPA9) Collaborative ontology development in a corporate environment (automotive industries); automatic scripting for transformation and query; creating 'virtual views' of data from disparate sources. (http://www.cogproject.org/)

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CoMMA - Corporate Memory Management through Agents (KA2) Multi-Agent System, based on semantic enterprise and user models, and ontologies. This system is applied to Corporate Memory Management using techniques for learning from user behaviour. http://www.ii.atosgroup.com/sophia/comma/Home Page.htm

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DIP (Data, Information, and Process Integration with Semantic Web Services): http://dip.semanticweb.org/

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ESPERONTO Application Service Provision of Semantic Annotation, Aggregation, Indexing and Routing of Textual, Multimedia, and Multilingual Web Content (KA3). It focuses on “legacy” web content and develops ontologies to support multimedia and multilingualism. (http://esperonto.semanticweb.org)

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FF-POIROT Financial Fraud Prevention-Oriented Information Resources using Ontology Technology (KA3) Interactive construction of multilingual ontologies through domain modeling, (automatic) text-mining and (semi-automatic) validation and alignment, as a basis for Semantic Web services for knowledge storage, management, retrieval and sharing. (http://www.starlab.vub.ac.be/research/projects/def ault.htm#Poirot)

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HtechSight: A knowledge management platform with intelligence and insight capabilities for technology intensive industries (KA2) utilizes standardized knowledge representation frameworks to facilitate interoperability, and contributes to the development of the next generation of Webenabled Knowledge Management platforms. (http://banzai.etse.urv.es/~htechsight/). IBROW An Intelligent Brokering Service for Knowledge-Component Reuse on the World-Wide Web (FET) Configures distributed, heterogeneous applications using pre-existing components (ontologies and problem solving methods - for information filtering, automatic classification and design problems) retrieved from distributed digital libraries. (http://www.swi.psy.uva.nl/projects/ibrow/home.ht ml)

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InDiCo Integrated Digital Conferencing (KA3) Develops semantics-based multimedia indexing and browsing methods for conference and distance learning applications (http://indico.sissa.it/)

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KnowledgeWeb: http://knowledgeweb.semanticweb.org/

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MONET Mathematics on the net (KA3) Develops and applies ontologies for mathematics services description, querying, explanation and use. (http://monet.nag.co.uk)

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MOSES A modular and scalable environment for the Semantic Web (CPA9) Focuses on the scalability and linguistic aspects of ontology construction and evolution through content (mainly text) structure analysis.

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NEWS: http://www.news-project.com

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OBELIX: Ontology-Based ELectronic Integration of compleX products and value chains (KA2). Develops an ontology tool suite for smart collaborative e-business, enabling scaleable integration and interoperability in dealing with complex products and services, supply chains and value networks, and associated electronic market transactions. (http://www.cs.vu.nl/~obelix/)

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supported information (http://www.ontoknowledge.com)

GRACE Grid Search and Categorization Engine (CPA9) Develops a decentralized search and categorization engine for unstructured textual information; builds on-top of Grid technology (peer-to-peer), uses locally computed indexes. (http://pertinax.cms.shu.ac.uk/projects/cmslb2/)

On-To-Knowledge: Content-driven knowledge management through evolving ontologies (KA4) Design of languages (OIL) and implementation of tools for ontologies, for automatic derivation of semantics of semi-structured data (text-mining), knowledge maintenance, view definitions and agent Copyright © 2007 Praise Worthy Prize S.r.l. - All rights reserved

access.

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ONTO-LOGGING Corporate Ontology Modelling and Management System (KA2) Distributed formalisation of corporate ontologies; dynamic optimisation using agent technology for user modelling and category extraction. (http://www.ontologging.com)

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OntoWeb: http://ontoweb.aifb.uni-karlsruhe.de/

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REWERSE (Reasoning on the Web with Rules and Semantics): http://rewerse.net/

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SCULPTEUR: Semantic and content-based multimedia exploitation for European benefit (KA3) Constructs a semantic layer enhancing search in distributed digital libraries of images of 3D objects, by linking low and high-level features; implementing agents for classification and search of structured and unstructured content. (http://www.sculpteurweb.org)

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SEKT: (Semantically Enabled Knowledge Technologies): http://www.sekt-project.com/

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SEWASIE: Semantic Webs and agents in integrated economies (KA3) Designs a distributed agent architecture for semantic search and inferencing based on multilingual ontologies. (http://www.sewasie.org/)

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SPACEMANTIX Combining spatial and semantic information in product data (KA3) Enriches 3D graphics in product catalogues with semantic information (e.g. assembling instructions) for easy and natural access to and manipulation of 3D models. (http://www.agc.fhg.de/uniGoethe/forschung/Space mantiX/)

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SPIRIT Spatially-aware information retrieval on the Internet (KA3) Derives/extracts ontology based geo-metadata from Web pages and digital map datasets, for spatially-aware search engines. (http://www.researchprojects.unizh.ch/math/unit70600/area20/p2563.ht m)

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SWAD-Europe W3C Semantic Web advanced development for Europe (KA3) Informs W3C work on new “Semantic Web” recommendations, through research, open source implementation and testing (http://www.w3.org/2001/sw/Europe/).

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SWAP Semantic Web and Peer-to-Peer (KA3) Realises a Semantic Web based peer-to-peer system, building on available Open Source peer-topeer solutions, for sharing individual views on knowledge through emergent semantics. (http://swap.semanticweb.org)

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SWWS Semantic Web enabled Web services (KA3) Develops semantic means for describing, recognizing, configuring, combining, comparing and negotiating Web services, supporting Web service discovery and scalable mediation. (http://swws.semanticweb.org)

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VICODI Visual Contextualisation of Digital Content (KA3) Provides mechanisms for contextualising distributed multilingual digital content (European history), taking into account topics (category, hierarchy), location and time, through semantic indexing and ontological markup and using neural classifiers; development of a suitable SVG-based visualization interface.

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WIDE Semantic web-based information management and knowledge sharing for innovative product design and engineering (KA3) Integrates, using Semantic Web technologies, proprietary inhouse databases, off-line and on-line catalogues, and the World Wide Web to support the information and knowledge sharing needs of industrial designers and product engineers. (http://www.cefriel.it/topics/research/index.xml?tid =27)

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WISPER Worldwide intelligent semantic patent extraction & retrieval (KA3) Automates semantic mark-up of structured and multi-lingual digital content (patents), in support of searching and visualising search results.

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WonderWeb Ontology infrastructure for the Semantic Web (FET) Analyses requirements for large-scale deployment of ontologies: ontology languages, semantic integration, migration, reconciliation and sharing of ontologies, foundational ontologies, tool support (for editing, integrating and extracting ontologies), ontology server architectures and services such as persistent storage and reasoning support. (http://wonderweb.semanticweb.org)

X.

terms of communications and security) and modify its behaviour, if required to ensure continuous operation as desired by its end-users. A design-solution to this problem is to describe the state of each network node and specifies actions that a node should take in each state by a comprehensive OWL ontology that views a network node as having multiple components (security, communication terms). Such a ‘network ontology’ could enable a network node to determine its state. An incorporated intelligent network mechanism could reason over a given instance of a node and respond with a value that indicates the state of the node. To summarize, ontologies will play an important role in many industries as they promise a shared and common understanding of concepts that reaches across people and application systems. Delivering the semantic web to many application domains depends upon: the syntactical and semantic mark-up of content; the development of better knowledge analysis and modeling tools; the widespread adoption of interoperable knowledge representation languages; and the construction of suitable ontologies. Yet, proof and trust are central research issues that must be solved. In addition, we are faced with the difficulties of ensuring and maintaining semantic integrity and a lack of methods for testing its presence. The requirements of intelligent information systems raise several technical research issues, such as: semantic interoperability and mediated architectures; e-business frameworks supporting processes across organizations [69]; mobility and embedded intelligence; personalization and contextbased services; and information-to-knowledge transformations – data mining and knowledge management. To this direction, ontology languages and rules are evolving. So, we have the OWL 1.1. (http://owl1_1.cs.manchester.ac.uk) and advances in Rule Interchange Format (RIF) (http://www.w3.org/2005/rules/). According to Kim [70] the ontology community must place emphasis on: (a) designing an ontology development tool demonstrated to be useful and usable to a knowledge worker, who is not a knowledge representation expert, and (b) development of decentralized and adaptive ontologies, which have value in and of themselves, but whose full potential will only be realized if they are used in combination with other ontologies in the future to enable data sharing. The immediate value may be use of ontologies for software specification. Finally, not to forget to mention the synergy of the semantic web with the Web 2.0 [71]. The core characteristic of Web 2.0 is that a website is a dynamic platform upon which users can generate their own experience. The richness of this experience is powered by the implicit threads of knowledge that can be derived

Conclusions

This survey has presented major approaches that have been used for representing and managing knowledge in the semantic web. Many industries such as e-learning, e-government, e-tourism, e-commerce etc. are facing rapid changes with the advent of the semantic web technologies. Now, there is the need for developing an infrastructure to manage the online information and deliver to users new intelligent services. New superior services (e.g., market overview and price comparison in e-commerce) can be deployed. Besides, novel designs for communication networks could be achieved. For example, we could design a multimedia communication network that can determine its current state (e.g., in Copyright © 2007 Praise Worthy Prize S.r.l. - All rights reserved

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and the semantic web, Springer Verlag, 2004. ISBN 1852335513.

from the content supplied by users and how they interact with the site. Web 2.0 platforms provide users with access to their data through well-defined APIs and thus encourage new uses of that data. Well-known Web 2.0 platforms include Flickr (http://www.flickr.com), wikipedia and Yahoo Maps.

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Steffen Staab, Rudi Studer. Handbook on Ontologies. Heidelberg: Springer Verlag, 2004. ISBN 3-540-408347.

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Franz Baader, Peter Patel-Schneider, Diego Calvanese, Deborah L. McGuinness, Daniele Nardi. The Description Logic Handbook: Theory, Implementation, and Applications, Cambridge University Press, 2003. ISBN 0521781760.

Appendix A: Useful References Websites ƒ ƒ

OMWG (Ontology Management Working Group): http://www.omwg.org/

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Semantic Web ORG: http://semanticweb.org/

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Semantic Web Science Association: http://www.iswsa.org/index.html

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SWSI (Semantic Web Services Initiative): http://www.swsi.org/

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W3C (World Wide Web Consortium): http://www.w3.org/

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Ontologies

AIS SIGSEMIS (Semantic Web and Information Systems): http://www.sigsemis.org/

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W3C Semantic Web: http://www.w3.org/2001/sw/

EuroWordNet: http://www.illc.uva.nl/EuroWordNet/ KIMO (Knowledge and Information Management Ontology): http://www.ontotext.com/kim/kimo.rdfs

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MILO (MId Level Ontology): http://www.ontologyportal.org/

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OpenCyC: http://www.cyc.com/opencyc PROTON (PROTO ONtology): http://proton.semanticweb.org/

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SUMO (Suggested Upper Merged Ontology): http://www.ontologyportal.org/

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TAP Knowlege Base: http://tap.stanford.edu/ WordNet: http://wordnet.princeton.edu/

Journals ƒ

Commercial activity

Journal of Web Semantics, Elsevier: http://www.elsevier.com/wps/find/journaldescription.cws _home/671322/description

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IEEE Intelligent Systems, IEEE: http://www.computer.org/portal/site/intelligent/

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Applied Ontology, IOS Press: http://www.iospress.nl/html/15705838.php

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International Journal of Knowledge and Learning, Inderscience: https://www.inderscience.com/browse/index.php?journal ID=42

The leading technology providers for semantic web technologies are: ƒ Ontoprise GmbH (http://www.ontoprise.com) ƒ WebMethods (http://www.webmethods.com) ƒ Ontology Works (http://www.ontologyworks.com/) ƒ Intellidimension (http://www.intellidimension.com)

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John Davies, Dieter Fensel, Frank van Harmelen. Towards the Semantic Web: Ontology-Driven Knowledge Management, John Wiley & Son, 2003. ISBN 0-47084867-7.

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Dieter Fensel, Wolfgang Wahlster, Henry Lieberman, James Hendler. Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential. The MIT Press, 2002. ISBN 0-262-06232-1.

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Grigoris Antoniou, Frank van Harmelen. A Semantic Web Primer, The MIT Press, 2004. ISBN 0-262-01210-3.

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Authors’ information 1

Educational Software Development Laboratory (ESDLab), Department of Mathematics, University of Patras, Greece. 2 Educational Software Development Laboratory (ESDLab), Department of Mathematics, University of Patras, Greece. Dimitris N. Kanellopoulos received a B.E. degree in Electrical Engineering from the Department of Electrical and Computer Engineering of the University of Patras, Greece, in 1990. He holds a Ph.D. degree in multimedia communications from the same Dept. He is a member of the Educational Software Development Laboratory (ESDLab) in the Dept. of Mathematics at the University of Patras. His research interests include multimedia communications, intelligent information systems, knowledge representation, web engineering, and web-based education. He has published more than 70 technical papers in refereed journals and conference proceedings. He has served on the program committees of various international conferences. He serves on the editorial boards of various academic journals. Sotiris B. Kotsiantis received a diploma in mathematics, a Master and a Ph.D. degree in computer science from the University of Patras, Greece. He is an adjunct lecturer in the Department of Computer Science and Technology at the University of Peloponnese, Greece. His main research interests are in the field of machine learning, data mining and knowledge representation. He has more than 60 publications to his credit in international journals and conferences.

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