Ontology Semantics and Applications - CiteSeerX

Ontology Semantics and Applications Panorea Gaitanou Benaki Museum. Library, Athens, Greece [email protected]

Abstract. This paper presents the widespread use of ontologies in many areas. One of the most important is Semantic Web development. The existence of numerous search engines, which produce results simply by using keywords, is an ineffective method of finding the requested information. With the use of ontologies, intelligent search could be performed, based on semantics and concepts. Furthermore, in the e-commerce field, it is explicitly necessary to integrate and combine applications from different enterprises and indefinable single web entities. Moreover, in the multimedia and graphics field, ontologies can be expanded from their original form and each concept can be described as a multimedia entity. Multimedia ontologies could help in content indexing and optimization, in knowledge sharing and learning based applications and can provide reasoning services. In Peer-to-peer networks and grid technologies, ontologies can facilitate the interoperability among computer systems. Finally, in Pervasive (or Ubiquitous) Computing Environments, ontologies can address several issues concerning the development and management of such systems. Keywords: knowledge management, ontologies, grid technologies, pervasive computing, e-commerce, multimedia

1. Introduction

In recent years, ontologies have moved from the AI (Artificial Intelligence) research community into real-word applications in a number of domains. In many disciplines, standardized ontologies have been developed, so that domain experts can use them to share and annotate information in their fields. Ontology is defined as a formal explicit specification of a shared conceptualization [18]. In other words, it is a shared understanding of some domain interest, which is often realized as a set of classes (concepts), relations, functions, axioms and instances. Fensel, in an attempt to analyze the complex definition given by Gruber, emphasizes on the four basic components: “conceptualization” is an abstract model of a phenomenon in the world; an ontology is “formal” because it should be machine readable and “explicit” because the concepts used and the constraints on their use are explicitly defined; finally it is “shared” because there is an agreement between those who use the ontologies [19]. Ontologies try to capture the semantics of domain

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expertise by deploying knowledge representation primitives enabling a machine to understand the relationships between concepts in a domain [17]. They are complex knowledge representation artefacts intended for the development of intelligent applications and also social constructions for communication and crystallisation of domain-specific knowledge [28]. This paper is a result of a thorough search and synthesis, aiming to provide an overall view of the application fields where ontologies have already been deployed, and to present the benefits from their use in each field. In the following paragraphs, ontology applications in the fields of semantic web, knowledge management, ecommerce, multimedia and graphics, grid computing, pervasive computing environments are discussed, and in each field, an effort is made to outline the basic ontology features that deal with real or potential issues that without the use of ontologies, would possibly remain unsolved.

2. Ontology application fields

2.1 Semantic Web and knowledge management The World Wide Web provides direct access to 3 billion pages, thus offering a wide range of information sources. However, studies have shown that these static pages represent approximately 20% of the actual quantity of information that is available over the web. Therefore, it is obvious that plain browsing is an ineffective search method, for such an information size. This is the reason for the development of hundreds of search engines, which operate by using keywords for the support of the information search in the web (e.g. Google, AltaVista, Yahoo!, etc.). These engines usually consist of a retrieval mechanism of the available sources in the web, an indexing mechanism, which exports the keywords of each object/item, and a query interface, which accepts the user queries and forwards them to the database. These processes are generally acted out automatically. However, in many cases (according to each search engine’s policy) there is also human intervention in specific “problematical” spots. The search engines display results in the form of a list of hyperlinks and information (text and images) in which these links refer to. In order to retrieve this information, a lot of human interaction is demanded and this process adds remarkable delay. Moreover, these results cannot be machine translated automatically, but human intervention is required, to retrieve the results and convert them in a specific standard. On the whole, all issues regarding the existent search procedures, which could have been solved with the use of ontologies, are the following: • Information search: traditional systems base their search on a keyword, meaning that not only irrelevant items are retrieved (a word might be used in a different content, but also that desired content with different vocabulary cannot be retrieved). With the use of ontologies, intelligent search could be accomplished, based on the real sense and the concepts.

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• Information retrieval: Self-operating agents that are used until recently, don’t have the ability to integrate the information they meet, nor the capability to assimilate common sense understanding. On the other hand, the use of ontologies allows effective retrieval through specific queries, with notional content and intelligent data retrieval. • Maintenance: The maintenance of simply structured text sources is a hard, time consuming procedure. Ontologies could also offer in this domain, with document exchange through XSL translators. • Automatic document creation: This process which is required in adaptable websites that are modified according to the demands and preferences of each user, needs a representation of the information semantics in a way that can be easily machine perceptible. The only effective solution, regarding this issue, is the use of ontologies and the definition of views in the diverse files and information documents. Consequently, the importance of ontologies to express semantics is recognized in several areas, such as Semantic Web and Knowledge Management. These areas have a common problem: the continued rapid growth in information volume, which makes difficult to find, organize, access and maintain information. The use of ontologybased applications is being pointed as one of the most promising ways to deal with this problem [25]. The major challenge and research issue regarding Semantic Web is information integration. Information integration is defined as the field of study of techniques attempting to merge information from disparate sources despite differing conceptual, contextual and typographical representations1. Information integration provides the basis for a rich “knowledge space” built on top of the basic web “data layer”. This layer is composed of value-added services that offer abstracted information and mostly knowledge, rather than returning documents [24]. Some of the specific challenges in ontology integration are: finding similarities between different information systems in an automatic and a semi-automatic way, developing an ontology-integration architecture, composing mappings across different ontologies. In the Semantic Web, there will be multiple ontologies developed independently but will interact with one another. Thus, there must be a way for these mechanisms to facilitate interoperability and information sharing. In this section, we mention approaches that highlight the use of ontologies, their emphasis on knowledge sharing and their use in reasoning and make use of several general ontologies. The goal of these top-level ontologies is to have domain-specific ontologies extend them, providing at the same time the grounding in common vocabulary. Two of the ontologies designed with the purpose of being formal toplevel ontologies are the Suggested Upper Merged Ontology (SUMO) and DOLCE. SUMO is an effort by the IEEE Standard Upper Ontology Working Group that develops a standard upper ontology promoting data interoperability, information search and retrieval, automated inference and natural language processing. It was created by merging publicly available ontological content into a single, comprehensive and cohesive structure [29]. It provides axioms in first-order logic, describing properties of concepts and relations among them. Likewise, the DOLCE 1

Wikipedia: Information integration, http://en.wikipedia.org/wiki/Information_integration

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Ontology is a formal foundational ontology developed as a top-level ontology in the WonderWeb project2 (it includes a large number of European research groups). DOLCE aims at providing a common reference framework regarding WonderWeb ontologies, so as to ease information sharing. As far as it concerns its representation, it captures ontological categories underlying natural language and human common sense [10]. It is also worth to mention the Process Specification Language (PSL), which was developed at the National Institute for Standards and Technology. PSL is a formal language aimed at creating a neutral, standard language for process specification to serve as a neutral representation to integrate multiple process related applications throughout the manufacturing life cycle [23]. PSL works as a semantic integration tool and is based on the theory that, if two ontologies extend the same reference ontology in a consistent way, then it is much easier to find correspondence between their concepts. PSL facilitates the correct and complete exchange of process information among manufacturing systems such as scheduling, process modeling and process planning and is used as an interlingua for ontologies representing these different processes. Ontologies have been recognized as a fundamental infrastructure for advanced approaches to Knowledge Management automation. OntobUM and the LibrarianAgent application are applied in the context of Knowledge Management Systems. OntobUM is a user modeling system that relies on a user ontology, using Semantic Web technologies and is integrated in an ontology-based KM, called Ontologging. To be more specific, OntobUM is a user-modeling server, which stores data in a RDF/RDFS format and unlike other similar servers it uses semantics. It has been implemented as a web application using Java 2 and it uses the KAON-Karlsruhe Ontology and semantic Web framework as an API for managing ontologies [7]. The Librarian-Agent application is also an ontology-based approach for the improvement of searching and retrieving data in an information portal. It plays the role of a human librarian in a traditional library. The system realizes a library scenario in which users search for information in a repository. The application uses all possible information about the domain vocabulary, the behavior of previous users and the capacity of the knowledge repository, so that users can find the resources they need. Consequently, through an interactive interface, it guides them in more efficient searching and retrieving [14]. Finally, OpenCyc, an upper ontology, is implemented in many knowledge management systems. OpenCyc [32] is an open source version of Cyc Knowledge Base, which contains over one hundred thousands atomic terms and is provided with an associated efficient inference engine. It contains many formal definitions that are useful for knowledge management systems, such as time and date, terms descriptions and description of events and activities [33]. Researchers are actively working all these years on these challenges concerning information integration and specifically ontology integration. Nevertheless, we could support that they are only scratching the surface. Semantic Web requires significant new advances to make integration possible on the Web scale.

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http://wonderweb.semanticweb.org/

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2.2 Large scale applications - Machine translation The continuous increasing development of large-scale databases, which are used in linguistic applications, artificial intelligence applications, etc., creates the need for an integrated knowledge framework, in which the contents must be interrelated. In the new technologies field, where the use of computers is widely increased, it is practically impossible for a basic concept or entity to be represented in the same way in all systems that use it. For example, the concept “Person” is treated in a totally different way by a doctor (and his computer system), than an employment agency (and its system). When these systems are remote and segregated, there is no particular problem, humans do all the “translating”. However, when there is need to integrate all these systems, or if a simple data sharing is necessary, then the problem arises. Incommensurate views of the same object lead to incompatible systems and thereupon in lack of data sharing possibility. Thus, no knowledge can be transferred and there is the need for multiple efforts. The only solution is mapping the terms either directly, with large bi-domain correspondence tables, or indirectly, with the use of a neutral internal terminology, which will have been structured in such a way that facilitates correspondence. The second solution seems to be less complex and could be implemented using a single neutral ontology, offering the following advantages: • Terminology standardization. The selected name for a term or a relation may also be used by others in new domain modeling work in order to facilitate the communication among different systems. Despite this fact, no standards are enforced, allowing thus the free operation of each independent system. • Knowledge transfer. When a section of a domain ontology, designed for a specific area, is also appropriate for another field, then the creator of the second area, can easily search and locate the section, via the neutral ontology, and integrate it into the second domain ontology. • Interoperability implementation. When a computer system is built, to execute some action using the neutral set of terms, it could be easily ported and adapted to other domains, which also use the neutral ontology [5]. 2.3 E-commerce The use of ontologies in the e-commerce field may prove to be especially useful, because it is explicitly necessary to integrate and cooperate applications from different enterprises and indefinable single web entities. In general, e-commerce can be divided in two fields, Business-to-Customer (B2C) and Business-to-Business (B2B). Ontologies could offer in both fields, for the increase of efficiency and the ease of cooperation. In the B2C field, ontologies could be used for the implementation of the so-called shopbots [22]. A shopbot can compare specific characteristics, such as price, shipping costs, etc. for the same product from different merchants. At the time being, the shopbots use some wrappers, which are specially designed for every vendor, from whom information is retrieved. These systems apply heuristic algorithms for text extraction from the products’ pages into the merchant’s website. For each merchant, a

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special wrapper must be designed, thus, each time the merchant changes the website’s structure, the wrapper must be modified accordingly. With the use of ontologies by merchants, the shopbot agent could simply use the ontology of each merchant, or a mapping of his own ontology to the merchant’s ontology. Thus, he could retrieve the information in this typical way. Consequently, we have simultaneous, integrated and automated query based procedures for the products from a number of merchants. Therefore, the agent displays the query results on the screen of the user-customer. In the B2B field, a lot of work has already been done for the standardization of emails representation among several companies, such as the EDIFACT and XML applications. Furthermore, quite a few attempts have been made to describe the content of the e-mails, (e.g. RosettaNet3, VerticalNet4 applications, which promote collaborative commerce). These content descriptions could be used in a vertical market and are unique for each market. How do we benefit from the use of ontologies in this area? First of all, it would be much easier for an e-market place to translate the different representations used among the companies. Moreover, it could manage the diverse ontology products and services from several e-businesses. Generally, ontologies and especially ontology-based systems appear to be necessary for the development of efficient and profitable e-commerce solutions. An ontology-based application has the potential to accelerate the penetration of ecommerce, within vertical industry sectors, as it can enable interoperability at the business level, reducing at the same time the need for standardization at the technical level [15]. A system that makes use of ontologies to mediate between languages and to infer answers to user questions in the multilingual e-commerce mediation is the technical approach MKBEEM [8]. This approach emphasizes on the combination of human language processing and ontologies and shows how it can help three basic functions: multilingual cataloguing, processing customer language information requests and multilingual trading. In the first case, MKBEEM system5 implements a multilingual cataloguing scenario for editing, checking and automatically translating product articles or product translations, deriving set of possible market-specific categories for a given product and finally processing queries to locate pre-existing product descriptions for maintenance. In the second case, ontologies improve the accuracy in fuzzy information search and solve the cross-language problem, as the system is able to find the same product even if the query is made in a different language than that of the product attributes. Finally, as far as it concerns multilingual trading, ontologies could enable description of multiple content providers through a unique multilingual portal, offering thus integrated access [15]. 2.4 Multimedia and graphics. Ontologies have the ability to be adapted according to the demands of each Knowledge domain. In the multimedia field, ontologies can be expanded from their 3

http://portal.rosettanet.org/cms/sites/RosettaNet/ http://www.verticalnet.com/solutions/overview.asp 5 http://www.mkbeem.com 4

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simple shape, where the concepts are represented with text, and multimedia ontologies can be created, in which each concept can be described as a multimedia entity (including image, video, sound, etc.). Multimedia ontologies cover a broad application field and especially [1]: a. Content visualization: they could be used to create tables of content and also for browsing. b. Content indexing: they can improve indexing consistency in manual annotation systems, or they can help in propagation of labels in automatic indexing systems. c. Knowledge sharing: annotated multimedia collections can be more easily shared, if they use a common conceptual representation. d. Learning: collection annotated by different individuals using common ontologies leads to annotation consistency which is considered of extreme importance in applying approaches based on learning methodologies that use annotated collection for training. e. Reasoning: not explicit information in data could be exported with the use of an ontology. OntoELAN was designed to deal with the lack of ontology-based annotations tools for linguistic multimedia data, which provide limited support for multimedia. OntoELAN was partially sponsored and developed as a part of Electronic MetaStructure for Endangered Language Data (E-MELD) project. It features 1) support of OWL ontologies, 2) management of language profiles which allow the user to choose a subset of ontological terms for annotation, 3) management of ontological tiers, which can be annotated with language profiles terms and thus corresponding ontological terms and 4) storing OntoELAN annotation documents in XML format based on multimedia and domain ontologies. It is the first audio-video annotation tool in the linguistic domain providing support for ontology-based annotation [13]. The Large-Scale Concept Ontology is a remarkable step towards the goal of automatically tagging multimedia content for the support of end-user interactions (searching, filtering, personalization, summarization, etc). This application (led by IBM, Carnegie Mellon University, Columbia University and CyC Corporation) was designed to satisfy multiple criteria of utility, coverage, feasibility and observability. LSCOM effort has produced a set of use cases and queries along with a large annotated data set of broadcast news video and it aims at creating a framework for ongoing research on semantic analysis of multimedia content [21]. Finally, in the Reach Greek National Project, an ontology based approach is used in order to provide enhanced unified access to heterogeneous distributed cultural heritage digital databases. The Reach project deals with new forms of access to multimedia cultural heritage material. In order to achieve this requirement, a core ontology is used, the CIDOC-CRM ontology, so as to provide a global model able to integrate information (metadata) originating from different sources [26]. In graphics, the semantic enrichment of scenes can play an extremely important role in enabling viewers to query, understand and interact with the often complex and obscure visualized information in a simple, instinctive, user-friendly way. Thus, it allows user to identify 3D objects or sets based on their graphic and semantic attributes and their relations with the other image objects, at a certain point of time. The interactive queries, such as “What is the object which I’ve just clicked upon with

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the mouse?” or “What is the functionality, the behavior and the role of the component represented by this object?” etc. should be answered with the suitable visualized and textual response. These queries allow users to explore the internal and external parts of models and understand the way they function and behave. All these procedures usually correspond to events of the real word [3]. A representative example of ontology-based application is OntoSphere. OntoSphere6 is a rather new approach to ontology visualization, using a more than 3-dimensional space. In the system, the ontology information is represented on a 3D view-port enriched by visual cues. This approach is more effective, especially in navigation and browsing, involving “manipulation-level” operation, such as zooming, rotating and translating objects. Furthermore, it aims to graphically represent the taxonomic and the not taxonomic links, as well as selecting and presenting information on the screen at an appropriate detail level according to the user’s interest. 2.5 Peer-to-peer networks and Grid Computing The increase of use of semantic technologies has reached almost every computer science related field, including peer-to-peer networks and the grid computing field. Peer-to-peer networks (P2P) rely primarily on the computing power and bandwidth of the participants in the network rather than concentrating it in a relatively low number of servers. Peer-to-peer networks are typically used for connecting nodes via largely ad hoc connections. Such networks are useful for many purposes, such as sharing content files containing audio, video, data or anything in digital format including real-time data. A pure peer-to-peer network does not have the notion of clients or servers, but only equal peer nodes that simultaneously function as both "clients" and "servers" to the other nodes on the network. This model of network arrangement differs from the client-server model where communication is usually to and from a central server. On the other hand, grid computing technologies cover location and allocation of resources, authentication for remote and community usage, data exchange, sharing, migration, and collaboration for distributed group of users. The idea of the grid is in fact the creation of an ideal, simple but at the same time powerful, self-administrated virtual computer, through a set of interconnected heterogeneous systems, which share several resources combinations. With the help of grid technologies, applications will be modified to split the task into multiple chunks which can be processed simultaneously by each of the computing nodes in a grid. By distributing these chunks of the task among multiple computing nodes, the task can be completed within a significantly shortened time frame. Thus, the Grid is an emerging technology for enabling resource sharing and coordinated problem solving in dynamic multiinstitutional virtual organizations. Grid technologies and peer-to-peer networks are supplementary. The grids can provide services to peer-to-peer networks, whereas applications concerning peer-to-peer could be used for the grid management. One of the most significant sources that computer systems will share in these modern networks is knowledge, such as information for the existent networks and 6

http://ontosphere3d.sourceforge.net

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their current state, user profiles, concept definitions, optimum computer techniques, model descriptions etc. Three basic elements are required for the cooperation among the variants users, and in order to make possible the existence and the exploitation of this knowledge: the access to distributed sources, an agreed process of knowledge representation, and the existence, supply and use of domain ontologies, which will contribute to the concept, definition and representation of the existent knowledge. Ontologies are especially useful, as they have the ability to facilitate the interoperability among computer systems. With the definition of a common vocabulary, errors among concepts are drastically reduced and the availability and the existent situation of several resources are described with accuracy and clarity. Domain ontologies consider being also very valuable for the semantic description of the grid technologies. Grids can provide semantic modelling of the users’ tasks and demands, of the available services and data sources, supporting at the same time high quality services and dynamic service detection and development. The transition from monolithic, centralized ontology services to a virtual organization of Grid compliant and Grid aware ontology services that can coordinate and cooperate with each other, is crucial to progress towards the Semantic Grid. As far as it concerns peer-to-peer networks, one of the most important problems regarding these networks is their scalability limitation. These networks cannot contain a large number of nodes because of the large amount of traffic that they have to handle. Local indexing is considered to be an efficient method, so as to avoid this problem and reduce the traffic. Rostami (et. al.) introduce a novel ontology based local index (OLI) which limits the size of local indexes without losing indexing information. This method can be implemented on many p2p networks and can be a base for future developments in this area [30]. An interesting implementation regarding grid technologies, based on ontologies, is the OnBrowser, an ontology manager that provides access to “knowledge objects” coding ontology portions and referring to Grid metadata, through both user interfaces and application programming interfaces. The system provides a simple and user friendly graphic interface and the possibility to browse ontology levels as users like. Consequently, it is a Grid-aware tool that is able to manage ontologies and integrate them with application-level and Grid-level metadata management systems and information systems. Finally, OnBrowser aims at facing main ontology life cycle phases on the Grid and it provides three different classes of APIs for ontology browsing, querying, and ontology maintenance [17]. Moreover, the Earth System Grid is developing a framework that integrates Grid technologies, to facilitate the analysis of the impacts of global climate change at national laboratories, universities and other laboratories. It supports a collaboratory and a Web portal in which climate scientists and researchers may utilize distributed computing services to discover, access, select and analyze model data produced and stored on a daily basis on super computers across the US. This ontology provides a basis for classifying and retrieving data files etc. It aims to save time and bring more transparency to a scientific user. Finally, OntoEdu, is a kind of flexible educational platform architecture for e-learning. It aims at gaining concept reusability, device and user adaptability, automatic composition, function and performance scalability. In OntoEdu, an ontology is used to describe the concept of networked education platform and their relations and it includes two kinds of ontology: content ontology

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and activity ontology. With the use of the ontology, OntoEdu can “learn” knowledge from education specialists and then automatically composes specific service system in terms of user requests which is called automatic composition [27]. 2.6 Pervasive Computing environments Pervasive (or Ubiquitous) Computing Environments are physical environments enhanced with computing and communications integrated with human users. These environments require the construction of massively distributed systems, which implement a large number of autonomous entities or agents. These entities could be devices, applications, databases, users, etc. Several types of middleware have been developed so as to enable communication between different entities. Nevertheless, none of the existing middleware can offer semantic interoperability. The vision of these environments is that, the addition of computation and communication abilities to the artifacts that surround people, will enable the users to set up their living spaces in a way that it will minimize the required human intervention. Ontologies can help addressing some serious issues concerning knowledge representation, semantic interoperability and service discovery [16]. They can also give systems the necessary semantics to provide users with enough functionality in limited user interfaces, without really accessing services themselves [25]. Six key requirements are dominant in ubiquitous computing: 1) distributed composition, 2) partial validation, 3) information richness and quality, 4) incompleteness and ambiguity, 5) level of formality and 6) applicability. Ontologies are able to incorporate these assets for context modeling and, the most important, they can provide better modeling facilities than pure logic-based approaches. Ontological schemas and instances can be used by reasoners to infer additional knowledge, so that problems like ambiguity, incompleteness and validity of contextual data can be addressed. This additional knowledge may fill the information gaps (left by interruption or low quality measurements). All these highlight the benefits of the use of ontologies and, in addition to the right choice and implementation of ontology languages and tools, a significant number of issues could be handled [34]. Moreover, the use of ontologies provides the ability to resolve several issues concerning the development and management of such systems. Specifically: • Semantic service discovery: In these environments, the concept of service is considered to be crucial, as each user selects the services he wants, in order to achieve certain service configurations. Moreover, the services determine artifacts’ replaceability and plugs’ compatibility. Therefore, a semantic service discovery method is indispensable so as to discover the semantically similar services [34]. • Context-awareness: an important issue regarding ubiquitous computing is contextawareness. Relevant applications have to perceive the current context and adapt their behavior to different situations (by the word “context” we refer to the physical information, e.g. location, time, environmental information, personal information etc.) [34]. Therefore, there is need to have the ability to adapt to rapidly changing situations. The different types of contextual information must be well-defined, so that various entities can have a common understanding of context.

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Consequently, there must be mechanisms that can enable and facilitate all these issues [4]. • Interoperability between different entities: in these environments, new entities edge in at any time. These entities interact with the existent and their interaction must be based on common, well-defined concepts, so that they are able to interoperate. Furthermore, they must form a complete conceptual model of the context, so as they are able to cooperate more easily [4]. As an example, we could mention ONTONAV, an indoor navigation system, which enables personalized path selection. This model exploits a User Navigation Ontology (UNO). Specifically, UNO is an ontology developed for modelling users based on their individual characteristics that influence a) navigational decision (i.e. selection of the optimum path) and b) the form and the means that these navigational decisions are communicated to them. OntoNav uses Ontology Web Language (OWL) for describing the user classes and their properties and specifically OWL-DL, which enables very expressive user definition. The system is based on a hybrid modelling (both geometric and semantic) of such environments. It is purely user-centric, meaning that both the navigation path and the guidelines that describe them are provided to the users according to their physical and perpetual capabilities as well as their particular routing preferences. For the description of path elements (e.g. Corridors, junctions, stairways etc.) an Indoor Navigation Ontology (INO) has been developed. The instances of such ontology are created by annotated GIS building blueprints [6]. Another interesting approach for the support of pervasive computing applications is the Standard Ontology for Ubiquitous and Pervasive Applications (SOUPA). This project began in November 2003 aiming to define ontologies for pervasive environments. It is driven by a set of use cases and it is believed that it can help developers who are inexperienced in knowledge representation to quickly begin constructing ontology-driven applications using a shared ontology that combines many useful vocabularies from different consensus ontologies. Therefore, there is no need to define ontologies from the scratch and they can focus more on the functionalities of the actual system implementations. SOUPA uses OWL [9]. Finally, we should mention another interesting ontology-based application, the GAS ontology which provides a common language for the communication and the collaboration among ubiquitous computing devices, mentioned as eGets. The GAS Ontology addresses a number of key issues, such as the heterogeneity among the various systems and services, the demand for semantic interoperability, the dynamic nature of these environments, etc. [16].

3. Conclusions From the above review and analysis of the available applications, we have a positive indication that ontologies are no longer only academic curiosities; they are an established technology for knowledge collection and they aim at fulfilling the need to facilitate the noble goals of sharing, reuse, and interoperability. They play the role of a binding factor that brings together various knowledge items and processes, so as to

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provide a richer and integrated view of the various domains to the users. They contribute to the quality of interoperating information systems partly by increasing capability. A lot of ontology technologies have been implemented in many areas and fields, some remarkable results have been achieved, however, still, there is neither an overall architecture, nor complete solutions that could cover a wide field. This does not imply that all the aforementioned efforts are not valuable; on the contrary, it is obvious that there is a lot of effort and research performed, with very interesting and precious results that will be further used to proceed to more integrated applications, which will display all the benefits that ontologies have to offer to the semantic world. Although it is practically impossible to include all ontology applications in a single paper, an effort was made to review various domains and to locate, in each domain, the benefits that ontology use could offer. It is obvious that ontologies are very powerful in managing semantic content and for delivering high quality services to end users, and that they can really reveal and make any information available, from any source, and, additionally, assist each user in handling and using it more effectively and efficiently. In the previous paragraphs, a remarkable number of already developed ontology applications were presented, which proves that the ontology field is very attractive, and numerous researchers all around the world perform exhaustive research with very good results. It is obvious that the described applications are not the only available; this paper just described selected applications from each field, in an effort to present an overall view, and to prove that all the ontology benefits also described are not only a theoretical approach, but they are already been used in real applications. It is more than certain, that in the next years, ontologies will be further researched and used, and semantic knowledge management will be further explored, and numerous powerful ontology applications will be presented.

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