Ontologies

One of the most important characteristics of today’s society is that a huge amount of information is shared by many participants (people, applications). This information must be characterized by a uniformity of terms. This means that, in similar contexts, everyone should understand the same meaning when reading or hearing the same word and everyone should use the same word to refer to the same concept. In different Computer Science disciplines one of the methods that satisfies this need for “common understanding” of concepts is the creation of

ontologies

. Curiously, there are different interpretations of what ontology is. In this chapter, we show the way that the concept of ontology has expanded from Philosophy into Computer Science.

This chapter describes the role of ontologies and corporate taxonomies in managing the content and knowledge within organizations. Managing content in a reusable and effective manner is becoming increasingly important in knowledge centric organizations as the amount of content generated, both text based and rich media, is growing exponentially. Search, categorization and document characterization, content staging and content delivery are the key technology challenges in knowledge management systems. This chapter describes how corporate taxonomies and ontologies can help in making sense of huge amount of content that gets generated across the locations in different languages and formats Different information silos can be connected and workflow and collaboration can be achieved using ontologies. As the KM solutions are moving from a centralized approach to a distributed approach, a framework where multiple taxonomies and ontologies can co-exist with uniform interfaces is needed.

The majority of current information systems were implemented using traditional paradigms which include business modeling techniques applied during the analysis phase such as System Flow Charts, Data Flow Diagrams and Entity-Relationship Diagrams. These legacy systems are now struggling to cope with recent developments, particularly trends towards e-Commerce applications, platform independence, reusability of pre-built components, capacity for reconfiguration and higher reliability. Many organizations now realize they need to re-engineer their systems using new component-based systems approaches and object-oriented computer languages. Although the traditional and component-based approaches have different grammars for representing business models, these business models can be compared, based on their ontological grammars. This paper illustrates how an ontological evaluation of business models can be used to compare them for equivalency of representation of business requirements, when re-engineering legacy systems into component-based information systems.

The Semantic Web is well recognized as an effective infrastructure to enhance visibility of knowledge on the Web. The core of the Semantic Web is “ontology”, which is used to explicitly represent our conceptualizations. Ontology engineering in the Semantic Web is primarily supported by languages such as RDF, RDFS and OWL. This chapter discusses the requirements of ontology in the context of the Web, compares the above three languages with existing knowledge representation formalisms, and surveys tools for managing and applying ontology. Advantages of using ontology in both knowledge-base-style and database-style applications are demonstrated using three real world applications.

This paper revisits the debate between positivism and its alternatives in the field of information systems from a philosophical point of view. It will argue that the heart of the debate is the ontological difference between the views of reality as observer-independent versus observer-dependent. The logical axiom of the excluded third (

tertium non datur

) informs us that two contradictory options cannot simultaneously be true. The paper will discuss what the incompatibility of the ontological positions of positivism and its alternatives means for IS research. It will discuss why scholars attempt to mix the two and will spell out the consequences of an acceptance of their incompatibility. The paper will end by arguing that this debate needs to be contextualized with the problem of positivism versus non-positivism in society and it will ask whether a tolerant coexistence of the two approaches is feasible. Without this contextualized understanding of ontology in general, regional ontologies in IS are not likely to be successful as they will be based on unclear bases.

This paper discusses the influence which philosophical ontology has on the ascription of responsibility in IS. It starts out with a description of two ontological positions which are treated under the heading of “positivism” and “life-world”. Positivism is defined as being based on the assumption of an observer-independent objective reality whereas the life-world represents a reality that is created by intentional perception and hermeneutic interaction. In the subsequent section the paper introduces the concept of responsibility as a possible approach to the ethical and moral questions raised by the use of information systems. The final part then proceeds to discuss the influence that the ontological underpinning has on the ascription of responsibility. In conclusion it will be argued that these questions are of fundamental importance to information systems and that philosophical ontology therefore deserves a more explicit place in IS than it currently has.

Our approach to Case Based Reasoning (CBR) is towards integrated applications that combine case specific knowledge with models of general domain knowledge. In this paper, we describe a domain independent architecture to help in the design of knowledge intensive CBR systems. It is based on knowledge acquisition from a library of application-independent ontologies and the use of CBROnto, ontology with the common CBR terminology that guides case representation; allows the description of flexible, generic and reusable CBR Problem Solving Methods; and allows to reason about the description of CBR systems.

Ontologies and Model-Driven Architecture (MDA) are two modeling approaches being developed in parallel, but by different communities. They have common points and issues and can be brought closer together. Many authors have so far attempted to bridge gaps and have proposed several solutions. The result of these efforts is the recent OMG’s initiative for defining an ontology development platform. In this chapter, we are giving an overview of the state-of-the-art research on the subject of applications of MDA standards for ontology development. The chapter is a result of our experience in developing the MDA-based ontology infrastructure as well as a series of tutorials we gave at many international conferences. The chapter tries to indicate the most important definition for both of the considered modeling approaches. Using those definitions, we depict their mutual similarities and differences. Then, we show the present solution pursuing to apply MDA standard to ontology development with the main stress on OMG’s standardization efforts.

An ontology designed for multimedia applications should enable integration of the conceptual and media spaces. We present M-OWL, a new ontology language, that supports this capability. M-OWL supports explicit definition of media properties for the concepts. The language has been defined as an extension of OWL, the standard ontology language for the web. We have proposed a new Bayesian Network based probabilistic reasoning framework with M-OWL for semantic interpretation of multimedia data. We have also proposed a new model for ontology integration, based on the similarity of the concepts in the media domain. It can be used to integrate several multimedia and traditional ontologies.

One of the difficulties in the development of ontology is the issue of revising ontology. When the system accepts new information or knowledge, this new information may contradict what was initially agreed or defined in the ontology. When this happens, the ontology may need to be revised to reflect the changes. The belief revision theory provides a way that ensures new information does not cause inconsistencies with the existing system when it is introduced. This paper discusses the feasibility of using the concept of belief revision as a basis for ontology revision.

Ontologies evolve when the underlying domain world changes at different points of time. The result then is a series of ontologies whose concepts are related with each other not only within one ontology valid at a moment but through the time, too. This chapter presents a model for representing ontology time series. The focus is on modeling partial overlap between concepts evolving over long periods of time, and the domain of application is historical geospatial reasoning. A framework is presented for representing and reasoning about conceptual overlap of concepts that evolve over an ontology time series. The idea is to provide the ontology developer with an intuitive change ontology for expressing local ontological changes in a declarative way. An algorithm is presented for reasoning about overlapping concepts globally over long periods of time. This algorithm can be applied, e.g., in concept-based information retrieval for ranking search results according to their relevance.

Ontologies are an essential component in Information Systems since they enable knowledge re-use and sharing in a formal, homogeneous and unambiguous way. A domain ontology captures knowledge in a static way, as it is snapshot of knowledge from a particular point of view in a specific time-period. However, in open and dynamic settings, where knowledge changes and evolves, ontology maintenance methods are required to keep knowledge up-to-date. In this chapter we tackle the problem of ontology maintenance as an ontology population problem of the evolving ontologies proposing an incremental ontology population methodology that exploits machine learning techniques and is enforced with a bootstrapping technique in order to tackle large scale problems. The methodology is enriched with fine-tuning methods towards improving the quality and the number of the discovered instances. Finally, experimental results are shown, which prove the applicability and effectiveness of the proposed methodology.

Ontologies can play a very important role in information systems. They can support various information system processes, particularly information acquisition and integration. Ontologies themselves need to be designed, built and maintained. An important part of the ontology engineering cycle is the ability to keep a handcrafted ontology up to date. Therefore, we have developed a tool called MnM that helps during the ontology maintenance process. MnM extracts information from texts and populates ontology. It uses NLP (Natural Language Processing), Information Extraction and Machine Learning technologies. In particular, MnM was tested using an electronic newsletter consisting of news articles describing events happening in the Knowledge Media Institute (KMi). MnM could constitute an important part of an ontology-driven information system, with its integrated web-based ontology editor and provision of open APIs to link to ontology servers and to integrate with information extraction tools.

Too many systems constructed by the software industry fail to meet users’ needs. Requirements elicitation is the set of activities performed to understand users’ needs for a system. Although most texts focus on a few elicitation techniques, there are numerous variations of these basic techniques. So, the question arises, how can an analyst understand all these techniques and their variations? Moreover, most experts today agree that the selection of an appropriate technique must be a function of the situation. But, a seemingly infinite number of situational characteristics exist. So, how can an analyst know which of these many situational characteristics should be taken into account when trying to select elicitation techniques? And, how does an analyst select a technique that makes sense given those situational characteristics?

The overarching goal of this research is to construct an information system to aid novice analysts in selecting the most effective requirements elicitation techniques for their project situation. Fundamental to the success of this endeavor is the creation of an ontology which: (1) sets the context for requirements elicitation and elicitation technique selection; (2) defines key characteristics of elicitation techniques that highlight their essential similarities and differences; and (3) identifies the important characteristics of a situation that should be considered when selecting an elicitation technique. This chapter describes the iterative ontology engineering approach used, summarizes the proposed requirements elicitation ontology, and demonstrates how the ontology will be used as a basis for an information system to assist analysts in selecting an appropriate elicitation technique. As a result, this chapter, rather than focusing on ontology research per se, focuses on the application of ontologies to improve the state of research and practice in one specific information systems discipline — requirements elicitation.

Knowledge intensive business processes are a category of business processes that rely on experience and expert judgment. Automating such processes is a challenge for most enterprises. This chapter introduces the characteristics of such processes, provides some examples and describes the architecture for implementing a system that caters to knowledge intensive business processes.

In this paper we introduce and discuss our approach to creating an object model from a problem domain text description as a basic deliverable of the analysis phase in Object-Oriented Software Engineering using ontologies. For this purpose we first briefly compare object models with ontologies. The object model of a system consists of objects, identified from the text description and structural linkages corresponding to existing or established relationships. The ontologies provide metadata schemas, offering a controlled vocabulary of concepts. At the center of both object models and ontologies are objects within a given problem domain. The both concepts are based on reusability using intensively libraries. The major difference is that while the object model contains explicitly shown structural dependencies between objects in a system, including their properties, relationships and behavior, the ontologies are based on related terms (concepts) only. Because ontology is accepted as a formal, explicit specification of a shared conceptualization, we can naturally link ontologies with object models, which represent a system-oriented map of related objects. To become usable programming entities these objects should be described as Abstract Data Types (ADTs). This paper addresses ontologies as a basis of a complete methodology for object identification and their modeling as (converting to) ADTs, including procedures and available tools such as CORPORUM OntoExtract and VisualText, which can help the conversion process. This paper describes how the developers can implement this methodology on the base of an illustrative example.

This chapter takes the complex knowledge systems of metaphors and shows that their structured knowledge can be represented and predicted by ontology. The complex knowledge system of metaphors contains two knowledge systems, source domain and target domain, as well as the knowledge mapping between the two domains. Hence metaphors offer a test case of how structured knowledge can be manipulated in an information system. In terms of the theory of metaphor, we integrate the Conceptual Mapping Model with an ontology-based knowledge representation. We demonstrate that conceptual metaphor analysis can be restricted and eventually, automated. In terms of knowledge processing, we argue that the knowledge structure encoded in ontology, such as the Suggested Upper Merged Ontology (SUMO), is the necessary foundation for manipulating information from multi-domain and multilingual sources. We first extract source domain knowledge structure based on ontology. Next we show that the ontological account allows correct explanation of the parallel yet different use of the same source domain in two different languages. Thirdly, we showed that the restricted set of upper ontology can be combined with the open lexical knowledgebase of wordnets to provide a principled, yet robust, general coverage of language-based knowledge systems.

The Knowledge Collective is a multi-layer, multi-agent framework for information reuse in an intelligent knowledge base that supports a collection of agents called MicroDroids, which provide information management capabilities through a variety of interfaces for experts, human users, and software components. This information is stored in a variety of internal structures (e.g., Java objects, rules, database structures). The main concept is that information is stored in a format that is natural to the type of information being maintained (e.g., data, metadata, ontologies, concept maps, lexicons, rules). The Knowledge Collective will make ontology based information accessible to many end users, maintainable by domain experts and reusable by many users across many applications without knowing how or where the information is stored. The Knowledge Collective’s first use is in version 4 of CIRCSIM-Tutor, an Intelligent Tutoring System developed at the Illinois Institute of Technology in Chicago, IL.

The emergence of Web technologies has made widespread the use of hypermedia systems as the underlying support for Information Systems in organizations. Hypermedia elements and their associated functionality in this context become organizational assets that are created, improved and delivered to users in an attempt to increase the overall value of the system. Semantic Web approaches to Information Systems focus on providing computational semantics to resources by means of shared meanings encoded as part of formal ontologies. These meanings in turn are intended to enable a higher degree of automation and delegation of tasks to software agents. This chapter addresses the fundamental elements of the ontological representation of hypermedia structures and their connection to the main aspects of Information Systems in the organizational context. Concretely, the integration of hypermedia concepts in a Knowledge Management framework is described, and the role of adaptiveness is characterized as a function driven by organizational value inside such framework. The resulting ontological framework provides ground for the development of ontology-based Information Systems in which hypermedia assets are managed.

Many large organizations have their data and processing spread across multiple independent database applications. These data sources, with their own schemas, need to inter-operate to meet new requirements, both within and across organizations. In this paper, we propose a vision of an ontology-enabled database management systems (called OeDBMS) so that the end users can co-relate and integrate ontologies associated with individual sources and extract, co-relate and integrate data from different sources. We propose the architecture and ontology model for OeDBMS. We propose many useful extensions to the RDF/S-based ontology models that are emerging as standards, and provide a graph-based abstraction for the model. This becomes a basis for defining many useful ontology operators and an ontology query language for browsing, searching, matching and maintaining ontologies. We also address the need for ontology evolution by providing temporal support for ontology.

A web service choreography standard enables a standardized description of business processes that allows not only a clear specification of the control flow, but also it forms the basis for the actual process execution. Such standards are part of the Web Services stack and facilitate Enterprise Systems interoperability. A simple indication of the rapid growth of this area is the number of new and existing standards. While the need for the theoretical evaluation and comparison of these standards is being addressed, regardless of the results of such evaluations and comparisons, the interoperability enabled by such standards is still limited. This limitation stems from a number of shortcomings. First, there is a lack of agreement on a general ontology that can be used to describe any type of phenomena in the world. Second, there is limited agreement on the actual set of phenomena that is being modeled in the Enterprise Systems interoperability domain. Third, interoperability is limited by the lack of agreement on the mapping of such phenomena to the chosen general ontology. Last, a means for replicating the constructivist processes that humans use to ascribe meaning to phenomena is lacking. In this paper, we address each of these limitations. We argue that the BWW representation model is a good choice of a general ontology to be used in this domain. Utilizing this ontology for the purposes of analysis, we present the unique set of phenomena that is currently being modeled by the collective of the four leading Enterprise Systems interoperability standards,

viz

. ebXML BPSS, BPML, BPEL4WS, and, WSCI, and the mappings of these phenomena to the chosen general ontology. Further, we discuss the problems in true interoperability of being able to negotiate and ascribe meaning to constructs.

Automatic discovery of services is a crucial task for the e-Science and e-Business communities. Finding a suitable way to address this issue has become one of the key points to convert the Web in a distributed source of computation, as it enables the location of distributed services to perform a required functionality. To provide such an automatic location, the discovery process should be based on the semantic match between a declarative description of the service being sought and a description being offered. This problem requires not only an algorithm to match these descriptions, but also a language to declaratively express the capabilities of services. This section presents a context-aware ontology selection framework, which allows an increase in precision of the retrieved results by taking the contextual information into account.

What are the key success factors for a knowledge management system (KMS), and how to design and implement successful knowledge management systems, are topical research areas. We argue that designing effective knowledge management systems requires not only a focused view, which is achieved by considering organizational imperatives and technological solutions, but it also benefits from a larger perspective that considers a user-centered design, the individual needs of the users (e.g. work tasks, responsibilities), individual motivational drivers, usability and ergonomics issues. This article emphasizes the role of user models and user modeling within Ontology-based Knowledge Management System (OKMS), integrating a highly interdisciplinary approach. It shows how user models, models of the knowledge workers and user modeling processes can be applied in the context of knowledge management systems. An ontology-based user modeling approach is proposed and concrete examples of how ontology-based inferences can be used for expertise modeling are provided. This chapter emphasizes the importance of using ontology-based representations for modeling the users and providing enhanced user support and advanced features in KMSs.

As the number of Internet users and the number of accessible Web pages grows, it is becoming increasingly difficult for users to find documents that are relevant to their particular needs. Users who submit a query to a publicly available search engine must wade through hundreds of results, most of them irrelevant. The core of the problem is that, whether they are an eighth grade student or a Nobel Prize winner, the identical Web pages are selected and they are presented in the same way. In this chapter, we report on research that is aimed at providing search results tailored to individual users. In order to provide these personalized search results, the search engine exploits information about the user captured in automatically created user profiles. We compare a variety of mechanisms for automatically creating the user profiles, and discuss open issues in user profile creation, representation, and use.

Information system design for a supply chain with its complex organizational dynamics and operational uncertainty is a challenging task. The problem assumes greater significance due to the necessity of integrating largely distributed and diverse information system implementations. A framework to address these issues is proposed. Its implementation is a semantic application that is deployed on the Web and managed by agents. This chapter describes the usefulness of separating domain knowledge from information system solutions and developing models in the form of ontologies. An ontology-driven information system for supply chain management incorporating various forms of modeling in decision-making is described. Ontology is proposed as a separate component in information system architecture that effectively complements its three other constituents, viz., interface, management, and gathering. We demonstrate how ontology development is becoming a critical aspect of the information system design. The object-oriented system development life cycle is utilized for this purpose. The role of ontology in information system’s two temporal dimensions is demonstrated. At development time, ontology replaces the information system analysis completely and the design stage partly. At run time, ontology separates the domain knowledge from other components and delivers it to them upon request, thus ensuring the compatibility of information support with organizational dynamics, and its adaptability to the rapidly changing environment.

Today, enterprises have information technology that could fulfill their requirements in each operational phase and with external partners, e.g., suppliers. For instance, in industrial environment, many applications are available to support operating their Product Life Cycle stages. However, organizations typically acquire them aiming to solve focused needs, without an overall view of the global enterprise’s system integration. Even when enterprise models are interoperable, very often difficulties arise with respect to data semantics when information has to be exchanged, though common semantic models are not in place. Researchers have proposed methodologies and platforms to assist the integration of applications and data. However, implementing new technology in organizations is a complex task, and the advent of continuous technological evolution makes organizations unable to be constantly updated. Such dynamics has a recognized impact in costs and work environment that companies cannot afford, and most of such proposals do not go beyond the research phase. This chapter proposes a methodology to enhance enterprise’s interoperability, keeping the same organization’s technical and operational environment, improving its methods of work and the usability of the installed technology through ontological harmonization of the enterprise product models in use. The presented work was developed and has been applied in the scope of the Intelligent Manufacturing Systems (IMS) SMART-fm program (www.ims.org) and European ATHENA project (www.athena-ip.org), under real industrial environments.

An initial fragment of a core ontology for the manufacturing domain is presented and motivated. It consists of an ontological classification of ADACOR concepts according to the DOLCE foundational ontology. The ontology is conceptually transparent and semantically explicit thus suitable for information communication, sharing, and retrieval. The system here described considers entities performing the manufacturing scheduling and control operations only.

Babylon Knowledge Explorer (BKE) is an integrated suite of tools and information sources being developed in GlaxoSmithKline’s

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to support the prototyping and implementation of ontology-driven information systems and ontology-enhanced knowledge applications. In this paper we describe the current state of BKE development and focus on some of its distinctive or novel approaches, highlighting

How BKE makes use of multiple large pre-existing ontologies in support of text and data mining.

The methodology employed for importing an ontology and making it immediately accessible to BKE’s tools, interfaces, and API.

A formal description of BKE’s ontology-based fact model and how this is employed in implementing information retrieval and data mining capabilities.

A sample application built on BKE that illustrates an ontology-enhanced machine learning tool.

Ontology provides an overarching framework and vocabulary for describing system components and relationships. As such, they represent a means to devise, analyze and compare information systems. This research investigates the development of a software-based ontology within the context of a rural wireless emergency medical (EMS) services. Wireless EMS has developed in response to the unprecedented growth of wireless as a means to communicate in emergency situations. Using an inductive, field-based approach, this study devises and tests a new ontology-based framework for wireless emergency response in rural Minnesota. The ontology is developed by integrating concepts and findings from in-depth field reviews in Minnesota into an ontological software originating out of bioinformatics. This software, Protégé 2000, is an open source ontological software system developed by Stanford University’s Medical Informatics group. Using Protégé 2000, the authors developed a wireless EMS ontological framework populated by the real data gathered from field interviews and related data collection. This EMS framework distinguishes between classes of systems, instances within the classes, and the relationships among classes and instances. The next step in the research involved conducting a simulation of performance using a sample of case study data and demonstrated important linkages among system classes. It is expected that use of such performance ontology will assist researchers and program managers with identifying basic problems in terms of technical and non-technical rural EMS issues, as well as possible patterns of inconsistency or discrepancies across EMS deployments.

In the information society the security of information during access and communication has of basic importance. Smart cards (SC) can integrate security services and the actual application functions, so they are perfect tool to fulfill the role of the key of information society. As there is extremely high number of different smart card applications developed individually, connecting these systems, their interoperability is a growing problem. An additional problem is that the combination of functions on one card needs the thorough configuration of the smart card software. Because of the frequent changes in the SC applications during the life cycle of the card, there is a more frequent need for the reconfiguration of the card software as well. A structured, general description, reference architecture of the smart card system elements and the possible relations among them could support the interoperability and configuration/ reconfiguration in a great extent. The chapter gives a short overview on smart card systems and applications, and introduces ontology — based smart card reference architecture and an smart card attack tree.

Monitoring is not simply the collection and enumeration of data points but the discovery of information and knowledge inherent within a system under study. Mobile Surveyor, a model-based monitoring system, provides a new perspective and approach to software monitoring by incorporating data models throughout the monitoring process. This paper presents the construction and usage of ontology in Mobile Surveyor, attempting to capture the conceptual structure that offers semantic terms to model the system and build a generic application-independent monitoring tool. Ontology enables the information and knowledge in Mobile Surveyor to be accessible, sharable, reusable and transformable to the heterogeneous systems and applications that use Mobile Surveyor. The Ontolingua Server with OKBC is used to provide an environment for ontology development and knowledge base query and manipulation. The performance of Mobile Surveyor and the role ontology play in it are demonstrated by using a mobile application as the case study.

In the Information Society, being well informed is a basic necessity. News agencies, journals, newspapers, and other communication media are required to provide fresh, relevant, high quality information to their clients. As partners of the NEWS (News Engine Web Services) EU IST project we believe that the usage of Semantic Web technologies may enable news providers to achieve these goals. So we are working on developing tools to bring Semantic Web technology into the world of professional journalism. As part of this process, we are currently developing an ontology for NEWS applications. It integrates current journalistic standards with existing top level ontologies and other metadata-related standards. In this chapter we describe the current state of that ontology, including aspects such as its intended usage, the development process and the architecture.