A Language and Algorithm for Automatic Merging of Ontologies

Ontologies are becoming important repositories of information useful for business transactions and operations since they are amenable to knowledge processing using artificial intelligence techniques. They offer the potential of amassing large contents of relevant information, but until now the fusion or merging of ontologies, needed for knowledge buildup and its exploitation by machine, was done manually or through computer-aided ontology editors. Thus, attaining large ontologies was expensive and slow. This chapter offers a new, automatic method of joining two ontologies to obtain a third one. The method works well in spite of inconsistencies, redundancies, and different granularity of information.

Linking Ontological Conceptions and Mapping Business Life Worlds

This chapter describes a methodology developed to elicit knowledge for the design of a corporate Intranet within a government agency. This Intranet was intended to supply knowledge management systems solutions to various problems such as work duplication, document location, and accessing tacit expertise that was distributed across different office locations and departments. An inventory of the pertinent knowledge was required. We situate our discussion of this undertaking within the context of ontology and what it means for something to be a depiction of a socially constructed reality, a representation of the knowledge of a group. We developed a methodology combining soft systems methodology, causal cognitive mapping, and brainstorming to create a knowledge ontology using UML class diagrams. The methodology offers an effective approach for understanding nonroutine yet rigorous knowledge work and conveying relevant and contextual knowledge to the designers of solutions.

Overview of Semantic Technologies

Semantic technologies are a new wave of computing, using explicit representation of meaning to enable data interoperability and more powerful and flexible information services and transactions. At the core of semantic technologies are ontologies, which capture meaning explicitly and may be used to manipulate and reason over information via its semantics. Unlike traditional data schemas or models, ontologies are capable of representing far more complex relations, may be linked directly to the data they describe, and have a formal logical semantics, facilitating automated deductive reasoning. This chapter introduces the vision of semantic technologies, and provides an overview of the approach and the techniques developed to date. It provides both an executive summary and an orienting framework for reading more technical material.

Knowledge Management Support for Enterprise Distributed Systems

Explosion of information and increasing demands on semantic processing Web applications have pushed software systems to their limits. To address this problem, we propose a semantic-based formal framework (ADP) that makes use of promising technologies to enable knowledge generation and retrieval. We argue that this approach is costeffective, as it reuses and builds on existing knowledge and structure. It is also a good starting point for creating an Organizational Memory and providing Knowledge Management functions.

POVOO

Ontologies still lack in including and considering the dynamic aspects of business processes. Therefore, existing ontology-based information systems provide only static information which does not suit the actual working context of a user. In this project we extend information retrieval techniques with ontologies through a process oriented view on ontologies (POVOO). The purpose is to satisfy a user with information that depends on the current process the user is working on. Due to a context aware approach, it is possible to adapt the information to the user’s current working situation dynamically. We introduce a methodology for generating views on ontologies and we illustrate how an application can use them to query highly specialized knowledge bases.

Ontology as Information System Support for Supply Chain Management

Information is essential to integrating business processes. An information organization framework formalized as a reference model is proposed. It captures the specifics (e.g., dynamics and uncertainty) and functional requirements (e.g., information standardization and problem-orientation) of a supply chain (SC), assumed as a managerial, dynamic, complex, and open system. An information modeling formalism is presented that captures different aspects of SC information system support (ISS) as follows: (1) system taxonomy, tackling the problem of information standardization and unified presentation, (2) problem taxonomy, aiming to capture SC operational specifics, such as problem classification and modeling, (3) ontology, for representing problem specific knowledge in a computational language, and (4) ontology-driven information system, accumulating the above components in a collaborative environment, where SC members work on common problems. A SC-ISS reference model is introduced covering components of the above groupings. An industrial case study is presented.

Toward an Ontology of ICT Management

As an emergent field of research and practice, the management of information and communication technologies (ICT) offers complex challenges, such as how to structure and organize the accumulated body of knowledge as well as the need to orchestrate and encapsulate theoretical perspectives and methodologies. As in any emergent field, the ICT management (ICTM) body of knowledge has mostly expanded through diverse theoretical lenses. It also has to overcome concept redundancy and ambiguity in order to gain insights that are more than “old wine in new bottles.” In this chapter, we focus on two main issues. First, we strive to achieve a holistic perspective of ICTM. Second, we explore the way in which organizational theories can contribute to a better understanding of this holistic perspective. For this, we introduce an ontology that describes four ICTM core constructs—policy, project, assets, and evaluation—and their interrelationships. We discuss each one of these constructs in light of six common organizational theories.

Grounding Business Interaction Models

In the information systems field there exist several theories for guiding the evaluation and design of information systems. These theories need to be transparent and harmonious. In this chapter, business action theory (BAT) as a domain ontology for business interaction and business processes is clarified by elaborating on socio-instrumental pragmatism (SIP) as a base ontology. SIP is an eclectic theory synthesizing several pragmatic theories from reference disciplines outside the IS area. One purpose of SIP is to enable seamless theorizing in the IS area. In this chapter we put forward the foundations of BAT and SIP which are then followed by grounding BAT in SIP. This grounding means that there will be an ontological clarification of BAT by specifying the social action and interaction character of business interaction.

Modeling Strategic Partnerships Using the E3value Ontology

The banking industry is subject to a wave of consolidation taking the form of mergers but also the formation of strategic partnerships. In this chapter, we present how such a partnership can be assessed using the e3value ontology. This ontology allows us to model networks of enterprises and partnerships, exchanging things of economic with each other. To adequately model strategic partnerships, the e3value ontology has been extended to represent investment arrangements and outsourcing constructs. All this is explained using an industry-strength case banking study.

Matching Dynamic Demands of Mobile Users with Dynamic Service Offers

This chapter describes the use of ontologies for personalized situation-aware information and service supply of mobile users in different application domains. A modular application ontology, composed of upper-level ontologies such as location and time ontologies and of domain-specific ontologies, acts as a semantic reference model for a compatible description of user demands and service offers in a service-oriented information-logistical platform. The authors point out that the practical deployment of the platform proved the viability of the conceptual approach and exhibited the need for a more performant implementation of inference engines in mobile multi-user scenarios. Furthermore, the authors hope that understanding the underlying concepts and domain-specific application constraints will help researchers and practitioners building more sophisticated applications not only in the domains tackled in this chapter but also transferring the concepts to other domains.