Ontology as a Mechanism for Application Integration and Knowledge Sharing in Collaborative Design: A Review
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paper_title: Knowledge level modelling: concepts and terminology

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We address the problem of highly varied and inconsistent usage of terms by the knowledge technology community in the area of knowledge-level modelling. It is arguably difficult or impossible for any standard set of terms and definitions to be agreed on. However, de facto standard usage is already emerging within and across certain segments of the community. This is very difficult to see, however, especially for newcomers to the field. It is the goal of this paper to identify and reflect the most common usage of terms as currently found in the literature. To this end, we introduce and define the concept of a knowledge level model, comparing how the term is used today with Newell's original usage. We distinguish two major types of knowledge level model: ontologies and problem solving models. We describe what an ontology is, what they may be used for and how they are represented. We distinguish various kinds of ontologies and define a number of additional related concepts. We describe what is meant by a problem solving model, what they are used for, and attempt to clarify some terminological confusion that exists in the literature. We define what is meant by the term ‘problem’, and some common notions used to characterise and represent problems. We introduce and describe the ideas of tasks, problem solving methods and a variety of other important related concepts.
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paper_title: Ontology Development 101: A Guide to Creating Your First Ontology

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1 Why develop an ontology? In recent years the development of ontologies—explicit formal specifications of the terms in the domain and relations among them (Gruber 1993)—has been moving from the realm of ArtificialIntelligence laboratories to the desktops of domain experts. Ontologies have become common on the World-Wide Web. The ontologies on the Web range from large taxonomies categorizing Web sites (such as on Yahoo!) to categorizations of products for sale and their features (such as on Amazon.com). The WWW Consortium (W3C) is developing the Resource Description Framework (Brickley and Guha 1999), a language for encoding knowledge on Web pages to make it understandable to electronic agents searching for information. The Defense Advanced Research Projects Agency (DARPA), in conjunction with the W3C, is developing DARPA Agent Markup Language (DAML) by extending RDF with more expressive constructs aimed at facilitating agent interaction on the Web (Hendler and McGuinness 2000). Many disciplines now develop standardized ontologies that domain experts can use to share and annotate information in their fields. Medicine, for example, has produced large, standardized, structured vocabularies such as SNOMED (Price and Spackman 2000) and the semantic network of the Unified Medical Language System (Humphreys and Lindberg 1993). Broad general-purpose ontologies are emerging as well. For example, the United Nations Development Program and Dun & Bradstreet combined their efforts to develop the UNSPSC ontology which provides terminology for products and services (www.unspsc.org). An ontology defines a common vocabulary for researchers who need to share information in a domain. It includes machine-interpretable definitions of basic concepts in the domain and relations among them. Why would someone want to develop an ontology? Some of the reasons are:
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paper_title: The ontolingua server: a tool for collaborative ontology construction

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Reusable ontologies are becoming increasingly important for tasks such as information integration, knowledge-level interoperation and knowledge-base development. We have developed a set of tools and services to support the process of achieving consensus on commonly shared ontologies by geographically distributed groups. These tools make use of the World Wide Web to enable wide access and provide users with the ability to publish, browse, create and edit ontologies stored on anontology server. Users can quickly assemble a new ontology from a library of modules. We discuss how our system was constructed, how it exploits existing protocols and browsing tools, and our experience supporting hundreds of users. We describe applications using our tools to achieve consensus on ontologies and to integrate information.The Ontolingua Server may be accessed through the URLhttp://ontolingua.stanford.edu
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paper_title: Description Logics Emerge from Ivory Towers

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Description logic (DL) has existed as a field for a few decades yet somewhat recently have appeared to transform from an area of academic interest to an area of broad interest. This paper provides a brief historical perspective of description logic developments that have impacted their usability beyond just in universities and research labs and provides one perspective on the topic. Description logics (previously called terminological logics and KL-ONE-like systems) started with a motivation of providing a formal foundation for semantic networks. The first implemented DL system – KL-ONE – grew out of Brachman’s thesis [Brachman, 1977]. This work was influenced by the work on frame systems but was focused on providing a foundation for building term meanings in a semantically meaningful and unambiguous manner. It rejected the notion of maintaining an ever growing (seemingly adhoc) vocabulary of link and node names seen in semantic networks and instead embraced the notion of a fixed set of domain-independent “epistemological primitives” that could be used to construct complex, structured object descriptions. It included constructs such as “defines-an-attribute-of” as a built-in construct and expected terms like “hasemployee” to be higher-level terms built up from the epistemological primitives. Higher level terms such as “has-employee” and “has-part-time-employee” could be related automatically based on term definitions instead of requiring a user to place links between them. In its original incarnation, this led to maintaining the motivation of semantic networks of providing broad expressive capabilities (since people wanted to be able to represent natural language applications) coupled with the motivation of providing a foundation of building blocks that could be used in a principled and well-defined manner. KL-ONE provided an important first step in description l ogic history and since then, many systems have been
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paper_title: Reusable Components for Knowledge Modelling

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In this work I illustrate an approach to the development of a library of problem solving components for knowledge modelling. This approach is based on an epistemological modelling framework, the Task/Method/Domain/Application (TMDA) model, and on a principled methodology, which provide an integrated view of both library construction and application development by reuse. ::: ::: The starting point of the proposed approach is given by a task ontology. This formalizes a conceptual viewpoint over a class of problems, thus providing a task-specific framework, which can be used to drive the construction of a task model through a process of model-based knowledge acquisition. The definitions in the task ontology provide the initial elements of a task-specific library of problem solving components. ::: ::: In order to move from problem specification to problem solving, a generic, i.e. taskindependent, model of problem solving as search is introduced, and instantiated in terms of the concepts in the relevant task ontology, say T. The result is a task-specific, but method-independent, problem solving model. This generic problem solving model provides the foundation from which alternative problem solving methods for a class of tasks can be defined. Specifically, the generic problem solving model provides i) a highly generic method ontology, say M; ii) a set of generic building blocks (generic tasks), which can be used to construct task-specific problem solving methods; and iii) an initial problem solving method, which can be characterized as the most generic problem solving method, which subscribes to M and is applicable to T. More specific problem solving methods can then be (re-)constructed from the generic problem solving model through a process of method/ontology specialization and method-to-task application. ::: ::: The resulting library of reusable components enjoys a clear theoretical basis and provides robust support for reuse. In the thesis I illustrate the approach in the area of parametric design.
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paper_title: A comparison of (semantic) markup languages

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Several languages have been proposed as candidates for semantic markup. We needed to adopt a language for our current research on developing user-oriented tools operating over the Semantic Web. This paper presents the results of our analysis of three candidates that we considered: XML, RDF, and DAML+OIL along with their associated schemas and ontology specifications. The analysis focuses on the expressiveness of each language, and is presented along several dimensions and summarized in a comparison table. A surprising result of our analysis is the decision to adopt XML(Schema) for practical reasons, since it is able to accommodate a relatively expressive set of constructs and is widely known and commercially supported. We also discuss how we plan to complement XML(S) with a small set of conventions, so that we will have an easier transition to other markup languages in the future.
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paper_title: A Roadmap to Ontology Specification Languages

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The interchange of ontologies across the World Wide Web (WWW) and the cooperation among heterogeneous agents placed on it is the main reason for the development of a new set of ontology specification languages, based on new web standards such as XML or RDF. These languages (SHOE, XOL, RDF, OIL, etc) aim to represent the knowledge contained in an ontology in a simple and human-readable way, as well as allow for the interchange of ontologies across the web. In this paper, we establish a common framework to compare the expressiveness and reasoning capabilities of "traditional" ontology languages (Ontolingua, OKBC, OCML, FLogic, LOOM) and "web-based" ontology languages, and conclude with the results of applying this framework to the selected languages.
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paper_title: Logical foundations of object-oriented and frame-based languages

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We propose a novel formalism, called Frame Logic (abbr., F-logic), that accounts in a clean and declarative fashion for most of the structural aspects of object-oriented and frame-based languages. These features include object identity, complex objects, inheritance, polymorphic types, query methods, encapsulation, and others. In a sense, F-logic stands in the same relationship to the object-oriented paradigm as classical predicate calculus stands to relational programming. F-logic has a model-theoretic semantics and a sound and complete resolution-based proof theory. A small number of fundamental concepts that come from object-oriented programming have direct representation in F-logic; other, secondary aspects of this paradigm are easily modeled as well. The paper also discusses semantic issues pertaining to programming with a deductive object-oriented language based on a subset of F-logic.
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paper_title: Toward principles for the design of ontologies used for knowledge sharing?

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Recent work in Artificial Intelligence is exploring the use of formal ontologies as a way of specifying content-specific agreements for the sharing and reuse of knowledge among software entities. We take an engineering perspective on the development of such ontologies. Formal ontologies are viewed as designed artifacts, formulated for specific purposes and evaluated against objective design criteria. We describe the role of ontologies in supporting knowledge sharing activities, and then present a set of criteria to guide the development of ontologies for these purposes. We show how these criteria are applied in case studies from the design of ontologies for engineering mathematics and bibliographic data. Selected design decisions are discussed, and alternative representation choices and evaluated against the design criteria.
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paper_title: Stratified Ontologies: the Case of Physical Objects

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This phenomenon is quite common: the examples above have been taken from existing toplevel ontologies used in practice, and they are responsible, in our opinion, of many situations of tangleness, confusion, and lack of semantic rigour. We advocate in the present paper a design principle aimed to clarify such situations and produce more reusable and well-founded ontologies. It can be stated as follows:
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paper_title: The ontolingua server: a tool for collaborative ontology construction

paper_content:

Reusable ontologies are becoming increasingly important for tasks such as information integration, knowledge-level interoperation and knowledge-base development. We have developed a set of tools and services to support the process of achieving consensus on commonly shared ontologies by geographically distributed groups. These tools make use of the World Wide Web to enable wide access and provide users with the ability to publish, browse, create and edit ontologies stored on anontology server. Users can quickly assemble a new ontology from a library of modules. We discuss how our system was constructed, how it exploits existing protocols and browsing tools, and our experience supporting hundreds of users. We describe applications using our tools to achieve consensus on ontologies and to integrate information.The Ontolingua Server may be accessed through the URLhttp://ontolingua.stanford.edu
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paper_title: WebODE: a scalable workbench for ontological engineering

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This paper presents WebODE as a workbench for ontological engineering that not only allows the collaborative edition of ontologies at the knowledge level, but also provides a scalable architecture for the development of other ontology development tools and ontology-based applications. First, we will describe the knowledge model of WebODE, which has been mainly extracted and improved from the reference model of METHONTOLOGY's intermediate representations. Later, we will present its architecture, together with the main functionalities of the WebODE ontology editor, such as its import/export service, translation services, ontology browser, inference engine and axiom generator, and some services that have been integrated in the workbench: WebPicker, OntoMerge and the OntoCatalogue.
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paper_title: A hierarchical classification scheme to define and order the design space for integral snap-fit assembly

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The diversity of integral attachment snap-fit feature types (e.g. cantilever hooks, bayonet-fingers, compressive hooks, annular snaps, and others), and their possible combinations, sizes and locations and orientations on parts to enable assembly has made it appear that design possibilities may be unbounded. Attempts at understanding, no less optimization, seemed intractable. This paper presents a hierarchical classification scheme that brings order to the design space, and uses that classification scheme to define boundaries and size of the design space for achieving attachment at a level above feature detailing. Classification is based on the essential geometry of parts being assembled. The result is surprising order and simplicity, and the ability to reduce viable options for any assembly situation to a number (e.g. 8–10) that will permit true optimization.
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paper_title: Multi-Agent Systems for Concurrent Intelligent Design and Manufacturing

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Part One: Introduction Chapter 1: General Introduction. 1.1 Motivation. 1.2 Book Organization. 1.3 How To Use This Book. Chapter 2: Collaborative Design and Manufacturing. 2.1 Introduction. 2.2 Engineering Design. 2.3 Advanced Manufacturing Systems. 2.4 Next Generation Collaborative Design and Manufacturing Systems. Chapter 3: DAI and Agents. 3.1 Classic AI and DAI. 3.2 Research Themes in DAI. 3.3 Models of DAI Systems. 3.4 Objects vs. Agents. 3.5 Different Types of Agents. 3.6. Why Agents for Collaborative Design and Manufacturing. Part Two: Important Issues Chapter 4: Knowledge Representation in Agent-Based Concurrent Design and Manufacturing Systems. 4.1 Introduction 4.2 What needs to be Represented. 4.3 How to Represent Knowledge in Agent-Based Systems. 4.4 Research Literature and Further References. Chapter 5: Learning in Agent-Based Concurrent Design and Manufacturing Systems. 5.1 Introdution. 5.2 Why to Learn. 5.3 Single-Agent Learning or Multi-Agent Learning. 5.4 When to Learn. 5.5 Where to Learn. 5.6 What is to be Learned. 5.7 How to Learn. 5.8 Examples. 5.9 Research Literature and Additional References. Chapter 6: Agent Structures. 6.1 Introduction. 6.2 Desirable characteristics of an agent. 6.3 Essential Modules (Components) for agents. 6.4 Different Approaches. 6.5 Comparison of Different Approaches. 6.6 Research Literature and further References. Chapter 7: Multi-Agent System Architectures. 7.1 Introduction. 7.2 Organization and System Architectures. 7.3 Different Approaches. 7.4 Select a suitable system architecture for a specific application. 7.5 Research Literature and Additional Readings. Chapter 8: Communication, Cooperation and Coordination. 8.1 Introduction. 8.2 Communication. 8.3 Coordination. 8.4 Cooperation. 8.5 Coordination, Cooperation and Communication. 8.6 Research Literature and Further References. Chapter 9: Collaboration, Task Decompsition and Allocation. 9.1 Introduction. 9.2 Different Approaches for Task Decomposition and Allocation. 9.3 Coordinated Task Allocation by Mediation. 9.4 Distributed Task Allocation. 9.5 Task Decomposition in MetaMorph: an Example. 9.6 Research Literature and Additional References. Chapter 10: Negotiation and Conflict Resolution. 10.1 Introduction. 10.2 Classification of Negotiation Categories. 103. Negotiation Protocols. 10.4 Negotiation Strategies. 10.5 Negotiation for Conflict Resolution. 10.6 Examples in Concurrent Design and Manufacturing. 10.7 Research Literature and Additional Information. Chapter 11: Ontology Problems. 11.1 Introduction. 11.2 What is Ontology? 11.3 Ontology and Knowledge Sharing. 11.4 Ontology Problems in Concurrent Design and Manufacturing. 11.5 Related concepts, Theories and Methods. 11.6 Ontolingua: A System for Managing Portable Ontologies. 11.7 Research Literature and Additional References. Chapter 12: Other Important Issues. 12.1 Introduction. 12.2 Agent Encapsulation. 12.3 Human machine integration (human participation). 12.4 System dynamics. 12.5. Design and manufacturability assessments. 12.6 Integration of manufacturing Planning, Scheduling and Execution. 12.7 Distributed Dynamic Scheduling. 12.8 Enterprise Integration and Supply Chain Management. 12.9 Legacy problem. 12.10 External interfaces. Part Three: Agent-Based Systems for Engineering Design & Manufacturing Chapter 13: Agent-Based Engineering Design Systems. 13.1 Introduction. 13.2 PACT (PACE) 13.3 SHARE (DSC) 13.4 First-Link, Next-Link and Process Link. 13.5 DIDE. 13.6 SiFAs. 13.7 RAPPID. 13.8 Other projects. 13.9 Summary. Chapter 14: Agent-Based manufacturing Planning, Scheduling and Control. 14.1 Introduction. 14.2 MetaMorph. 14.3 AARIA. 14.4 ADDYMS. 14.5 Other Projects. 14.6 Summary. Chapter 15: Enterprise Integration and Supply Chain Management. 15.1 Introduction. 15.2 ISCM. 15.3 CIIMPLEX. 15.4 MetaMorph II. 15.5 AIMS. 15.6 Other Projects. 15.7 Summary. Part Five: Developing Agent-Based Design and Manufacturing Systems Chapter 16: Methodology, Standards, Tools, Languages, and Frameworks. 16.1 Introduction. 16.2 Tools and Framework. 16.3 Methodology, Languages, and Standards. 16.4 Further references. Chapter 17: Building Agent-Based Design and Manufacturing Systems. 17.1 Introduction. 17.2 Selecting or developing an agent architecture. 17.3 Selecting an approach for agent organization. 17.4 Selecting or developing protocols for inter-agent communication. 17.5 Developing mechanisms for cooperation, coordination and negotiation. 17.6 Selecting platforms, tools and languages. 17.7 Agent-Oriented Design and Analysis. 17.8 Simulation and Implementation. 17.9 Testing, Debugging and Evaluation. Chapter 2: Collaborative Design and Manufacturing, Chapter 3: DAI and Agents. Part Two: Important Issues Chapter 4: Knowledge Representation in Agent-Based Concurrent Design and Manufacturing Systems. Chapter 5: Learning in Agent-Based Concurrent Design and Manufacturing Systems. Chapter 6: Agent Structures. Chapter 7: Multi-Agent System Architectures. Chapter 8: Communication, Cooperation and Coordination. Chapter 9: Collaboration, Task Decomposition and Allocation. Chapter 10: Negotiation and Conflict Resolution. Chapter 11: Ontology Problems. Chapter 12: Other Important Issues. Part Three: Agent-Based Systems for Engineering Design and Manufacturing Chapter 13: Agent-Based Engineering Design Systems. Chapter 14: Agent-Based manufacturing Planning, Scheduling and Control. Chapter 15: Enterprise Integration and Supply Chain Management. Part Four: Developing Agent-Based Design and Manufacturing Systems Chapter 16: Methodlogy, Standards, Tools, Languages, and Frameworks
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<format>
Title: Ontology as a Mechanism for Application Integration and Knowledge Sharing in Collaborative Design: A Review

Section 1: Introduction
Description 1: Introduce the concept of collaborative design and the need for application systems to work together, along with the role of ontology in this context.

Section 2: Overview of Ontology
Description 2: Provide an explanation of the term ontology and its significance in various fields within computer science.

Section 3: Classification of Ontologies
Description 3: Discuss the types and formality of ontologies, detailing domain ontologies, task ontologies, common sense ontologies, and meta-ontologies.

Section 4: Ontology vs. a Knowledge Base
Description 4: Explain the differences between ontology and knowledge base in terms of content, scope, language features, and goals of knowledge codification.

Section 5: Use of Ontology
Description 5: Describe the common usages of ontology, including communication and shared understanding between people, interoperability among systems, and knowledge sharing and reuse.

Section 6: Comparison of Ontology Languages
Description 6: Compare several ontology languages, highlighting their features, expressiveness, and reasoning capabilities.

Section 7: Ontology Building Methodology
Description 7: Outline the methodology for building ontologies, including design criteria, development activities, and integral activities like knowledge acquisition, integration, evaluation, and documentation.

Section 8: Ontology Building Tools and Environments
Description 8: Review the tools and environments available for building and deploying ontologies, mentioning their functionalities and supporting capabilities.

Section 9: Ontology in Collaborative Design
Description 9: Discuss the application of ontology in collaborative design, including improved communication among humans, better data exchange among programs, and enhanced knowledge management.

Section 10: Conclusions
Description 10: Summarize the findings of the review, highlighting the necessity of both domain and task ontologies for supporting knowledge reuse and proposing a hierarchical framework for better integration.
</format>
