Showing papers on "Ontology (information science) published in 2000"

Algorithm and Tool for Automated Ontology Merging and Alignment

Abstract: Researchers in the ontology-design field have developed the content for ontologies in many domain areas. Recently, ontologies have become increasingly common on the WorldWide Web where they provide semantics for annotations in Web pages. This distributed nature of ontology development has led to a large number of ontologies covering overlapping domains. In order for these ontologies to be reused, they first need to be merged or aligned to one another. The processes of ontology alignment and merging are usually handled manually and often constitute a large and tedious portion of the sharing process. We have developed and implemented PROMPT, an algorithm that provides a semi-automatic approach to ontology merging and alignment. PROMPT performs some tasks automatically and guides the user in performing other tasks for which his intervention is required. PROMPT also determines possible inconsistencies in the state of the ontology, which result from the user’s actions, and suggests ways to remedy these inconsistencies. PROMPT is based on an extremely general knowledge model and therefore can be applied across various platforms. Our formative evaluation showed that a human expert followed 90% of the suggestions that PROMPT generated and that 74% of the total knowledge-base operations invoked by the user were suggested by PROMPT.

PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment

Abstract: Researchers in the ontology-design field have developed the content for ontologies in many domain areas. Recently, ontologies have become increasingly common on the WorldWide Web where they provide semantics for annotations in Web pages. This distributed nature of ontology development has led to a large number of ontologies covering overlapping domains. In order for these ontologies to be reused, they first need to be merged or aligned to one another. The processes of ontology alignment and merging are usually handled manually and often constitute a large and tedious portion of the sharing process. We have developed and implemented PROMPT, an algorithm that provides a semi-automatic approach to ontology merging and alignment. PROMPT performs some tasks automatically and guides the user in performing other tasks for which his intervention is required. PROMPT also determines possible inconsistencies in the state of the ontology, which result from the user’s actions, and suggests ways to remedy these inconsistencies. PROMPT is based on an extremely general knowledge model and therefore can be applied across various platforms. Our formative evaluation showed that a human expert followed 90% of the suggestions that PROMPT generated and that 74% of the total knowledge-base operations invoked by the user were suggested by PROMPT.

An environment for merging and testing large ontologies

Abstract: Large-scale ontologies are becoming an essential component of many applications including standard search (such as Yahoo and Lycos), ecommerce (such as Amazon and eBay), configuration (such as Dell and PC-Order), and government intelligence (such as DARPA’s High Performance Knowledge Base (HPKB) program). The ontologies are becoming so large that it is not uncommon for distributed teams of people with broad ranges of training to be in charge of the ontology development, design, and maintenance. Standard ontologies (such as UNSPSC) are emerging as well which need to be integrated into large application ontologies, sometimes by people who do not have much training in knowledge representation. This process has generated needs for tools that support broad ranges of users in (1) merging of ontological terms from varied sources, (2) diagnosis of coverage and correctness of ontologies, and (3) maintaining ontologies over time. In this paper, we present a new merging and diagnostic ontology environment called Chimaera, which was developed to address these issues in the context of HPKB. We also report on some initial tests of its effectiveness in merging tasks.

Multi-Agent Systems for Concurrent Intelligent Design and Manufacturing

Abstract: 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

OIL in a Nutshell

Abstract: Currently computers are changing from single isolated devices into entry points into a worldwide network of information exchange and business transactions. Support in data, information, and knowledge exchange is becoming the key issue in current computer technology. Ontologies will play a major role in supporting information exchange processes in various areas. A prerequisite for such a role is the development of a joint standard for specifying and exchanging ontologies. The purpose of the paper is precisely concerned with this necessity. We will present OIL, which is a proposal for such a standard. It is based on existing proposals such as OKBC, XOL and RDF schema, enriching them with necessary features for expressing ontologies. The paper sketches the main ideas of OIL.

A Formal Ontology of Properties

Abstract: A common problem of ontologies is that their taxonomic structure is often poor and confusing. This is typically exemplified by the unrestrained use of subsumption to accomplish a variety of tasks. In this paper we show how a formal ontology of unary properties can help using the subsumption relation in a disciplined way. This formal ontology is based on some meta-properties built around the fundamental philosophical notions of identity, unity, essence, and dependence. These meta-properties impose some constraints on the subsumption relation that clarify many misconceptions about taxonomies, facilitating their understanding, comparison and integration.

Ontology-based knowledge representation for bioinformatics

Abstract: Much of biology works by applying prior knowledge ('what is known') to an unknown entity, rather than the application of a set of axioms that will elicit knowledge. In addition, the complex biological data stored in bioinformatics databases often require the addition of knowledge to specify and constrain the values held in that database. One way of capturing knowledge within bioinformatics applications and databases is the use of ontologies. An ontology is the concrete form of a conceptualisation of a community's knowledge of a domain. This paper aims to introduce the reader to the use of ontologies within bioinformatics. A description of the type of knowledge held in an ontology will be given.The paper will be illustrated throughout with examples taken from bioinformatics and molecular biology, and a survey of current biological ontologies will be presented. From this it will be seen that the use to which the ontology is put largely determines the content of the ontology. Finally, the paper will describe the process of building an ontology, introducing the reader to the techniques and methods currently in use and the open research questions in ontology development.

Dynamic Ontologies on the Web

Abstract: We discuss the problems associated with managing ontologies in distributed environments such as the Web. The Web poses unique problems for the use of ontologies because of the rapid evolution and autonomy of web sites. We present SHOE, a web-based knowledge representation language that supports multiple versions of ontologies. We describe SHOE in the terms of a logic that separates data from ontologies and allows ontologies to provide different perspectives on the data. We then discuss the features of SHOE that address ontology versioning, the effects of ontology revision on SHOE web pages, and methods for implementing ontology integration using SHOE’s extension and version mechanisms.

Enriching very large ontologies using the WWW

Abstract: This paper explores the possibility to exploit text on the world wide web in order to enrich the concepts in existing ontologies. First, a method to retrieve documents from the WWW related to a concept is described. These document collections are used 1) to construct topic signatures (lists of topically related words) for each concept in WordNet, and 2) to build hierarchical clusters of the concepts (the word senses) that lexicalize a given word. The overall goal is to overcome two shortcomings of WordNet: the lack of topical links among concepts, and the proliferation of senses. Topic signatures are validated on a word sense disambiguation task with good results, which are improved when the hierarchical clusters are used.

The Chimaera Ontology Environment

Abstract: Ontologies have become central components in many applications including search, e-commerce, configuration and, arguably, every large web site (at least for organization and navigation). As ontologies become larger, more distributed, and longer-lived, the need for ontology creation and maintenance environments grows. In our work with ontologies and tool environments over the last few years, we have observed growing needs for automated support of two tasks: (1) merging multiple ontologies and (2) diagnosing (and evolving) ontologies. Chimaera is aimed at supporting these two tasks.

Using Ontological Engineering to Overcome Common AI-ED Problems

Abstract: This paper discusses long-term prospects of AI-ED research with the aim of giving a clear view of what we need for further promotion of the research from both the AI and ED points of view. An analysis of the current status of AI-ED research is done in the light of intelligence, conceptualization, standardization and theory-awareness. Following this, an ontology-based architecture with appropriate ontologies is proposed. Ontological engineering of IS/ID is next discussed followed by a road map towards an ontology-aware authoring system. Heuristic design patterns and XML-based documentation are also discussed.

Function in Device Representation

Abstract: We explore the meanings of the terms ‘structure’, ‘behaviour’, and, especially, ‘function’ in engineering practice. Computers provide great assistance in calculation tasks in engineering practice, but they also have great potential for helping with reasoning tasks. However, realising this vision requires precision in representing engineering knowledge, in which the terms mentioned above play a central role. We start with a simple ontology for representing objects and causal interactions between objects. Using this ontology, we investigate a range of meanings for the terms of interest. Specifically, we distinguish between function as effect on the environment, and a device-centred view of device function. In the former view, function is seen as an intended or desired role that an artifact plays in its environment. We identify an important concept called mode of deployment that is often left implicit, but whose explicit representation is necessary for correct and complete reasoning. We discuss the task of design and design verification in this framework. We end with a discussion that relates needs in the world to functions of artifacts created to satisfy the needs.

Mining Ontologies from Text

Abstract: Ontologies have become an important means for structuring knowledge and building knowledge-intensive systems. For this purpose, efforts have been made to facilitate the ontology engineering process, in particular the acquisition of ontologies from domain texts. We present a general architecture for discovering conceptual structures and engineering ontologies. Based on our generic architecture we describe a case study for mining ontologies from text using methods based on dictionaries and natural language text. The case study has been carried out in the telecommunications domain. Supporting the overall text ontology engineering process, our comprehensive approach combines dictionary parsing mechanisms for acquiring a domain-specific concept taxonomy with a discovery mechanism for the acquisition of non-taxonomic conceptual relations.

WonderTools? A comparative study of ontological engineering tools

Abstract: Ontologies are becoming increasingly important in a variety of different fields, such as intelligent searching on the web, knowledge sharing and reuse, knowledge management, etc. Therefore, we expect that the need for tools to support the construction of ontologies will increase significantly in the coming years. In this paper, we investigate several of these tools. We evaluate the tools using two different ontologies: a simple one about university employees, and a second, more complex one, about the structure of a university study. The evaluation was conducted using a framework, which incorporates aspects of ontology buildings and testing, as well as cooperation with other users. Our conclusions are that the usefulness of the tools depends on the level of the users and the stage of development of the ontology.

An ontology for biological function based on molecular interactions

Abstract: Motivations: A number of important bioinformatics computations involve computing with function: executing computational operations whose inputs or outputs are descriptions of the functions of biomolecules. Examples include performing functional queries to sequence and pathway databases, and determining functional equality to evaluate algorithms that predict function from sequence. A prerequisite to computing with function is the existence of an ontology that provides a structured semantic encoding of function. Functional bioinformatics is an emerging subfield of bioinformatics that is concerned with developing ontologies and algorithms for computing with biological function. Results: The article explores the notion of computing with function, and explains the importance of ontologies of function to bioinformatics. The functional ontology developed for the EcoCyc database is presented. This ontology can encode a diverse array of biochemical processes, including enzymatic reactions involving smallmolecule substrates and macromolecular substrates, signal-transduction processes, transport events, and mechanisms of regulation of gene expression. The ontology is validated through its use to express complex functional queries for the EcoCyc DB.

Semi-Automatic Engineering of Ontologies from Text

Abstract: Ontologies have become an important means for structuring information and information systems and, hence, important in knowledge as well as in software engineering. However, there remains the problem of engineering large and adequate ontologies within short time frames in order to keep costs low. For this purpose, e orts have been made to facilitate the ontology engineering process, in particular the acquisition of ontologies from domain texts. We present a general architecture for discovering conceptual structures and engineering ontologies. Based on the architecture we propose a new approach to extend current approaches, who mostly focus on the semi-automatic acquisition of taxonomies, by the discovery of non-taxonomic conceptual relations. We use a generalized association rule algorithm that does not only detect relations between concepts, but also determines the appropriate level of abstraction at which to de ne relations.

A Method for Semi-Automatic Ontology Acquisition from a Corporate Intranet

Abstract: The focused access to knowledge resources like intranet documents plays a vital role in knowledge management and supports in general the shifting towards a Semantic Web. Ontologies act as a conceptual backbone for semantic document access by providing a common understanding and conceptualization of a domain. Building domain-specific ontologies is a time-consuming and expensive manual construction task. This paper describes our actual and ongoing work in supporting semi-automatic ontology acquisition from a corporate intranet of an insurance company. We present a comprehensive architecture and generic method for discovering a domain-tailored ontology from given intranet resources.

ScholOnto: an ontology-based digital library server for research documents and discourse

Abstract: The internet is rapidly becoming the first place for researchers to publish documents, but at present they receive little support in searching, tracking, analysing or debating concepts in a literature from scholarly perspectives. This paper describes the design rationale and implementation of ScholOnto, an ontology-based digital library server to support scholarly interpretation and discourse. It enables researchers to describe and debate via a semantic network the contributions a document makes, and its relationship to the literature. The paper discusses the computational services that an ontology-based server supports, alternative user interfaces to support interaction with a large semantic network, usability issues associated with knowledge formalisation, new work practices that could emerge, and related work.

Revisiting Ontology Design: A Methodology Based on Corpus Analysis

Abstract: We promote a new approach for knowledge modelling based on knowledge elicitation from technical documents. It benefits of the increasing amount of available electronic texts and of the maturity of natural language processing tools. The approach defines a framework where the knowledge engineer selects the appropriate tools, combines their use and interprets their results to build up a domain model. The paper presents the method and reports an on-going application to design an ontology of knowledge engineering tools in French.

A Roadmap to Ontology Specification Languages

Abstract: 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.

The KRAFT architecture for knowledge fusion and transformation

Abstract: This paper describes the Knowledge Reuse And Fusion/Transformation (KRAFT) architecture which supports the fusion of knowledge from multiple, distributed, heterogeneous sources. The architecture uses constraints as a common knowledge interchange format, expressed against a common ontology. Knowledge held in local sources can be transformed into a common constraint language, and fused with knowledge from other sources. The fused knowledge is then used to solve some problem or deliver some information to a user. Problem solving in KRAFT typically exploits pre-existing constraint solvers. KRAFT uses an open and flexible agent architecture in which knowledge sources, knowledge fusing entities and users are all represented by independent KRAFT agents, communicating using a messaging protocol. Facilitator agents perform matchmaking and brokerage services between the various kinds of agent. KRAFT is being applied to an example application in the domain of network data services design.

From Manual to Semi-Automatic Semantic Annotation: About Ontology-Based Text Annotation Tools

Abstract: Semantic Annotation is a basic technology for intelligent content and is beneficial in a wide range of content-oriented intelligent applications. In this paper we present our work in ontology-based semantic annotation, which is embedded in a scenario of a knowledge portal application. Starting with seemingly good and bad manual semantic annotation, we describe our experiences made within the KA2-initiative. The experiences gave us the starting point for developing an ergonomic and knowledge base-supported annotation tool. Furthermore, the annotation tool described are currently extended with mechanisms for semi-automatic information-extraction based annotation. Supporting the evolving nature of semantic content we additionally describe our idea of evolving ontologies supporting semantic annotation.

Designing clustering methods for ontology building: the Mo'K workbench

Abstract: This paper describes Mo'K, a configurable workbench that supports the development of conceptual clustering methods for ontology building. Mo'K is intended to assist ontology developers in the exploratory process of defining the most suitable learning methods for a given task. To do so, it provides facilities for evaluation, comparison, characterization and elaboration of conceptual clustering methods. Also, the model underlying Mo'K permits a fine-grained definition of similarity measures and class construction operators, easing the tasks of method instantiation and configuration. This paper presents some experimental results that illustrate the suitability of the model to help characterize and assess the performance of different methods that learn semantic classes from parsed corpora.

Ontology Engineering beyond the Modeling of Concepts and Relations

Abstract: This papers presents a new approach for modeling large-scale ontologies. We extend well-established methods for modeling concepts and relations by transportable methods for modeling ontological axioms. The gist of our approach lies in the way we treat the majority of axioms. They are categorized into different types and specified as complex objects that refer to concepts and relations. Considering language and system particularities, this first layer of representation is then translated into the target representation language. This two-layer approach benefits engineering, because the intended meaning of axioms is captured by the categorization of axioms. Classified object representations allow for versatile access to and manipulations of axioms via graphical user interfaces.

Ontology for database design and application development

Abstract: A system and method lets a user create or import ontologies and create databases and related application software. These databases can be specially tuned to suit a particular need, and each comes with the same error-detection rules to keep the data clean. Such databases may be searched based on meaning, rather than on words-that-begin-with-something. And multiple databases, if generated from the same basic ontology can communicate with each other without any additional effort. Ontology management and generation tools enable enterprises to create databases that use ontologies to improve data integration, maintainability, quality, and flexibility. Only the relevant aspects of the ontology are targeted, extracting out a sub-model that has the power of the full ontology restricted to objects of interest for the application domain. To increase performance and add desired database characteristics, this sub-model is translated into a database system. Java-based object-oriented and relational application program interfaces (APIs) are then generated from this translation, providing application developers with an API that exactly reflects the entity types and relations (classes and methods) that are represented by the database. This generation approach essentially turns the ontology into a set of integrated and efficient databases.

Knowledge-based product development using XML and KCs

Abstract: As we enter the new millennium, our approach to product development is evolving rapidly. Companies are in the process of creating a distributed design and manufacturing environment that enables integrated product, processes, and protocols development (IP3D). Certain strategies and some specific technologies are required to create such an environment. For instance, effective reuse of enterprise knowledge is a key strategic component of IP3D. To reuse the knowledge, however, we must first capture and maintain it in a persistent manner and then disseminate and share it in a practical manner throughout the development cycle. To do this task efficiently, we need technologies and protocols to define a communication dictionary and implement a user-friendly access interface. In part one [Rezayat M., Computer Aided Design 2000;32:85–96], we showed that such technologies and protocols must be Web-based because of its open standards, ease of use, and ubiquity. In this paper, we review one IP3D strategy referred to as Knowledge-Based Product Development (KBPD) and then take a close look at one Web-based technology, the eXtensible Markup Language (XML), for defining the required interfaces. We also propose use of Key Characteristics (KCs) as a main enabler for defining the ontology. Some examples and scenarios demonstrate the use of KCs and XML as key components for implementation of a KBPD system.

Ontology-driven information system

Abstract: An ontology-driven information system includes a plurality of models, each of which expresses an aspect of a business domain using concepts and relationships between concepts. An ontology, which is in communication with each of the plurality of models, provides uniform definitions for the concepts and relationships between concepts used in the plurality of models. A method for executing an interaction flow model includes receiving an event and categorizing the received event. Once the event is categorized, a situation that matches the categorized received event is identified. One or more tasks are then executed for the situation. The execution of the one or more tasks can include either an interpretation of a model or the execution of a method of an object. The information system also includes a user and application interface and a reasoning engine that is in communication with the user and application interface. A knowledge manager is in communication with the user and application interface and is interfaced with the reasoning engine. A distributed information service also is in communication with the reasoning engine, the knowledge manager, and the user and application interface.