Building and (re) using an ontology of air campaign planning

2003

Currently, the development of knowledge-based systems by reusing is the most robust paradigm in knowledge engineering. The design of knowledge bases by reusing existing ontologies provides several advantages: the design is more simple, the resulting representation semantics is more clear and the knowledge can be shared more reliably. However, the process is not straightforward. In most cases, the current ontologies contain too general constructs, so the reusing process must try to bridge the gap between domain applications and general ontologies. A solution is to extract specific concepts from some unified terminology server and to combine them with generic concepts from some ontology. This is the approach followed in this chapter.

2000

: The goal of this project was to enable knowledge engineers to construct knowledge bases (KBs) faster. To achieve this goal, we investigated two techniques: knowledge reuse and axiom templates. The results were demonstrated by developing a question-answering system for the crisis management challenge problem (CMCP). The solution of the CMCP required addressing problems broader than just knowledge reuse and axiom templates. The technical issues addressed in the process can be broadly classified into two categories: knowledge base content development and knowledge server development. We developed a geo-political KB for the CMCP. Specifically, we represented knowledge about international actions, terrorist groups, terrorist events, military capabilities, escalation and de-escalation of conflict, threats, and so forth. In the process of encoding the domain knowledge, we developed and adapted techniques for representing temporal knowledge and qualitative influences, and identified princ...

1999

Abstract This paper describes the practical application of ontologies in designing knowledge-based systems. We built a common ontology to allow knowledge sharing among several applications in the domain of air campaign planning. In trying to reuse this shared ontology, we confirmed previous findings in the knowledge acquisition literature: it is difficult to simultaneously obtain high usability (ie, adequacy for a specific use) and reusability (ie, adequacy for several uses).

Ijca Proceedings on International Conference on Advances in Computer Engineering and Applications, 2014

As a backbone of the Semantic Web, Ontologies provide a shared understanding of a domain of text. Ontologies, with their appearance, usage, and classification address for concrete ontology language which is important for the Semantic Web. They can be used to support a great variety of tasks in different domains such as knowledge representation, natural language processing, information retrieval, information exchange, collaborative systems, databases, knowledge management, database integration, digital libraries, information retrieval, or multi agent systems. Thus a fast and efficient ontology development is a requirement for the success of many knowledge based systems and for the Semantic Web itself. This paper provides discussion on existing ontology tools and methodologies and the state of the art of the field.

2004

With the advent of the WorldWide Web, end-users-individuals and organizations who access information from the Web-have access to large amounts of information. In most cases, a single information source cannot satisfy the needs of end-users. The end-users need to compose information from multiple sources to adequately satisfy their requirements and to get answers to their queries. Today, the end-users are responsible for manually composing information. Ideally, the end-user should have tools that alleviate the burden of manual composition by composing information from diverse sources automatically. Before composing information from diverse sources, however, the composer needs to resolve any semantic heterogeneity among them. Although considerable progress has been made in resolving heterogeneity related to operating systems, and networking protocols, little progress has been made towards solving problems that arise due to semantic heterogeneity. Such heterogeneity arises due to mismatches in the semantics of terms used in the information sources. Mismatches occur because different organizations have different business needs. Attempts at achieving agreement on the semantics of terms and maintaining such agreement may be successful in small domains but is futile for large ones. In order to resolve the semantic heterogeneity of information across information sources, one must understand the semantics associated with the information. Despite advances in technology, machines find it difficult, if not impossible, to decipher the exact semantics associated with information. A popular approach to solving the problem of unspecified or under-specified semantics is to use meta-data structures like ontologies that can be processed by machines. Information sources increasingly come with ontologies that explicitly define the terms used in the sources and their relationships. Large information sources use large vocabularies. Large vocabularies, in turn, result in large ontologies. Often, creating an ontology from scratch is unnecessary and more expensive than constructing an ontology by reusing existing ontologies. A large number of This research was partially supported by the Scalable Knowledge Computing and the OntoAgents projects by the AFOSR New World Vistas program and the DARPA DAML program respectively.

1999

This paper presents the approach to ontology development that is part of the Disciple-LAS shell and methodology for the building of knowledge-based agents. A characteristic feature of this approach is that a detailed specification of the ontology to be developed results from a conceptual modeling of the application domain of the knowledge-based agent to be built. This specification guides the process of building the ontology which consists in importing knowledge from external knowledge servers, and in using the ontology building tools of Disciple. Knowledge import and reuse are facilitated by the fact that the representation of the ontology is based on the OKBC knowledge model. This approach is used to develop an ontology for an agent that critiques military courses of actions.

Ontology plays a vital part in knowledge representation. These-days e-learning packages are prepared on the basis of the ontological structure which represents the knowledge to be designed. If we talk in simple terms, we can claim that taxonomical representation of text materials in text books are nothing but a sort of ontological representations. Taxonomy is a sort of hierarchical representation of texts based on topics, sub-topics, titles, subtitles, etc. Ontology has relations which links the above mentioned items. That way ontology goes a step further form the taxonomical representations. In a way, hierarchies, typologies, taxonomies, and ontologies are related and one depend on the other or one is an extension of the other. Here we are reminded of field semantics or sematic fields propounded by Trier and expanded by Lehrer. A semantic field is a 'group of words closely related in meaning, often subsumed under a general term. The examples are kinship terms, colour terms etc. The object of analysis of semantic field is to collect all the words that belong to a field and show the relationship of each of them to one another and to the general term. The taxonomy and ontology go further to take into their folds concepts beyond words. The purpose of this paper is to elaborate on how field semantics, hierarchies, typologies, taxonomies and ontologies help us to represent knowledge there by find ways to create e-learning packages for sake of learners who wants to access knowledge in a systematic fashion. Ontologies are a vast domain in lexical semantics and in artificial intelligence whose boundaries are somewhat fuzzy. Basically, ontology is a formal system that aims at representing for a given domain by means of basic elements, the different concepts and their related linguistic realizations. A large view of ontological knowledge can also include various forms of encyclopedic knowledge about the domain, and common-sense knowledge as well as rhetorical and metaphorical knowledge and expressions. Ontologies represent an important bridge between knowledge representation and computational lexical semantics. Ontologies are widely used as a formal device to represent the lexical content of words. Declarative representation of knowledge has long been acknowledged as a key building block of Al systems. A knowledge base (KB) with associated reasoning mechanisms can serve as a backbone for tasks such as query answering, learning and diagnosis. One of the key challenges in deploying knowledge representation (KR) systems is the cost and the complexity of building the knowledge base, especially in cases where it needs to cover a broad domain. Building a KB is highly labor intensive for several reasons. First, the acquisition of domain

1999

Abstract Integration as the process of building an ontology reusing other ontologies which are parts of the resulting ontology has already been acknowledged as an essential process in building ontologies. Unfortunately little work has been done in this area. In this article we characterize the integration process, we provide some guidelines to that process, and we present a living classification of integration operations.

2005

Many ontologies are built for the main purpose of representing a domain, rather than to meet the requirements of a specific application. When applications and services are deployed over an ontology, it is sometimes the case that only few parts of the ontology are queried and used. Identifying which parts of an ontology are being used could useful for realising the necessary fragments of the ontology to run the applications. Such information could be used to winnow an ontology, i.e., simplifying or shrinking the ontology to smaller, more fit for purpose sizes. This paper presents a study on the use of the AKT Reference Ontology by a number of applications and services, and investigate the possibility of using this information to winnow that ontology.

International Journal of Human-Computer Studies, 1997

We analyse the construction as well as the role of ontologies in knowledge sharing and reuse for complex industrial applications . In this article , the practical use of ontologies in large-scale applications not restricted to knowledge-based systems is demonstrated , for the domain of engineering systems modelling , simulation and design . A general and formal ontology , called P HYS S YS , for dynamic physical systems is presented and its structuring principles are discussed . We show how the P HYS S YS ontology provides the foundation for the conceptual database schema of a library of reusable engineering model components , covering a variety of disciplines such as mechatronics and thermodynamics , and we describe a full-scale numerical simulation experiment on this basis pertaining to an existing large hospital heating installation . From the application scenario , several general guidelines and experiences emerge . It is possible to identify various iewpoints that are seen as natural within a large domain : broad and stable conceptual distinctions that give rise to a categorization of concepts and properties . This provides a first mechanism to break up ontologies into smaller pieces with strong internal coherence but relatively loose coupling , thus reducing ontological commitments . Secondly , we show how general and abstract ontological super theories , for example mereology , topology , graph theory and systems theory , can be used and reused as generic building blocks in ontology construction . We believe that this is an important element in knowledge sharing across domains . Thirdly , we introduce ontology projections as a flexible means to connect dif ferent base ontologies . Ontology projections can occur in simple forms such as include-and-extent and include-and-specialize , but are in their richest form very knowledge-intensive , being in fact themselves full-blown ontological theories .