Ontology-based Information Extraction with SOBA

In this paper we describe SOBA, a sub-component of the SmartWeb multi-modal dialog system. SOBA is a component for ontologybased information extraction from soccer web pages for automatic population of a knowledge base that can be used for domainspecific question answering. SOBA realizes a tight connection between the ontology, knowledge base and the information extraction component. The originality of SOBA is in the fact that it extracts information from heterogeneous sources such as tabular structures, text and image captions in a semantically integrated way. In particular, it stores extracted information in a knowledge base, and in turn uses the knowledge base to interpret and link newly extracted information with respect to already existing entities

Knowledge structure, knowledge granulation and knowledge distance in a knowledge base

AbstractOne of the strengths of rough set theory is the fact that an unknown target concept can be approximately characterized by existing knowledge structures in a knowledge base. Knowledge structures in knowledge bases have two categories: complete and incomplete. In this paper, through uniformly expressing these two kinds of knowledge structures, we first address four operators on a knowledge base, which are adequate for generating new knowledge structures through using known knowledge structures. Then, an axiom definition of knowledge granulation in knowledge bases is presented, under which some existing knowledge granulations become its special forms. Finally, we introduce the concept of a knowledge distance for calculating the difference between two knowledge structures in the same knowledge base. Noting that the knowledge distance satisfies the three properties of a distance space on all knowledge structures induced by a given universe. These results will be very helpful for knowledge discovery from knowledge bases and significant for establishing a framework of granular computing in knowledge bases

Knowledge Management Architecture - Principles and Tendencies

Algorithmic research is an established knowledge engineering process that has allowed researchers to identify new or significant problems, to better understand existing approaches and experimental results, and to obtain new, effective and efficient solutions. While algorithmic researchers regularly contribute to this knowledge base by proposing new problems and novel solutions, the processes currently used to share this knowledge are inefficient, resulting in unproductive overhead. Most of these publication-centered processes lack explicit high-level knowledge structures to support efficient knowledge management. The authors describe a problem-centered collaborative knowledge management architecture associated with Computational Problem Solving (CPS).Knowledge Management Architecture, algorithmic research, ontology, Knowledge-Based Systems

Attempto Controlled English (ACE)

Attempto Controlled English (ACE) allows domain specialists to interactively formulate requirements specifications in domain concepts. ACE can be accurately and efficiently processed by a computer, but is expressive enough to allow natural usage. The Attempto system translates specification texts in ACE into discourse representation structures and optionally into Prolog. Translated specification texts are incrementally added to a knowledge base. This knowledge base can be queried in ACE for verification, and it can be executed for simulation, prototyping and validation of the specification.Comment: 13 pages, compressed, uuencoded Postscript, to be presented at CLAW 96, The First International Workshop on Controlled Language Applications, Katholieke Universiteit Leuven, 26-27 March 199

QL-CONST1: an expert system for quality level prediction in concrete structures

Current trends in the fields of artifical intelligence and expert systems are moving towards the exciting possibility of reproducing and simulating human expertise and expert behaviour into a knowledge base, coupled with an appropriate, partially ‘intelligent’, computer code. This paper deals with the quality level prediction in concrete structures using the helpful assistance of an expert system, QL-CONST1, which is able to reason about this specific field of structural engineering. Evidence, hypotheses and factors related to this human knowledge field have been codified into a knowledge base. This knowledge base has been prepared in terms of probabilities of the presence of either hypotheses or evidence and the conditional presence of both. Human experts in the fields of structural engineering and the safety of structures gave their invaluable knowledge and assistance to the construction of the knowledge base. Some illustrative examples for, the validation of the expert system behaviour are included

Towards distributed diagnosis of the Tennessee Eastman process benchmark

A distributed hybrid strategy is outlined for the isolation of faults and disturbances in the Tennessee Eastman process, which would build on existing structures for distributed control systems, so should be easy to implement, be cheap and be widely applicable. The main emphasis in the paper is on one component of the strategy, a steady-state-based approach. Results obtained by applying this approach are presented and knowledge limitations are discussed. In particular a way in which a knowledge-base might evolve to improve isolation capabilities is suggested and the role of the operator is briefly discussed

Expert systems for quality prediction in structural engineering

This work deals with quality level prediction in concrete structures through the helpful assistance of an expert system wich is able to apply reasoning to this field of structural engineering. Evidences, hypotheses and factors related to this human knowledge field have been codified into a Knowledge Base in terms of probabilities for the presence of either hypotheses or evidences,and conditional presence of both. Human experts in structural engineering and safety of structures gave their invaluable knowledge and assistance necessary when constructing the "computer knowledge body"

Integrating descriptions of knowledge management learning activities into large ontological structures: A case study

Ontologies have been recognized as a fundamental infrastructure for advanced approaches to Knowledge Management (KM) automation, and the conceptual foundations for them have been discussed in some previous reports. Nonetheless, such conceptual structures should be properly integrated into existing ontological bases, for the practical purpose of providing the required support for the development of intelligent applications. Such applications should ideally integrate KM concepts into a framework of commonsense knowledge with clear computational semantics. In this paper, such an integration work is illustrated through a concrete case study, using the large OpenCyc knowledge base. Concretely, the main elements of the Holsapple & Joshi KM ontology and some existing work on e-learning ontologies are explicitly linked to OpenCyc definitions, providing a framework for the development of functionalities that use the built-in reasoning services of OpenCyc in KM ctivities. The integration can be used as the point of departure for the engineering of KM-oriented systems that account for a shared understanding of the discipline and rely on public semantics provided by one of the largest open knowledge bases available