A framework for information systems architecture
IBM Systems Journal
Computers in Industry - Special double issue: WET ICE '95
Ontology Learning for the Semantic Web
IEEE Intelligent Systems
Representing and reasoning about semantic conflicts in heterogeneous information systems
Representing and reasoning about semantic conflicts in heterogeneous information systems
IEEE Transactions on Knowledge and Data Engineering
The Knowledge Engineering Review
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Editorial: special issue on web content mining
ACM SIGKDD Explorations Newsletter
Supporting application development in the semantic web
ACM Transactions on Internet Technology (TOIT)
YALE: rapid prototyping for complex data mining tasks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
Ontological Engineering: with examples from the areas of Knowledge Management, e-Commerce and the Semantic Web. (Advanced Information and Knowledge Processing)
Information Integration with Ontologies: Experiences from an Industrial Showcase
Information Integration with Ontologies: Experiences from an Industrial Showcase
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Semantic Digital Libraries
Towards ontology-driven information systems: redesign and formalization of the REA ontology
BIS'07 Proceedings of the 10th international conference on Business information systems
The RuleML family of web rule languages
PPSWR'06 Proceedings of the 4th international conference on Principles and Practice of Semantic Web Reasoning
The OO jDREW reference implementation of RuleML
RuleML'05 Proceedings of the First international conference on Rules and Rule Markup Languages for the Semantic Web
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In enterprise firms, enormous amounts of electronic documents are generated by business analysts and other business domain application users. Applications that use these documents are often driven by business logic that is hard-coded together with application logic. One approach to the separation of business logic from applications is to create and maintain business and information extraction rules in an external, user-friendly format. The drawback of such an externalization is that the business rules, usually, do not have machine interpretable semantics. This situation often leads to misinterpretation of domain analysis documents, which can inhibit the productivity of computer-assisted analytical work and the effectiveness of business solutions. This paper proposes an ontology and rule-based framework for the development of business domain applications, which includes semantic processing of externalized business rules and to some extent externalization of application logic. The creation of external information extraction rules by the business analyst is a cumbersome and time consuming task. In order to overcome this problem, the framework also includes a rule learning system to semi-automate the generation of information extraction rules from source documents with the help of manual annotations. The main idea behind the work presented in this paper is to re-engineer very large enterprise information systems to adapt to Semantic Web computing techniques. The work presented in this paper is inspired by an industrial project.