Knowledge extraction

About: Knowledge extraction is a research topic. Over the lifetime, 20251 publications have been published within this topic receiving 413401 citations.

An algorithmic approach to concept exploration in a large knowledge network (automatic thesaurus consultation): symbolic branch-and-bound search vs. connectionist Hopfield net activation

Abstract: This paper presents a framework for knowledge discovery and concept exploration. In order to enhance the concept exploration capability of knowledge-based systems and to alleviate the limitations of the manual browsing approach, we have developed two spreading activation-based algorithms for concept exploration in large, heterogeneous networks of concepts (e.g., multiple thesauri). One algorithm, which is based on the symbolic AI paradigm, performs a conventional branch-and-bound search on a semantic net representation to identify other highly relevant concepts (a serial, optimal search process). The second algorithm, which is based on the neural network approach, executes the Hopfield net parallel relaxation and convergence process to identify “convergent” concepts for some initial queries (a parallel, heuristic search process). Both algorithms can be adopted for automatic, multiple-thesauri consultation. We tested these two algorithms on a large text-based knowledge network of about 13,000 nodes (terms) and 80,000 directed links in the area of computing technologies. This knowledge network was created from two external thesauri and one automatically generated thesaurus. We conducted experiments to compare the behaviors and performances of the two algorithms with the hypertext-like browsing process. Our experiment revealed that manual browsing achieved higher-term recall but lower-term precision in comparison to the algorithmic systems. However, it was also a much more laborious and cognitively demanding process. In document retrieval, there were no statistically significant differences in document recall and precision between the algorithms and the manual browsing process. In light of the effort required by the manual browsing process, our proposed algorithmic approach presents a viable option for efficiently traversing large-scale, multiple thesauri (knowledge network). © 1995 John Wiley & Sons, Inc.

Relations of attribute reduction between object and property oriented concept lattices

Abstract: As one of the basic problems of knowledge discovery and data analysis, knowledge reduction can make the discovery of implicit knowledge in data easier and the representation simpler. In this paper, relations of attribute reduction between object and property oriented formal concept lattices are discussed. And beautiful results are obtained that attribute reducts and attribute characteristics in the two concept lattices are the same based on new approaches to attribute reduction by means of irreducible elements. It turns out to be meaningful and effective in dealing with knowledge reduction, as attribute reducts and attribute characteristics in the object and property oriented formal concept lattices can be acquainted by only investigating one of the two concept lattices.

A Mutually Beneficial Integration of Data Mining and Information Extraction

Abstract: Text mining concerns applying data mining techniques to unstructured text. Information extraction(IE) is a form of shallow text understanding that locates specific pieces of data in natural language documents, transforming unstructured text into a structured database. This paper describes a system called DISCOTEX, that combines IE and data mining methodologies to perform text mining as well as improve the performance of the underlying extraction system. Rules mined from a database extracted from a corpus of texts are used to predict additional information to extract from future documents, thereby improving the recall of IE. Encouraging results are presented on applying these techniques to a corpus of computer job announcement postings from an Internet newsgroup.

Discovering Functional and Inclusion Dependencies in Relational Databases

Abstract: We consider the problem of discovering the functional and inclusion dependencies that a given database instance satisfies. This technique is used in a database design tool that uses example databases to give feedback to the designer. If the examples show deficiencies in the design, the designer can directly modify the examples. the tool then infers new dependencies and the database schema can be modified, if necessary. the discovery of the functional and inclusion dependencies can also be used in analyzing an existing database. the problem of inferring functional dependencies has several connections to other topics in knowledge discovery and machine learning. In this article we discuss the use of examples in the design of databases, and give an overview of the complexity results and algorithms that have been developed for this problem. © 1992 John Wiley & Sons, Inc.

A grey-based rough decision-making approach to supplier selection

Abstract: In this paper, we propose a grey-based rough set approach to deal with the supplier selection in supply chain management. The proposed approach takes advantage of mathematical analysis power of grey system theory while at the same time utilizing data mining and knowledge discovery power of rough set theory. It is suitable to the decision-making under more uncertain environments. We also provide a viewpoint on the attribute values in rough set decision table under the condition that all alternatives are described by linguistic variables that can be expressed in grey number. The most suitable supplier can be determined by grey relational analysis based on grey number. A case of supplier selection was used to validate the proposed approach.