Topic
Knowledge extraction
About: Knowledge extraction is a research topic. Over the lifetime, 20251 publications have been published within this topic receiving 413401 citations.
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TL;DR: The judgment theorems of consistent sets are examined, and the discernibility matrix of a formal context is introduced, by which an approach to attribute reduction in the concept lattice is presented.
Abstract: The theory of the concept lattice is an efficient tool for knowledge representation and knowledge discovery, and is applied to many fields successfully. One focus of knowledge discovery is knowledge reduction. This paper proposes the theory of attribute reduction in the concept lattice, which extends the theory of the concept lattice. In this paper, the judgment theorems of consistent sets are examined, and the discernibility matrix of a formal context is introduced, by which we present an approach to attribute reduction in the concept lattice. The characteristics of three types of attributes are analyzed.
140 citations
TL;DR: This paper proposes a new methodology to identify the customers whose decisions will be positively influenced by campaigns, which is easy to implement and can be used in conjunction with most commonly used supervised learning algorithms.
Abstract: In database marketing, data mining has been used extensively to find the optimal customer targets so as to maximize return on investment. In particular, using marketing campaign data, models are typically developed to identify characteristics of customers who are most likely to respond. While these models are helpful in identifying the likely responders, they may be targeting customers who have decided to take the desirable action or not regardless of whether they receive the campaign contact (e.g. mail, call). Based on many years of business experience, we identify the appropriate business objective and its associated mathematical objective function. We point out that the current approach is not directly designed to solve the appropriate business objective. We then propose a new methodology to identify the customers whose decisions will be positively influenced by campaigns. The proposed methodology is easy to implement and can be used in conjunction with most commonly used supervised learning algorithms. An example using simulated data is used to illustrate the proposed methodology. This paper may provide the database marketing industry with a simple but significant methodological improvement and open a new area for further research and development.
140 citations
TL;DR: The authors develop back-propagation learning for acyclic, event-driven networks in general and derive a specific algorithm for learning in EMYCIN-derived expert networks, which offers automation of the knowledge acquisition task for certainty factors, often the most difficult part of knowledge extraction.
Abstract: Expert networks are event-driven, acyclic networks of neural objects derived from expert systems. The neural objects process information through a nonlinear combining function that is different from, and more complex than, typical neural network node processors. The authors develop back-propagation learning for acyclic, event-driven networks in general and derive a specific algorithm for learning in EMYCIN-derived expert networks. The algorithm combines back-propagation learning with other features of expert networks, including calculation of gradients of the nonlinear combining functions and the hypercube nature of the knowledge space. It offers automation of the knowledge acquisition task for certainty factors, often the most difficult part of knowledge extraction. Results of testing the learning algorithm with a medium-scale (97-node) expert network are presented. >
139 citations
TL;DR: The paper seeks to pin‐point the strengths and weaknesses of IT in the domain of knowledge management (KM) and to explain why the technology promise remains unfulfilled, as seen by many KM practitioners.
Abstract: Purpose – Aims to impart new insights into the role of information technology (IT) in knowledge extraction, capture, distribution and personalization. The paper seeks to pin‐point the strengths and weaknesses of IT in the domain of knowledge management (KM) and to explain why the technology promise remains unfulfilled, as seen by many KM practitioners.Design/methodology/approach – The discussion in this paper is fundamentally based on Stankosky's four KM pillars conceptual framework. Within this framework the authors attempted to shed some light on the IT role and the hidden reasons that make knowledge prominently unreachable via IT.Findings – IT assimilation and representation of knowledge intangibility, dynamism, experience and other humanistic cognitive dimensions remain debatable. The current technology is immature to resolve such problems. For IT to be effective for KM it must shred its bivalent logic and instead learn to operate within an authentic continuum.Originality/value – Knowledge managers ne...
139 citations
01 Jan 1998
TL;DR: This paper presents two examples of Text Mining tasks, association extraction and prototypical document extraction, along with several related NLP techniques.
Abstract: In the general framework of knowledge discovery, Data Mining techniques are usually dedicated to information extraction from structured databases. Text Mining techniques, on the other hand, are dedicated to information extraction from unstructured textual data and Natural Language Processing (NLP) can then be seen as an interesting tool for the enhancement of information extraction procedures. In this paper, we present two examples of Text Mining tasks, association extraction and prototypical document extraction, along with several related NLP techniques.
139 citations