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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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Book ChapterDOI
23 Sep 1998
TL;DR: It is clarified that CKDD can be understood as a human-centered approach of Knowledge Discovery in Databases, which led to the software system TOSCANA, which is presented as a CKDD tool in this paper.
Abstract: In this paper we discuss Conceptual Knowledge Discovery in Databases (CKDD) as it is developing in the field of Conceptual Knowledge Processing (cf. [29],[30]). Conceptual Knowledge Processing is based on the mathematical theory of Formal Concept Analysis which has become a successful theory for data analysis during the last 18 years. This approach relies on the pragmatic philosophy of Ch.S. Peirce [15] who claims that we can only analyze and argue within restricted contexts where we always rely on pre-knowledge and common sense. The development of Formal Concept Analysis led to the software system TOSCANA, which is presented as a CKDD tool in this paper. TOSCANA is a flexible navigation tool that allows dynamic browsing through and zooming into the data. It supports the exploration of large databases by visualizing conceptual aspects inherent to the data. We want to clarify that CKDD can be understood as a human-centered approach of Knowledge Discovery in Databases. The actual discussion about human-centered Knowledge Discovery is therefore briefly summarized in Section 1.

136 citations

Patent
27 Oct 2011
TL;DR: In this paper, a pattern-identifying model is constructed using a computer system by applying a context-concept-cluster (CCC) data analysis method, and visualizing that information using the computer system interface.
Abstract: One or more systems and methods for knowledge pattern search from networked agents are disclosed in various embodiments of the invention. A system and a related method can utilizes a knowledge pattern discovery process, which involves analyzing historical data, contextualizing, conceptualizing, clustering, and modeling of data to pattern and discover information of interest. This process may involve constructing a pattern-identifying model using a computer system by applying a context-concept-cluster (CCC) data analysis method, and visualizing that information using a computer system interface. In one embodiment of the invention, once the pattern-identifying model is constructed, the real-time data can be gathered using multiple learning agent devices, and then analyzed by the pattern-identifying model to identify various patterns for gains analysis and derivation of an anomalousness score. This system can be useful for knowledge discovery applications in various industries, including business, competitive intelligence, and academic research.

136 citations

Journal ArticleDOI
TL;DR: This paper reviews the methods for functional dependency, conditional Functional Dependency, approximate functional Dependence, and inclusion dependency discovery in relational databases and a method for discovering XML functional dependencies.
Abstract: Functional and inclusion dependency discovery is important to knowledge discovery, database semantics analysis, database design, and data quality assessment. Motivated by the importance of dependency discovery, this paper reviews the methods for functional dependency, conditional functional dependency, approximate functional dependency, and inclusion dependency discovery in relational databases and a method for discovering XML functional dependencies.

136 citations

Journal ArticleDOI
TL;DR: The selection of right and appropriate text mining technique helps to enhance the speed and decreases the time and effort required to extract valuable information.
Abstract: Rapid progress in digital data acquisition tech-niques have led to huge volume of data. More than 80 percent of today’s data is composed of unstructured or semi-structured data. The discovery of appropriate patterns and trends to analyze the text documents from massive volume of data is a big issue. Text mining is a process of extracting interesting and non-trivial patterns from huge amount of text documents. There exist different techniques and tools to mine the text and discover valuable information for future prediction and decision making process. The selection of right and appropriate text mining technique helps to enhance the speed and decreases the time and effort required to extract valuable information. This paper briefly discuss and analyze the text mining techniques and their applications in diverse fields of life. Moreover, the issues in the field of text mining that affect the accuracy and relevance of results are identified.

136 citations

Journal ArticleDOI
TL;DR: The knowledge acquisition bottleneck impeding theDevelopment of expert systems is being alleviated by the development of computer-based knowledge acquisition tools, which work directly with experts to elicit knowledge, and structure it appropriately to operate as a decision support tool within an expert system.
Abstract: The knowledge acquisition bottleneck impeding the development of expert systems is being alleviated by the development of computer-based knowledge acquisition tools. These work directly with experts to elicit knowledge, and structure it appropriately to operate as a decision support tool within an expert system. However, the elicitation of expert knowledge and its effective transfer to a useful knowledge-based system is complex and involves diverse activities. The complete development of a decision support system using knowledge acquisition tools is illustrated. The example is simple enough to be completely analyzed but exhibits enough real-world characteristics to give significant insights into the processes and problems of knowledge engineering. >

135 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023120
2022285
2021506
2020660
2019740
2018683