Knowledge extraction

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

Method and system of knowledge based search engine using text mining

Abstract: A method of text mining is disclosed for automatically building text knowledge base. First, the text mining is applied to the web pages downloaded from internet/intranet or other text documents to extract phrases information. Then, the phrases are classified using automatic classification method or using existed classification information. In addition, the weights between the phrases are trained by using the text information in the web pages or the documents. A knowledge base system is built using the text mining results. The knowledge base is used to directly provide knowledge for a search. Also, the knowledge base helps search engine refine search results.

Schema mapping discovery from data instances

Abstract: We introduce a theoretical framework for discovering relationships between two database instances over distinct and unknown schemata. This framework is grounded in the context of data exchange. We formalize the problem of understanding the relationship between two instances as that of obtaining a schema mapping so that a minimum repair of this mapping provides a perfect description of the target instance given the source instance. We show that this definition yields “intuitive” results when applied on database instances derived from each other by basic operations. We study the complexity of decision problems related to this optimality notion in the context of different logical languages and show that, even in very restricted cases, the problem is of high complexity.

Data mining using rule extraction from Kohonen self-organising maps

Abstract: The Kohonen self-organising feature map (SOM) has several important properties that can be used within the data mining/knowledge discovery and exploratory data analysis process. A key characteristic of the SOM is its topology preserving ability to map a multi-dimensional input into a two-dimensional form. This feature is used for classification and clustering of data. However, a great deal of effort is still required to interpret the cluster boundaries. In this paper we present a technique which can be used to extract propositional IF..THEN type rules from the SOM network’s internal parameters. Such extracted rules can provide a human understandable description of the discovered clusters.

From “folklore” to “living design memory”

Abstract: We identify an important type of software design knowledge that we call community specific folklore and show problems with current approaches to managing it. We built a tool that serves as a living design memory for a large software development organization. The tool delivers knowledge to developers effectively and is embedded in organizational practice to ensure that the knowledge it contains evolves as necessary. This work illustrates important lessons in building knowledge management systems, integrating novel technology into organizational practice, and managing research-development partnerships.

Rough Set Analysis of Preference-Ordered Data

Abstract: The paper is devoted to knowledge discovery from data, taking into account prior knowledge about preference semantics in patterns to be discovered. The data concern a set of situations (objects, states, examples) described by a set of attributes (properties, features, characteristics). The attributes are, in general, divided into condition and decision attributes, corresponding to input and output of a situation. The situations are partitioned by decision attributes into decision classes. A pattern discovered from the data has a symbolic form of decision rule or decision tree. In many practical problems, some condition attributes are defined on preference-ordered scales and the decision classes are also preference-ordered. The known methods of knowledge discovery ignore, unfortunately, this preference information, taking thus a risk of drawing wrong patterns. To deal with preference-ordered data we propose to use a new approach called Dominance-based Rough Set Approach (DRSA). Given a set of situations described by at least one condition attribute with preference-ordered scale and partitioned into preference-ordered classes, the new rough set approach is able to approximate this partition by means of dominance relations. The rough approximation of this partition is a starting point for induction of "if..., then..." decision rules. The syntax of these rules is adapted to represent preference orders. The DRSA analyses only facts present in data and possible inconsistencies are identified. It preserves the concept of granular computing, however, the granules are dominance cones in evaluation space, and not bounded sets. It is also concordant with the paradigm of computing with words, as it exploits ordinal, and not necessarily cardinal, character of data.