Document analysis based automatic concept map generation for enterprises

Abstract

Ever growing knowledge bases of enterprises present the demanding challenge of proper organization of information that would enable fast retrieval of related and intended information. Document repositories of enterprises consist of large collections of documents of varying size, format and writing styles. This diversified and unstructured nature of documents restrict the possibilities of developing uniform techniques for extracting important concepts and relationships for summarization, structured representation and fast retrieval. The documented textual content is used as the input for the construction ofa concept map. Here a rule based approach is used to extract concepts and relationships among them. Sentence level breakdown enables these rules to identify those concepts and relationships. These rules are based on elements in a phase structure tree of a sentence. For improving accuracy and the relevance of the extracted concepts and relationships, the special features such as titles, bold and upper case texts are used. This paper discusses how to overcome the above mentioned challenges by utilizing high level natural language processing techniques, document pre-processing techniques and developing easily understandable and extractable compact representation of concept maps. Each document in the repository is converted to a concept map representation to capture concepts and relationships among concepts described in the said document. This organization would represent a summary of the document. These individual concept maps are utilized to generate concept maps that represent sections of the repository or the entire document repository. This paper discusses how statistical techniques are used to calculate certain metrics which are used to facilitate certain requirements of the solution. Principle component analysis is used in ranking the documents by importance. The concept map is visualized using force directed type graphs which represent concepts by nodes and relationships by edges.