Constructing tree-based knowledge structures from text corpus

Abstract

A knowledge structure identifies how people think and displays a macro view of human perception. By discovering the hidden structural relations of knowledge, significant reasoning patterns are retrieved to enhance further knowledge sharing and distribution. However, the utilization of such approaches is apt to be limited due to the lack of hierarchical features and the problem of information overload, which make it difficult to enhance comprehension and provide effective navigation. To address these critical issues, we propose a new approach to construct a tree-based knowledge structure from corpus which can reveal the significant relations among knowledge objects and enhance user comprehension. The effectiveness of the proposed method is demonstrated with two representative public data sets. The evaluation results show that the method presented in this work achieves remarkable consistency with the domain-specific knowledge structure, and is capable of reflecting appropriate similarities among knowledge objects along with hierarchical implications in the document classification task.

Does knowledge structure matter? Key factors influencing formal and informal knowledge sharing in manufacturing

“…Combined with previous study of knowledge structure (Eppler and Burkhard, 2007), this study clearly defines the level of knowledge structure and verifies the role of knowledge structure level in KS. The findings of this study are consistent with Li and Tsai (2010) and Khalifa and Shen (2010), who claim that the complexity and representation of knowledge structure, which are included in the level of knowledge structure proposed in this study, have an important impact on knowledge transfer and distribution. This study further clarifies that this effect is a positive effect, that is, the higher the level of knowledge structure, the stronger the promotion of KS.…”

“…Company staff and employees as knowledge workers require support and guidelines for KS and codification processes within the KM framework (Eppler and Platts, 2009). However, how to fully organize knowledge so as to obtain a systematic framework that can represent the entire specific domain knowledge and can have efficient problem-solving capabilities is a considerable challenge (Li and Tsai, 2010). Early research discussed different knowledge structures and forms (Eppler and Burkhard, 2007), including structured text and tables, heuristic sketches, conceptual diagrams and knowledge maps.…”

A fuzzy conceptualization model for text mining with application in opinion polarity classification

“…In order to imitate the remarkable accuracy and robustness of the human brain when dealing with natural language problems, FCA is designed to combine and organize concepts into a hierarchical ordered conceptual lattice. Although the complexity of lattices is wildly discussed in previous research [4,26,27,44], their interconnected structure is assumed to be one way to disambiguate the meaning of text with respect to the collective meaning of the context [43]. FCA is capable of providing different perspectives of formal concepts, from general to specific, according to the content of documents.…”

Algorithmic labeling in hierarchical classifications of publications: Evaluation of bibliographic fields and term weighting approaches

“…Classifications of research publications have some special features. Bibliographic databases such as Web of Science, Scopus and Dimensions contain information about journal articles and are more structured than most other sources commonly used in the algorithmic clustering literature, for example web pages, blog posts, Wikipedia pages or text documents (Carmel, Roitman, & Zwerdling, 2009;Hennig, Berger, Steuer, Wuerz, & Meinel, 2014; S.-T. Li & Tsai, 2010). In hierarchical classifications the subject orientation of classes needs to be determined at different semantic levels.…”