A metric for Literature-Based Discovery methodology evaluation

Abstract

Literature-Based Discovery (LBD) is the science of relating existing knowledge in literature to discover new relationships. It is sometimes referred to as hidden knowledge. This paper provides the most recent classification of the existing LBD methods relating the problem to other domains such as information retrieval. The papers identifies that Vector Space Model, Probabilistic Model, and Inference Network Model are the mostly used for LBD problem. The researchers of this paper justified their belief that there are important differences between the problem domains with regards to novelty, time factor, reasoning, and relevance. Moreover, the paper introduces the on-going work of the authors on proposing a new evaluation methodology that addresses the weaknesses of the current methodologies investigating the desirable characteristics of the future LBD evaluation methodology.