An ontological framework for the formalization, organization and usage of TCM-Knowledge

Background: The traditional Chinese Medicine Language System (TCMLS) is a large-scale terminology system, developed from 2002 on by the Institute of Information of Traditional Chinese Medicine (IITCM). Until now, more than 120,000 concepts, 300,000 terms and 1.27 million semantic relational links are included. Its top-level framework, called TCMLS-semantic network (SN), provides an important basis for the standardization and mapping of traditional Chinese Medicine (TCM) terminology systems. Though, many data produced and stored in TCMLS have poor quality for historical reasons or because of human factors. There is a large number of classification errors or inconsistent expressions of terms remained in the current TCMLS- SN, which hamper an efficient utilization of the data stored in TCMLS in practical applications.

Methods: We start with analyzing the technical specification based on TCMLS, considering some obvious classification errors and problems of ambiguity of semantic expressions in TCMLS-SN, followed with using a top-down approach for building a middle level ontology which is based on the framework General Formal Ontology (GFO), take into account the compatibility with TCM related concepts, turn out the results of a modification of the current TCMLS-SN, called GFO-TCM.

Results: Through comparison with TCMLS-SN, according to viewpoints of GFO, some semantic types and relations were reconstructed within GFO-TCM. We propose a middle level ontology for TCMLS which may support entailment and ensure coherence, we also draw out a mapping which possess a more reasonable framework with a unified semantic criterion, it is application scenarios oriented and can be further updated and extended.

Conclusions: The goal is to construct a formal middle-level ontology that is compatible with both the traditional medical terminology system and modern medical terminology standards. it is intended to satisfy functional requirements which are relevant for natural language processing, information extraction, semantic retrieval, clinical decision support in the field of traditional Chinese medicine. It also provides a foundation and methodology for building a large-scale, unified semantic and extensible knowledge graph platform.