Logic-based remodeling of the Digital Anatomist Foundational Model.

Logic-based Remodeling of the Digital Anatomist Foundational Model Rainer Beck Stefan Schulz Freiburg University Hospital, Department of Medical Informatics (http://www.imbi.uni-freiburg.de/medinf) Abstract This paper describes a development cycle for the engineering of large knowledge bases: A graphical tool is used for editing and the content is transformed into a logic-based representation language. This representation is used to check the consistency of the knowledge base as well as to facilitate the reviewing process. Showing the usefulness of this approach, aspects of the Digital Anatomist Foundational Model will be transformed into a Description Logics representation. We introduce a special modeling technique to account for the representation of the complex part/whole relationships in the biomedical domain. INTRODUCTION As more and more facts are gathered in the domain of life sciences, so do biomedical concept systems grow. The emphasis in most of these systems is on the comprehensiveness of coverage; only a few formalize concept representation. The UMLS [15] is a specific example since it merges vocabularies which differ in terms of conceptualization, resulting in poorly defined semantics. This deficiency can be largely attributed to the different contexts of its source vocabularies. However, there now is an increased trend towards strict semantics, resulting in concept systems capable of supporting formal reasoning [8, 14]. Physiology, pathology, molecular biology and other subdomains are characterizable by the change they inflict on underlying physical structure. Therefore formal representation of the structural sciences (like anatomy, structural biology) is particularly important. So it is to be expected that an elaborated, common structural model would facilitate the conceptualization and representation of all biomedical domains. Moreover this constitutes a critical requirement for a principled representation of knowledge about diagnostic and therapeutic procedures. As a representation of anatomical structure, the Digital Anatomist Foundational Model (FM) [11] is outstanding in respect to coverage and formal strictness: It describes canonical anatomical knowledge using rather precise semantics. The design of the model is guided by the very tight formal Aristotelian principles of genus and differentiae [5] (most prominently imple- mented in the upper level ontology). Taxonomic and partonomic hierarchies are strictly separated. Because of its coverage (more than 67,000 concepts) and its design principles, the FM presents an ideal candidate to be represented in a logic-based language. Whereas the taxonomic structure of the FM may be already interpreted in terms of a formal ontology, the semantics of the relations part-of and has-part needs to be further refined. This is particularly desirable since various fundamental properties of biological entities (like contains, has-function) interact with part / whole relationships between concepts. Therefore, the expressive representation of partonomy is of paramount importance. As a consequence of the comprehensiveness of the FM, ameliorating the part/whole relationships proves to be a complex and challenging task: Small changes of the partonomy may escalate throughout the hierarchy and lead to arbitrarily complex results. In this paper, we propose a development cycle suitable for the engineering of large knowledge bases obeying our principles of “single point of edit” and “minimum manual intervention”. Furthermore, we apply this method to the Foundational Model and show how to transform its taxonomy and partonomy into a representation language with formal semantics based on description logics (DL) [1]. We use a distinctive modeling pattern to address the specific needs for representing part/whole relations. DEVELOPMENT CYCLE The engineering of large knowledge bases on code level can be a daunting if not impossible task. To support the knowledge engineer in the process, we propose the following cycle (cf. Figure 1): 1. Concept Creation or Change In order to effect all modifications in a precise manner, the knowledge engineer must be able to identify the context he or she is working in: Which concepts are defined? Which relations hold between them? This process can be facilitated if there exists a comprehensive graphical representation including search and navigation facilities sufficient for the task. However, significant advantage will be lost if parts of the editing are left to be made outside this AMIA 2003 Symposium Proceedings − Page 71 ! ! ! Figure 1: Development Cycle environment because of lack of support. The “single point of edit” provided by the environment should not be compromised. We consider the kind of environment described a necessity for working with large knowledge bases. 2. Consistency Check After changing or adding to the knowledge represented, consistency checks should be performed: Semantic integrity is easily lost in large knowledge bases, especially when role propagation 1 along several axes is involved. Therefore, we propose at least semi-automatical checks by an inference engine which can provide a status report. Thus, inconsistencies as well as terminological cycles will be identified and can be corrected by iterating the first step. It is also highly desirable to integrate these mechanisms into the editing environment for automatic consistency checks. 3. Adequacy Check For good reasons, implicit knowledge as well as complete role propagation are usually not fully accessible in a knowledge editing tool. As these inferences are prerequisites for checking the adequacy of the representation entered, they have to be made accessible to the knowledge engineer: Any inference engine used should provide a suitable display interface to allow the analysis of all concepts and the accuracy of their representation by the knowledge engineer. Should the representation be incomplete or inadequate, steps one and two might be repeated until the results are satisfactory. 4. Completeness Check The criteria of adequacy being satisfied, the check for completeness may take place in which the knowledge engineer compares the coverage in the representation with the domain to be described. The display and browsing tools required for checking adequacy may be used for this purpose. Eventually, the process results in a valid and comprehen1 Inheritance sive knowledge base representing the domain of interest. If the transition between the steps described is well automated, it will be possible to handle large and very large knowledge bases with “minimum manual intervention”. The danger of losing track of the modifications can be minimized and their respective effects on the representation can be controlled at the same time. The merging of all steps into an editing tool would be the ultimate solution. However, considering the complexity involved with reasoning, online processing and feedback seems rather unlike (...truncated)