Formal terminology management for language based knowledge systems resistance is futile
The traditional i.e. non formal approach to terminology management, including multi lingual approaches supported by computers, is focused around the appropriate use of terms in the right context, and the internal organisation of terminology collections by means of relations such as narrower than, broader than, part of, etc. Such an organisation makes a terminology manageable for humans and has proved to be useful for better translations, easier and more accurate writing of documentation, and so on. However, this approach is insufficient when demands are more complex such as automated verification of very large terminology collections or semantic based document processing. This is where formal terminologies come into play: they are primarily designed to be understood by machines, and not by humans. In this paper, we address specifically the need for formal terminologies in extremely large domains such as healthcare. The number of concepts in healthcare is estimated to be in the range of 10 to 20 million, a collection that never can be maintained in the traditional way. But when properly managed by using the right tools, a wealth of possibilities becomes available to overcome the burden of today's knowledge overload. 1. Language based knowledge systems In business and manufacturing, three components have been considered extremely important: people, money and resources. Recently, a fourth component has been added: knowledge. In the consulting business, it has even become the most important component of all. What is knowledge, and where does it come from? A traditional view is the knowledge production cycle. At the beginning, there are raw unprocessed data. Once collected and formatted, they can be processed in such a way that relationships become visible: data are turned into information that can be used to improve business or manufacturing processes. The more information that is disclosed, the more clever we become, until so much information (inside a domain) is disclosed, that a level of deep understanding is reached: the knowledge level. Having this knowledge, it becomes easier to derive more and better information from the data. We might even control the events that produce the data. Becoming knowledgeable is itself a matter of moving from one state into another (the "knowledge microprocess" observation, understanding, prediction, application, justification. The ultimate goal (hopefully) is to reach the level of wisdom. This level can only be reached when sufficient knowledge from various domains has been acquired. Out of this insight, various technologies have emerged. Information Technology can be defined as a supporting technology to turn data into information. Knowledge Engineering is a similar discipline at the level of information and knowledge. Language Engineering can be seen as a special branch of knowledge engineering, dealing with knowledge in the form of language. Finally, Language Based Knowledge Systems are systems in which most of the knowledge is stored in free text as in Document Management Systems, but that differ from the latter in their ability to actively perhaps even "consciously" participate in the knowledge production cycle. As such, Language Based Knowledge Systems have a sufficient level of Ceusters W. Formal terminology management for language based knowledge systems: resistance is futile. In Temmerman R. (ed) Trends in Special Language and Language Technology, 2001;:135 53 "understanding" of the domain covered by the texts they contain, to assist users in adequately storing and retrieving information. 2. Healthcare as a knowledge intensive environment 1 with respect to size Medicine is one of these complex domains where new knowledge is accumulated at a daily basis, and at an exponential rate. Most of this knowledge resides in textbooks and papers, or more loosely structured in patient records. Despite the growing tendency to make this knowledge available in electronic format, the era of large scale knowledge based systems does not seem to have dawned yet. On the one hand, we are perhaps close, issues such as complexity and expressive power of knowledge representations being better understood. On the other hand, there is still a very long way to go as representing large quantities of knowledge is a major bottleneck if we ever want to develop systems that don't "fall of the knowledge cliff" [1] One of the research domains that might come up with solutions for the knowledge acquisition bottleneck is natural language understanding. A necessary condition is however that systems could be build that transform sentences into a meaning representation that is independent of the subtleties of linguistic structure that nevertheless underlie the way language works [2] Yet, here also is a bottleneck, be it in the form of a chicken and egg problem. Systems showing this much wanted behaviour must base their inferences on knowledge already available to them. The knowledge required comes in two different flavours. First, there is that kind of knowledge that is described as "linguistic semantics" the rules and principles explaining how literal meaning is grammaticalised or encoded in language [3] It is this kind of knowledge that enables us for instance to understand the "sense" of an expression or sentence, i.e. the set (or network) of sense relations that hold between it and other expressions within the same language [4] and that allows us to identify the same meaning independent of whether a given sentence is in the passive or active form. This knowledge is different from "conceptual semantics" or "conceptual knowledge" that describes what entities there are (supposed to be) in the world that can be denoted by language. 2 with respect to communication needs The pharmaceutical industry is well aware of the importance of effective knowledge and information management. Bringing a new drug to the market is a multi stage process that typically takes between 7 and 15 years. Huge amounts of information have to be gathered, analysed and communicated. Between 100 and 1000 people intervene somewhere in the drug development life cycle: feasibility studies, planning, clinical trial monitoring, medical writing, regulatory affairs, post marketing surveillance, pharmacovigilance, etc. Tens of thousands of documents are generated and have to be analysed. The pharmaceutical industry is also a multi national business. This means not only that multiple languages have to be addressed, but also that effective communication channels have to be set up across the language borders. Communicating information is an essential activity in the whole healthcare domain. Patients need to be informed by their doctors to what diagnostic or therapeutic Ceusters W. Formal terminology management for language based knowledge systems: resistance is futile. In Temmerman R. (ed) Trends in Special Language and Language Technology, 2001;:135 53 procedures they will be submitted in order to make them feel more comfortable in the often threatening environment of the hospital. Also nurses need to be informed on what happened to the patients there are responsible for before taking up their shift. It is mandatory that this exchange of information is done in an ambiguous, accurate and reproducible way. This is not always so easy because language itself the prime vehiculum in information interchange is difficult to use unambiguously. In addition, Europe is moving towards a global multilingual community in which from a functional perspective, national borders tend to fade. More often communication is required with colleagues speaking different languages, or having another cultural and educational background. Given the rather limited capacities of the human brain in storing and retrieving large quantities of factual data, the same information must also be registered in patient records for subsequent consultation. By using electronic patient records, some additional functional requirements for this kind of "external memories" became apparent: in one way or another, the information has to be understandable by machines, such that linking to other applications or information sources can be achieved nearly automatically. Unfortunately, computers don't speak natural language (yet) and they also have little knowledge of medicine. To overcome the problems related to the use of natural language in medical communication and clinical registration, terminology collections in the form of coding and classification systems have been introduced as interlingua. Systems such as ICD, Snomed International, ICPC, CPT and many others are now widely used to register medical findings, diagnoses or procedures. Similarly, so called terminological systems such as NIC, NANDA, ICNP and others are proposed to be used as interlingua in a nursing environment. The question of course is whether or not such systems are the right solutions to overcome the problems stated previously. After all, each of these systems is designed with a specific purpose in mind such as mortality and morbidity statistics, reimbursement, information retrieval to mention only three. They very seldom are detailed enough for a faithful registration of all relevant clinical data. And at least in their current (paper) format, they are a burden to use. 3. Non formal terminology In [5] terminology is defined as the study and the field of activity concerned with the collection, description, processing and presentation of terms belonging to specialised areas of usage of one or more languages. Central in this definition is the notion of terms, i.e. verbal representations of the things we speak or write about. Terminology differs from lexicology in the sense that only the terms pertaining to a specific domain are considered. Three dimensions need to be considered when developing terminologies: the cognitive dimension, the linguistic dimension, and the communicative dimension. Also when existing