Alan L. Rector

Relations in biomedical ontologies

To enhance the treatment of relations in biomedical ontologies we advance a methodology for providing consistent and unambiguous formal definitions of the relational expressions used in such ontologies in a way designed to assist developers and users in avoiding errors in coding and annotation. The resulting Relation Ontology can promote interoperability of ontologies and support new types of automated reasoning about the spatial and temporal dimensions of biological and medical phenomena.

The GRAIL concept modelling language for medical terminology

The GALEN representation and integration language (GRAIL) has been developed to support effective clinical user interfaces and extensible re-usable models of medical terminology. It has been used successfully to develop the prototype GALEN common reference (CORE) model for medical terminology and for a series of projects in clinical user interfaces within the GALEN and PEN&PAD projects. GRAIL is a description logic or frame language with novel features to support part-whole and other transitive relations and to support the GALEN modelling style aimed at re-use and application independence. GRAIL began as an experimental language. However, it has clarified many requirements for an effective knowledge representation language for clinical concepts. It still has numerous limitations despite its practical successes. The GRAIL experience is expected to form the basis for future languages which meet the same requirements but have greater expressiveness and more soundly based semantics. This paper provides a description and motivation for the GRAIL language and gives examples of the modelling paradigm which it supports.

Web ontology segmentation: analysis, classification and use

Ontologies are at the heart of the semantic web. They define the concepts and relationships that make global interoperability possible. However, as these ontologies grow in size they become more and more difficult to create, use, understand, maintain, transform and classify. We present and evaluate several algorithms for extracting relevant segments out of large description logic ontologies for the purposes of increasing tractability for both humans and computers. The segments are not mere fragments, but stand alone as ontologies in their own right. This technique takes advantage of the detailed semantics captured within an OWL ontology to produce highly relevant segments. The research was evaluated using the GALEN ontology of medical terms and procedures.

OWL Pizzas: Practical Experience of Teaching OWL-DL: Common Errors & Common Patterns

Understanding the logical meaning of any description logic or similar formalism is difficult for most people, and OWL-DL is no exception. This paper presents the most common difficulties encountered by newcomers to the language, that have been observed during the course of more than a dozen workshops, tutorials and modules about OWL-DL and it’s predecessor languages. It emphasises understanding the exact meaning of OWL expressions – proving that understanding by paraphrasing them in pedantic but explicit language. It addresses, specifically, the confusion which OWL’s open world assumption presents to users accustomed to closed world systems such as databases, logic programming and frame languages. Our experience has had a major influence in formulating the requirements for a new set of user interfaces for OWL the first of which are now available as prototypes. A summary of the guidelines and paraphrases and examples of the new interface are provided. The example ontologies are available online.

The galen project

The GALEN project is developing language independent concept representation systems as the foundations for the next generation of multilingual coding systems. It aims to support the flexibility required to cope with the diversity amongst medical applications, while ensuring the coherence necessary for integration and re-use of terminologies. GALEN is developing a fully compositional and generative formal system for modelling concepts: the GALEN Representation and Integration Language (GRAIL) Kernel. Its goal is to overcome many of the problems with traditional coding and classification systems, in particular the combinatorial explosion of terms in enumerative systems and the generation of nonsensical terms in partially compositional systems. It will also provide a clean separation between the concept model and linguistic mechanisms which interpret that model (i.e., the words in a specific language, syntax, alternative phrasings, etc.) in order to allow the development of multilingual systems. GRAIL aims to be formally sound and produce models that are verifiable and contain no contradictions or ambiguities, with realistic human effort. A Coding Reference (CORE) Model of medical terminology covering is being developed which aims to represent the core concepts in for example pathology, anatomy and therapeutics, that have widespread applicability in medical applications. It should also provide the basis for specialist extensions according to the formal principles of GRAIL. The main results of GALEN will be delivered as a Terminology Server (TeS) which encapsulates and coordinates the functionality of the concept module, multilingual module, and code conversion module, and also provides a uniform applications programming interface and network services for use by external applications.

Using OWL to model biological knowledge

Much has been written of the facilities for ontology building and reasoning offered for ontologies expressed in the Web Ontology Language (OWL). Less has been written about how the modelling requirements of different areas of interest are met by OWL-DLs underlying model of the world. In this paper we use the disciplines of biology and bioinformatics to reveal the requirements of a community that both needs and uses ontologies. We use a case study of building an ontology of protein phosphatases to show how OWL-DLs model can capture a large proportion of the communitys needs. We demonstrate how Ontology Design Patterns (ODPs) can extend inherent limitations of this model. We give examples of relationships between more than two instances; lists and exceptions, and conclude by illustrating what OWL-DL and its underlying description logic either cannot handle in theory or because of lack of implementation. Finally, we present a research agenda that, if fulfilled, would help ensure OWLs wider take up in the life science community.

Binding ontologies and coding systems to electronic health records and messages

A major use of medical ontologies is to support coding systems for use in electronic healthcare records and messages. A key task is to define which codes are to be used where - to bind the terminology to the model of the medical record or message. To achieve this formally, it is necessary to recognise that the model of codes and information models are at a meta-level with respect to the underlying ontology. A methodology for defining a Code Binding Interface in OWL is presented which illustrates this point. It generalises methodologies that have been used in a successful test of the binding of HL7 messages to SNOMED CT codes.

Ontological and Practical Issues in Using a Description Logic to Represent Medical Concept Systems: Experience from GALEN

GALEN seeks to provide re-usable terminology resources for clinical systems. The heart of GALEN is the Common Reference Model (CRM) formulated in a specialised description logic. The CRM is based on a set of principles that have evolved over the period of the project and illustrate key issues to be addressed by any large medical ontology. The principles on which the CRM is based are discussed followed by a more detailed look at the actual mechanisms employed. Finally the structure is compared with other biomedical ontologies in use or proposed.

Why Do It the Hard Way? The Case for an Expressive Description Logic for SNOMED

There has been major progress both in description logics and ontology design since SNOMED was originally developed. The emergence of the standard Web Ontology language in its latest revision, OWL 1.1 is leading to a rapid proliferation of tools. Combined with the increase in computing power in the past two decades, these developments mean that many of the restrictions that limited SNOMEDs original formulation no longer need apply. We argue that many of the difficulties identified in SNOMED could be more easily dealt with using a more expressive language than that in which SNOMED was originally, and still is, formulated. The use of a more expressive language would bring major benefits including a uniform structure for context and negation. The result would be easier to use and would simplify developing software and formulating queries.

Integration of Tools for Binding Archetypes to SNOMED CT

BackgroundThe Archetype formalism and the associated Archetype Definition Language have been proposed as an ISO standard for specifying models of components of electronic healthcare records as a means of achieving interoperability between clinical systems. This paper presents an archetype editor with support for manual or semi-automatic creation of bindings between archetypes and terminology systems.MethodsLexical and semantic methods are applied in order to obtain automatic mapping suggestions. Information visualisation methods are also used to assist the user in exploration and selection of mappings.ResultsAn integrated tool for archetype authoring, semi-automatic SNOMED CT terminology binding assistance and terminology visualization was created and released as open source.ConclusionFinding the right terms to bind is a difficult task but the effort to achieve terminology bindings may be reduced with the help of the described approach. The methods and tools presented are general, but here only bindings between SNOMED CT and archetypes based on the openEHR reference model are presented in detail.