A SOCIO-TECHNICAL PERSPECTIVE ON ONTOLOGY DEVELOPMENT IN

The paper focuses on the alignment of technical and human infrastructures in the semantic web, taking the development and alignment of ontologies in eHealth as a vehicle for exploring the barriers to achieving this. The authors report on the outcomes of a recent eScience workshop looking at data integration and ontology alignment across Grid projects in the same disease domain. It is premised on a view of Grids as socio-technical systems aligning technical and human networks in ways which can create useful synergies, but may also work at cross purposes, generating unpredicted costs and risks and barriers. 1. Socio-technical Alignment The eHealth vision of large-scale, seamless data-sharing for research and clinical trials faces real barriers in the development of shared infrastructures for collecting, coding, cleaning and representing data across multiple distributed communities of practice. Ontology development is a significant strand in that vision, premised on semantic alignment, and the collective definition and representation of terms and frames of reference across communities. We look here at some of the more persistent semantic and systemic alignment issues in the context of neuro-psychiatric disease, as experienced by Grid projects working in this domain in the UK, EU and US, where the opportunities and the challenges of integration have been particularly evident. The paper takes a perspective on Grids and other complex, distributed networks as co-evolving sociotechnical systems [Joslyn, 1999; Kling 2003]. Their design can be seen as an opportunity to align technical and human networks to create useful synergies, as for example in the case of many Web 2.0 applications, or as a challenge, where technical and distributed human infrastructures may work at cross purposes to generate unpredicted costs and risk [Luna-Reyes 2005]. The authors draw on the outcomes of a UK eScience funded workshop bringing together multiple Grid projects in the UK, EU and US to roadmap the issues in data integration and ontology alignment in HealthGrids working in the same disease domain [Ure et al 2007]. The presentations and a shared wiki are available on the UK e-Science site. The event was co-hosted by the National e-Science Centre, the Generation Scotland national population genomics project and the NeuroGrid project developing a platform for sharing clinical and brain imaging data relating to schizophrenia, dementia and stroke. This also built on the ongoing road-mapping process to support collaboration in data sharing in particular disease domains initiated in the HealthGrid Share project. Conclusions are also derived from a recent eSocial Science workshop on ontology development in the Humanities and the Social Sciences Grids [Lin Y. and Procter R. 2007], as well as other published work on data integration and semantic alignment in eHealthGrids [Breton 2006; Wilson and Lessens 2006] and in the semantic web [Goble 2006; de Roure2006] more generally. 1.2 Issues in Ontology Harmonisation Gruber [1993] uses the term ontology to mean a specification of a conceptualization, and as ‘a description of the concepts and relationships that can exist for an agent or a community of agents. ’ This formal specification is dependent on the agreement of common terms, referents and relationships, however 1 https://wikis.nesc. ac.uk/mod/Main_Page 2 www.generationscotland. org 3 www.neurogrid. ac.uk 4 http://www.eu-share.org/ there is also a need for harmonization of the protocols, tools and other processes through which that data is derived. These are increasingly issues of concern given the vision of international, multi-centre clinical trials, translational medicine, and interoperability in the European Health Area. Several Grid projects now operate in the same disease domains, and in taking one such disease domain (schizophrenia) as a test case it was possible to engage clinicians, ontologists, imaging scientists, e-scientists, and geneticists on real-world issues in the integration of heterogeneous datasets from diverse sites, populations and working contexts. 2. Issues in Alignment The data ‘supply chain’ underpinning eHealth and translational medicine is a gradual conversion process where many types of error or bias can arise at different stages from sampling, collection, coding, aggregation, analysis or use, sometimes referred to as the ‘social life of information’[Duguid, 2000]. Fig. 1 Socio-technical Alignment Scenarios in eHealth At the level of the data itself, there were many examples of real challenges in ensuring that this could be integrated without introducing confounding effects. These derived from differences in the make or calibration of equipment for example, differences in the interpretation of scan data or psychiatric tests for example, or differences in test populations. Many of the challenges in aligning or integrating distributed datasets, and their associated metadata derive from the very diverse contexts in which they are collected, shared and used. In the first instance we will outline some of these issues in aligning data, metadata and ontologies across multiple HealthGrids. The discussion section will then pull together some of the implications of this for ontology-building, drawing also on the experiences of the ‘Building Building Ontologies for the Humanities and Social Sciences’ Workshop at the National Centre for eSocial Science (NCeSS). 2.1 Across Communities 5 http://www.ncess. ac.uk/events/ASW/ontologies/ Within the same disease domain, concepts were the Project Lifecycle lems associated with ere drawn from regional or ites: Collection, Coding and Collation d different in scope, conceptualization, scale and semantics in the different projects, and an emerging consensus was to consider identifying core measures of symptom as a starting point for alignment, rather than attempting to align disease concepts in the first instance. In addition to this, within projects teams there were difficulties in achieving domain models that met the criteria of both clinicians and ontologists, with disengagement by clinicians from the process typically