Bio-medical Ontologies Maintenance and Change Management

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20 N. Cannata et al. systems. WSDL and SOAP are W3C standards. Orchestration and choreography [21], with their languages (respectively WSBPEL 13 and WSCL 14 ), represent another advantage for Web Services composition for the fulfillment of peculiar tasks. The automation, both in the discovery and in the access to the services still remains a problem to solve. SOAP-based interfaces have been developed for a wide variety of bioinformatics applications. BioMOBY [65] is an international, Open Source Web Service integration project. The MOBY-S system defines an ontology-based messaging standard through which a client (e.g. Taverna [49]) is able to automatically discover and interact with task-appropriate biological data and analytical service providers, without requiring manual manipulation of data formats as data flows from one provider to the next. Also the BioMOBY registry is becoming semantic 15 . 2.3 Software Agents Agent-oriented software development is a software engineering approach for designing complex, flexible applications in terms of agents [64]. Agents can be considered as a distinct kind of software abstraction, in the same way that methods, functions and objects are software abstractions. More specifically, an agent is a high-level software abstraction that provides a convenient and powerful way to describe a complex software entity in terms of its behaviour within a contextual computational environment [46]. An agent is a computer system capable of flexible, autonomous problem-solving actions; it is capable of operating as a stand-alone process, and performing actions without user intervention by maintaining a description of its own processing state and the state of environment in which it is situated [39]. The kinds of resources available in the bioinformatics domain, with numerous databases and analysis tools independently administered in geographically distinct locations, lend themselves almost ideally to the adoption of a multi-agent approach [46]. Here, the environment is open, distributed and dynamic, with resources entering and leaving the system over time. There are likely to be large numbers of interactions between entities for various purposes, and the need for automation is substantial and pressing. In [46] are reported some significant experiences in adopting agents in bioinformatics, computational and systems biology. Biological systems are complex and consist of a huge set of components interacting with each other and with an external (dynamic) environment. A conceptual framework for engineering an agent society that simulates the behavior of a biological system has been proposed in [15]. The research in this area is today very active and numerous are the attempts of modeling cellular processes and systems with a multi-agents approach. 13 http://www.oasis-open.org/committees/wsbpel/ 14 http://www.w3.org/TR/wscl10/ 15 http://semanticmoby.org/
2.4 Semantic Web Towards Bioinformatics Resourceomes 21 Ontologies and intelligent agents constitute the foundations of the Semantic Web. The Semantic Web is not a separate Web but an extension of the current one, in which information is given well-defined meaning, better enabling computers and people to work in cooperation [6]. Whereas the current Web provides links between pages that are designed for human consumption, the Semantic Web augments this with pages designed to contain machinereadable descriptions of Web pages and other Web resources [37]. In the foreseeable future the web of (semantic) links between documents, databases, and programs can provide a new level of interaction among scientific communities. The Semantic Web uses new Web languages based on RDF 16 (the Resource Description Framework), which go beyond the presentation capabilities of HTML. OWL 17 is a language for making ontological statements, developed as a follow-on from RDF and RDFS, as well as earlier ontology language projects including OIL, DAML and DAML+OIL. OWL is intended to be used over the World Wide Web, and all its elements (classes, properties and individuals) are defined as RDF resources, and identified by URI 18 . A new generation of (Semantic Web) browsers will raise, not only to render the semantic documents visually but also to aggregate(mashup [13]) information referenced by documents and data objects [47]. Based on the semantic browser Haystack [55], Biodash [48] attempts to aggregate heterogeneous yet related facts and statements concerning drug development into an intuitive, visually descriptive and interactive display. The W3C consortium itself, in its mission to drive the WWW toward a Semantic Web, founded a Special Interest Group (SIG) in Semantic Web for Health Care and Life Science (HCLSIG 19 ). Bringing together scientists, medical researchers, science writers and computer scientists coming from academia, government, no-profit organizations, and industry, this group intends to facilitate the development of future standards and tools [56]. A relevant example of Semantic Web for life science is FungalWeb [1], whose supporting ontology integrates various biological database schemas, web accessible textual resources, domain specific ontologies and experts’ knowledge. Another Semantic Web ongoing project in life science (neuromedicine) is SWAN [32], attempting to develop a practical, semantically structured, web-compatible framework for scientific discussion using Semantic Web technologies. In particular, a public ontology about scientific reasoning and aimed to discovery life-cycle (including concepts like hypotheses, experiments, claims, models, documents) has been represented in RDF to support group sharing and collaboration, as well as personal and community knowledge-based construction. Related to neuroscience, it is also the 16 http://www.w3.org/RDF/ 17 http://www.w3.org/2004/OWL/ 18 http://www.faqs.org/rfcs/rfc2396.html 19 http://www.w3.org/2001/sw/hcls/
Page 1 and 2: Amandeep S. Sidhu and Tharam S. Dil
Page 3 and 4: Amandeep S. Sidhu and Tharam S. Dil
Page 5 and 6: Preface The molecular biology commu
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Page 9 and 10: X Contents Mining Clinical, Immunol
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Page 19 and 20: Towards Bioinformatics Resourceomes
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72 L. Stanescu, D. Dan Burdescu, an
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Bio-medical Ontologies Maintenance
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TextToOnto (Maedche and Volz 2001)
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Extraction of Constraints from Biol
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Classifying Patterns in Bioinformat
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Mining Clinical, Immunological, and
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Substructure Analysis of Metabolic
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entry compound relation subtype com
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Running Time 4500 4000 3500 3000 25
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6 Conclusion Substructure Analysis
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Completing the Total Wellbeing Puzz
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296 M. Fernandez, M. Villasana, and
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Genetic Algorithm in Ab Initio Prot
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Author Index Apiletti, Daniele 169
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Bio-medical Ontologies Maintenance and Change Management

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