Chris Wroe

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.

A suite of DAML+OIL ontologies to describe bioinformatics web services and data

The growing quantity and distribution of bioinformatics resources means that finding and utilizing them requires a great deal of expert knowledge, especially as many resources need to be tied together into a workflow to accomplish a useful goal. We want to formally capture at least some of this knowledge within a virtual workbench and middleware framework to assist a wider range of biologists in utilizing these resources. Different activities require different representations of knowledge. Finding or substituting a service within a workflow is often best supported by a classification. Marshalling and configuring services is best accomplished using a formal description. Both representations are highly interdependent and maintaining consistency between the two by hand is difficult. We report on a description logic approach using the web ontology language DAML+OIL that uses property based service descriptions. The ontology is founded on DAML-S to dynamically create service classifications. These classifications are then used to support semantic service matching and discovery in a large grid based middleware project . We describe the extensions necessary to DAML-S in order to support bioinformatics service description; the utility of DAML+OIL in creating dynamic classifications based on formal descriptions; and the implementation of a DAML+OIL ontology service to support partial user-driven service matching and composition.

Using semantic web technologies for representing E-science provenance

Life science researchers increasingly rely on the web as a primary source of data, forcing them to apply the same rigor to its use as to an experiment in the laboratory. The Grid project is developing the use of workflows to explicitly capture web-based procedures, and provenance to describe how and why results were produced. Experience within Grid has shown that this provenance metadata is formed from a complex web of heterogenous resources that impact on the production of a result. Therefore we have explored the use of Semantic Web technologies such as RDF, and ontologies to support its representation and used existing initiatives such as Jena and LSID, to generate and store such material. The effective presentation of complex RDF graphs is challenging. Haystack has been used to provide multiple views of provenance metadata that can be further annotated. This work therefore forms a case study showing how existing Semantic Web tools can effectively support the emerging requirements of life science research.

A methodology to migrate the gene ontology to a description logic environment using DAML+OIL.

The Gene Ontology Next Generation Project (GONG) is developing a staged methodology to evolve the current representation of the Gene Ontology into DAML+OIL in order to take advantage of the richer formal expressiveness and the reasoning capabilities of the underlying description logic. Each stage provides a step level increase in formal explicit semantic content with a view to supporting validation, extension and multiple classification of the Gene Ontology. The paper introduces DAML+OIL and demonstrates the activity within each stage of the methodology and the functionality gained.

Learning domain ontologies for Web service descriptions: an experiment in bioinformatics

The reasoning tasks that can be performed with semantic web service descriptions depend on the quality of the domain ontologies used to create these descriptions. However, building such domain ontologies is a time consuming and difficult task.We describe an automatic extraction method that learns domain ontologies for web service descriptions from textual documentations attached to web services. We conducted our experiments in the field of bioinformatics by learning an ontology from the documentation of the web services used in myGrid, a project that supports biology experiments on the Grid. Based on the evaluation of the extracted ontology in the context of the project, we conclude that the proposed extraction method is a helpful tool to support the process of building domain ontologies for web service descriptions.

Feta: a light-weight architecture for user oriented semantic service discovery

Semantic Web Services offer the possibility of highly flexible web service architectures, where new services can be quickly discovered, orchestrated and composed into workflows. Most existing work has, however, focused on complex service descriptions for automated composition. In this paper, we describe the requirements from the bioinformatics domain which demand technically simpler descriptions, involving the user community at all levels. We describe our data model and light-weight semantic discovery architecture. We explain how this fits in the larger architecture of the myGrid project, which overall enables interoperability and composition across, disparate, autonomous, third-party services. Our contention is that such light-weight service discovery provides a good fit for user requirements of bioinformatics and possibly other domains.

Learning domain ontologies for semantic Web service descriptions

High quality domain ontologies are essential for successful employment of semantic Web services. However, their acquisition is difficult and costly, thus hampering the development of this field. In this paper we report on the first stage of research that aims to develop (semi-)automatic ontology learning tools in the context of Web services that can support domain experts in the ontology building task. The goal of this first stage was to get a better understanding of the problem at hand and to determine which techniques might be feasible to use. To this end, we developed a framework for (semi-)automatic ontology learning from textual sources attached to Web services. The framework exploits the fact that these sources are expressed in a specific sublanguage, making them amenable to automatic analysis. We implement two methods in this framework, which differ in the complexity of the employed linguistic analysis. We evaluate the methods in two different domains, verifying the quality of the extracted ontologies against high quality hand-built ontologies of these domains. Our evaluation lead to a set of valuable conclusions on which further work can be based. First, it appears that our method, while tailored for the Web services context, might be applicable across different domains. Second, we concluded that deeper linguistic analysis is likely to lead to better results. Finally, the evaluation metrics indicate that good results can be achieved using only relatively simple, off the shelf techniques. Indeed, the novelty of our work is not in the used natural language processing methods but rather in the way they are put together in a generic framework specialized for the context of Web services.

Automating experiments using semantic data in a bioinformatics grid

The transition from laboratory science to in silico e-science has facilitated a paradigmatic shift in the way we conduct modern science. We can use computationally based analytical models to simulate and investigate scientific questions such as those posed by high-energy physics and bioinformatics, yielding high-quality results and discoveries at an unprecedented rate. However, while experimental media have changed, the scientific methodologies and processes we choose for conducting experiments are still relevant. As in the lab environment, experimental methodology requires samples to undergo several processing stages. The staging of operations is what constitutes the in silico experimental process. The use of workflows formalizes earlier ad hoc approaches for representing experimental methodology. We can represent the stages of in silico experiments formally as a set of services to invoke.

On the use of agents in a BioInformatics grid

My Grid is an e-Science Grid project that aims to help biologists and bioinformaticians to perform workflow-based in silico experiments, and help them to automate the management of such workflows through personalisation, notification of change and publication of experiments. In this paper, we describe the architecture of my Grid and how it will be used by the scientist. We then show how my Grid can benefit from agents technologies. We have identified three key uses of agent technologies in my Grid: user agents, able to customize and personalise data, agent communication languages offering a generic and portable communication medium, and negotiation allowing multiple distributed entities to reach service level agreements.

Ontologies in Bioinformatics

Molecular biology offers a large, complex and volatile domain that tests knowledge representation techniques to the limit of their fidelity, precision, expressivity and adaptability. The discipline of molecular biology and bioinformatics relies greatly on the use of community knowledge, rather than laws and axioms, to further understanding, and knowledge generation. This knowledge has traditionally been kept as natural language. Given the exponential growth of already large quantities of data and associated knowledge, this is an unsustainable form of representation. This knowledge needs to be stored in a computationally amenable form and ontologies offer a mechanism for creating a shared understanding of a community for both humans and computers. Ontologies have been built and used for many domains and this chapter explores their role within bioinformatics. Structured classifications have a long history in biology; not least in the Linnean description of species. The explicit use of ontologies, however, is more recent. This chapter provides a survey of the need for ontologies; the nature of the domain and the knowledge tasks involved; and then an overview of ontology work in the discipline. The widest use of ontologies within biology is for conceptual annotation — a representation of stored knowledge more computationally amenable than natural language. An ontology also offers a means to create the illusion of a common query interface over diverse, distributed information sources — here an ontology creates a shared understanding for the user and also a means to computationally reconcile heterogeneities between the resources. Ontologies also provide a means for a schema definition suitable for the complexity and precision required for biology’s knowledge bases. Coming right up to date, bioinformatics is well set as an exemplar of the Semantic Web, offering both web accessible content and services conceptually marked up as a means for computational exploitation of its resources — this theme is explored through the myGRID services ontology. Ontologies in bioinformatics cover a wide range of usages and representation styles. Bioinformatics offers an exciting application area in which the community can see a real need for ontology based technology to work and deliver its promise.