Review of Ontologies for sharing, Ontologies for use

This article is well written and structured, stressing the point that in order to achieve the maximum usability and re-purposing of knowledge contained in an ontology, the ontology should be constructed in small manageable modules with a clearly delinated scope and follow normalisation principles. This reviewer agrees with this principle of ontology design however, has the following comments:

One of the main advantages of producing modular ontologies is that not only can they be extended and constrained for a new purpose, they can also be easily integrated with other modules to produce a new, or more comprehensive ontology. Therefore more space could be given towards the benefits of modular ontology design to ontology integration.

In order to re-use or integrate ontology modules, the process will be considerably easier if the modules are built with the same design principles, such as a common set of relations of the use of an upper ontology. The paper should describe the relevance of upper ontologies and common relations, in respect to their design principles.

It is not clear that these principles are applicable to all modular ontology development (i.e Reference and application) should it be a restriction on application ontologies that they should be shared?

General observations

The article presents principles that could be applied from day one of development of a modular ontology, whereas this is the ideal situation if we were starting ontology development again, some consideration should be given to how we make use of the ontologies that exist to be more like modular ontologies. For example, identifying overlap between these ontologies, and creating modular ontologies, applying the stated principles from these overlapping terms, which could then be re-imported back into the original ontologies.

I dont believe the “world” or the “Semantic Web” as a whole can agree on a singular view of the world in one go. The development of modular ontologies, motivated by identifying overlap between existing ontologies, seems more appropriate on a domain by domain basis – and then on a domain domain overlap. The example of dc:title is given, However, a title  is a prefix or suffix added to a person’s name to signify either veneration, an official position or a professional or academic qualification. Already we have a conflict. In the domain of publishing it is clear what a title refers to, in the wider world or semantic web it is not.

As the biomedical domain is highly interconnected, bio-ontologies may overlap with each other. For instance, the Ontology of Biomedical Investigation (OBI) requires the availability of definitions for those chemicals used in any investigation. These definitions do not need to be developed within the OBI ontology as there is already a biomedical ontology for the domain of chemicals, called ChEBI. Similarly, software, such as Array Express, making use of an ontology may require more than a single domain ontology. Typically, in these types of scenarios, it is necessary to integrate multiple ontologies into a single coherent narrative. In order to integrate or re-use specific domain ontologies following this “building-block” approach there has to be a high level structure or common “scaffold” where different parts of different domain ontologies may be “plugged” into. To ensure ease of interoperation, or re-use of a domain ontology, well designed and documented ontologies, are essential, and upper ontologies are fundamental in this integrative effort.

Upper level ontologies provide a domain independent conceptual model that aims to be highly re-usable across specific domain applications. One of the primary purposes of upper ontologies is to aid semantic integration across ontologies and to encourage a set of design principles within those ontologies that use them.  Upper ontologies usually describe very general level or abstract concepts. Most of the upper ontologies provide a general classification criterion that makes it easy to re-use, extend and maintain those existing ontologies required by a particular application.  Therefore, it is essential, to aid interoperability and re-use, that ontology development methodologies should provide general guidelines for the use of upper level ontologies. These guidelines should cover the documentation of

1. the design decisions and the justification for choosing one upper-ontology over another,
2. examples that illustrate how they used, in the conceptualisation of a particular domain.

Examples of upper level ontologies include: the Basic Formal Ontology (BFO), DOLCE and GFO. Depending on an upper ontologies representation, or “world view” the upper ontology will provide a framework on how to model, physical objects, processes and information and provide contraints on how these classes are related to each other.

Frank Gibson and James Malone^§

^§European Bioinformatics Institute, Cambridge, CB5 8LW, UK

Community driven ontology development is the process of collaboratively building an ontology which represent the understanding of a particular community or domain area. Within the biological domain, collaboration and community involvement is common place. As an ontology can be interpreted as a “shared understanding” of a particular domain, collaboration and community involvement should be maximised within the life-cycle of an ontology.

Background
Integrating biological knowledge within an ontological framework produces what is referred to as a bio-ontology, where a shared understanding of biology is represented in a computationally amenable form.  The study of the biological sciences is a global effort of researchers and institutions each specialising in, or across particular niches to further our understanding of biology. As the experts in a particular biological field are rarely physically co-located Bio-ontology development as a result is highly distributed, forming what can be thought of as virtual organisations in which experts with different but complementary skills collaborate in building an ontology for a specific purpose.

Typically bio-ontology development is dynamic where different domain experts join and leave the network at any time and decide on the scope of their contribution to the joint effort. In addition, biological ontologies continue to evolve, even after the initial development drive. The continued evolution reflects the advancement of scientific knowledge discovery. New classes, properties, and instances may be added at any time, and new uses or extended scope for the ontology may be identified .

The diversity of the life-science domain results in a multitude of application domains for ontology development and therefore produces numerous ontologies for biology. However, with the diversity, there is equal homology. Typically, the same experimental equipment and reagents can be used to study different aspects of biology. For example, a mass spectrometer can be used to determine the elemental composition of a molecule in a chemistry based experiment, and to determine the chemical structure of peptides in a proteomics investigation.  The multiple application of equipment, reagents and organisms in the study of biology and the potential to be represented multiple times across different bio-ontologies, with slightly different defintions. This potential proliferation or duplication of terms, could undermine the ethos of ontology development – to produce a shared understanding.

Examples

OBI
The Ontology of Biomedical Investigations (OBI) aims to produce an ontology which represents the common components of life-science experimentation, such as equipment, materials and protocols. The developer community of OBI is currently affiliated with 18 diverse biomedical communities, ranging from functional genomics to crop science to neuroscience. In addition to having a diverse community of expertise, the OBI developers work in a virtual organisation encompassing multiple countries and time zones.

The OBO Foundry
In an attempt to address the issue of bio-ontology proliferation and potential overlap the The Open Biomedical Ontology (OBO) Foundry was formed (Smith et al, 2007). The OBO Foundry describes itself as “a collaborative experiment involving developers of science-based ontologies who are establishing a set of principles for ontology development with the goal of creating a suite of orthogonal interoperable reference ontologies in the biomedical domain.”  The role of the OBO Foundry is twofold. In one role The OBO Foundry acts as a registry to collect public domain ontologies that, by design and revision, are developed by and available to the biomedical community, fostering information sharing and data interpretation. This section is called the OBO library. (As of 23rd of January 2010 there are 111 candidate ontologies at the OBO Foundry, representing knowledge domains ranging from Amphibian gross anatomy, infectious diseases to scientific experimentation. In addition to providing a library of bio-ontologies, the OBO Foundry was formed to reduce ontology overlap and ensure bio-ontology orthogonality. Initial steps at achieving this aim have produced a set of design {http://www.obofoundry.org/crit.shtml} to which domain ontologies should adhere, such as, openness, a shared syntax and class definitions. The OBO Foundry proposes a review process by which ontologies may become ‘certified’ as meeting the OBO Foundry criteria (e.g. orthognality, shared namespace. At present no OBO Foundry ontology has been awarded this certified status, although several have been proposed as “Candidate ontologies” which are ready to be reviewed. Criticims of the OBO Foundry centre around the lack of suggested community orientated engineering methodology , method or technique by which these principles can be met (see Hull and Gibson).

Authors: Frank Gibson and James Malone

Acknowledgements

This paper is an open access work distributed under the terms of the Creative Commons Attribution License 2.5 (http://creativecommons.org/licenses/by/2.5/), which permits unrestricted use, distribution, and reproduction in any medium, provided that the original author and source are attributed.

The paper and its publication environment form part of the work of the Ontogenesis Network, supported by EPSRC grant EP/E021352/1.

Review of  Semantic Integration for the Life Sciences

This article presents an overview of the concept of Semantic Integration how it can be performed and what life-science projects have used this approach

1. As this is a short article on “What is Semantic Integration” I think the Abstract and motivation headings are probably unnecessary. I would suggest starting the article at the first line of the current motivation section and either removing this heading or think of a slightly different one.
2. I would then move the Ontologies can be generic… paragraph further down the page to – and merge with – the Ontologies for integration section
3. Semantic Integration section
   What I would like to see here after this title is a definition of what semantic integration actually is making reference to semantic and syntactic heterogeneity
4. The RDF Database section does not really seem to fit with the flow of the article. I would suggest creating another heading for the final paragraph starting “Previous work on” called something like, Current Semantic Integration Efforts/Projects and merge the RDF database section in with this.

Who’s here? The following is an alphabetical list of people currently attending the Ontogenesis Blogging a Book Experiment.

1. Sean Bechhofer, University of Manchester
2. Michel Dumontier, University of Carleton
3. Mikel Egana-Aranguren
4. Frank Gibson
5. Matthew Horridge, University of Manchester
6. Duncan Hull, EBI
7. Simon Jupp, University of Manchester
8. Allyson Lister, Newcastle University
9. Phillip Lord, Newcastle University
10. James Malone, EBI
11. David Osumi-Sutherland, University of Cambridge
12. Helen Parkinson, EBI
13. Robert Stevens, University of Manchester
14. Christopher Brewster, Aston Business School
15. Alan Rector, University of Manchester
16. Ulrike Sattler, University of Manchester
17. David Shotton, University of Oxford