A note on the Computer Cooking Contest
Last summer I wrote about a computational analysis of culinary evolution where the mutations of the ingredients of recipes was investigated and modelled. We can speed up the evolution by stirring in some more AI to help you find and adapt recipes based on the ingredients you happen to have. To get to the point, and in the words of the contest organizers, David Aha and Amélie Cordier:
Once upon a time, we wondered whether some software system could help us to make a yummy meal from the contents of our fridge. Given a restricted set of ingredients, the task is to cook something “that tastes good”. More recently, we wondered whether a system could help us to explain our research interests to a broader audience. Given the technological state-of-the-art, the task is to create a problem-solving system. Glue the two together and you get: The Computer Cooking Contest!
There is no restriction on the technology, but the contestants have to start with the basic recipe database, which is available for download from the Computer Cooking Contest website, and it seems that it has to have a web interface. There are four categories in the contest, each with a prize. The “main” one concerns recipe selection and possible modification, the “adaptation” one has to solve specific adaptations of a recipe, the “open” one is, well, open, and scientific originality is the only criterion, and there is a “student” challenge slimmed to solving a chosen subtask. Examples and suggestions are given in the rules & categories page of the CFC. Deadline is 14 April.
Last year’s winner was CookIIS, which is a “recipe creator” using case-based reasoning: you can fill in the ingredients you happen to have, ones that should be excluded, and an additional constraint (such as vegetarian or low-cholesterol), and out comes a list of recipes satisfying the constraints. My “potato, cabbage, beans, cheese”, excluding “olive, banana” and vegetarian had as best suggestion Banda Kopir Tarkari, Chili bean dip, and Dilly Potato Salad (and another 545). After that, I could not play with the recipes; there is an “Adaptation” note at the end of the recipe to change ingredients, but it would be nice if, for instance, I just could click an ingredient (that I did not have and had not specified I did not have…) and see what I could swap it for, or maybe hook up my cupboard and fridge to the computer so the software knows which ingredients I have to begin with.
Either way, CookIIS definitely beats the wine and pizza ontologies in presentability… but then, maybe some of the Semantic Web technologies are just as suitable to excel in any of the four computer cooking challenges.
ICT, Africa, peace, and gender
Just in case you thought that the terms in the title are rather eclectic, or even mutually exclusive, then you are wrong. ICT4Peace is a well-known combination, likewise for other organisations and events, such as the ICT for peace symposium in the Netherlands that I wrote about earlier. ICT & development activities, e.g., by Informatici Senza Frontiere, and ICT & Africa (or here or here, among many sites) is also well-known. There is even more material for ICT & gender. But what, then, about the combination of them?
Shastry Njeru sees links between them and many possibilities to put ICT to good use in Africa to enhance peaceful societies and post-conflict reconstruction where women play a pivotal role [1]. Not that much has been realized yet; so, if you are ever short on research or implementation topics, then Njeru’s paper undoubtedly will provide you with more topics than you can handle.
So, what, then, can ICT be used for in peacebuilding, in Africa, by women? One topic that features prominently in Njeru’s paper is communication among women to share experiences, exchange information, build communities, keep in contact, have “discussion in virtual spaces, even when physical, real world meetings are impossible on account of geographical distance or political sensitivities” and so forth, using skype, blogs and other Web 2.0 tools such as Flickr, podcasts, etc., Internet access in their own language, and voice and video to text hardware and software to record the oral histories. A more general suggestion, i.e., not necessarily related to only women or only Africa is that “ICT for peacebuilding should form the repository for documents, press releases and other information related to the peace process”.
Some examples of what has been achieved already are: the use of mobile phone networks in Zambia to advocate women’s rights, Internet access for women entrepreneurs in textile industries in Douala in Cameroon, and ICT and mobile phone businesses are used as instruments of change by rural women in various ways in Uganda [1], including the Ugandan CD-ROM project [2].
Njeru thinks that everything can be done already with existing technologies that have to be used more creatively and such that there are policies, programmes, and funds that can overcome the social, political, and economic hurdles to realise the gendered ICT for peace in Africa. Hardware, maybe yes, but surely not software.
Regarding the hardware, mobile phone usage is growing fast (some reasons why) and Samsung, Sharp and Sanyo have jumped on board already with the solar panel-fuelled mobile phones to solve the problem of (lack of reliable) energy supply. The EeePc and the one laptop per child projects and the likes are nothing new either, nor are the palm pilots that are used for OpenMRS’s electronic health records in rural areas in, among others, Kenya. But this is not my area of expertise, so I will leave it to the hardware developers for the final [yes/no] on the question if extant hardware suffices.
Regarding software, developing a repository for the documents, press releases etc. is doable with current software as well, but a usable repository requires insight into how then the interfaces have to be designed so that it suits best for the intended users and how the data should be searched; thus, overall, it may not be simply a case of deployment of software, but also involve development of new applications. Internet access, including those Web 2.0 applications, in one’s own language requires localization of the software and a good strategy on how one can coordinate and maintain such software. This is very well doable, but it is not already lying on the shelf waiting to be deployed.
More challenging will be figuring out the best way to manage all the multimedia of photos, video reports, logged skype meetings and so forth. If one does not annotate them, then they are bound to end up in a ‘write-only’ data silo. However, those reports should not be (nor have been) made to merely save them, but one also should be able to find, retrieve, and use the information contained in them. A quick-and-dirty tagging system or somewhat more sophisticated wisdom-of-the-crowds tagging methods might work in the short term, but it will not in the long run, and thereby still letting those inadequately annotated multimedia pieces getting dust. An obvious direction for a solution is to create the annotation mechanism and develop an ontology about conflict & peacebuilding, develop a software system to put the two together, develop applications to access the properly annotated material, and train the annotators. This easily can take up the time and resources of an EU FP7 Integrated Project.
Undoubtedly, observation of current practices, their limitations, and subsequent requirements analysis will bring afore more creative opportunities of usage of ICT in a peacebuilding setting targeting women as the, mostly untapped, prime user base. A quick search on ICT jobs in Africa or peacebuilding (on the UN system and its affiliated organizations, and the NGO industry) to see if the existing structures invest in this area did not show anything other than jobs at their respective headquarters such as website development, network administration, or ICT group team leader. Maybe upper management does not realise the potential, or it is seen merely as an afterthought? Or maybe more grassroots initiatives have to be set up, be successful, and then organisations will come on board and devote resources to it? Or perhaps companies and venture capital should be more daring and give it a try—mobile phone companies already make a profit and some ‘philanthropy’ does well for a company’s image anyway—and there is always the option to take away some money from the military-industrial complex.
Whose responsibility would it be (if any) to take the lead (if necessary) in such endeavours? Either way, given that investment in green technologies can be positioned as a way out of the recession, then so can it be for ICT for peace(building) aimed at women, be they in Africa or other continents where people suffer from conflicts or are in the process of reconciliation and peacebuilding. One just has to divert the focus of ICT for destruction, fear-moderation, and the likes to one of ICT for constructive engagement, aiming at inclusive technologies and those applications that facilitate development of societies and empower people.
References
[1] Shastry Njeru. (2009). Information and Communication Technology (ICT), Gender, and Peacebuilding in Africa: A Case of Missed Connections. Peace & Conflict Review, 3(2), 32-40.
[2] Huyer S and Sikoska T. (2003). Overcoming the Gender Digital Divide: Understanding the ICTs and their potential for the Empowerment of Women. United Nations International Research and Training Institute for the Advancement of Women (UN-INSTRAW), Instraw Research Paper Series No. 1., 36p.
Easy widget for keeping track of visited countries
Following up on my whining last month about not being able to find a suitable and easy Web 2.0 widget to record the countries I have visited, I’ve stumbled upon one the other day that comes reasonably close!
Douwe Osinga, who works at Google, made an interactive applet for selecting the countries visited (for the USA and India also the states), and the generated code can then be copied into your home page, blog, and facebook. Updating the generated figure can be done by pasting the previously generated html back into the appropriate box, clicking on the new country/ies, and then pasting that code back into your home page, blog, or facebook. And no login hurdles etc have to be overcome.
Thus, it is not entirely interactive and cross-linked and all that, but it will do fine—and most certainly better than the lame Paint-job I did last month. So, here goes the updated picture (not including the holiday that I would like to take now), where at the bottom you will find the standard link to create your own map:
visited 33 states (14.6%)
Create your own visited map of The World
72010 SemWebTech lecture 12: Social aspects and recap part 2 of the course
You might ask yourself why we should even bother with social aspects in a technologies course. Out there in the field, however, SWT are applied by people with different backgrounds and specialties and they are relatively new technologies that act out in an inter/multi/transdisciplinary environment, which brings with it some learning curves. If you end up working in this area, then it is wise to have some notion about human dynamics in addition to the theoretical and technological details, and how the two are intertwined. Some of the hurdles that may seem ‘merely’ dynamics of human interaction can very well turn out to be scratching the surface of problems that might be solved with extensions or modifications to the technologies or even motivate new theoretical research.
Good and Wilkinson’s paper provides a non-technical introduction to Semantic Web topics, such as LSID, RDF, ontologies, and services. They consider what problems these technologies solve (i.e., the sensible reasons to adopt them), and what the hurdles are both with respect to the extant tools & technologies and the (humans working for some of the) leading biological data providers that appear to be reluctant in taking up the technologies. There are obviously people who have taken the approach of “let’s try and see what come out of the experimentation”, whereas others are more reserved and take the approach of “let’s see what happens, and then maybe we’ll try”. If there are not enough people of the former type, then the latter ones obviously will never try.
Another dimension of the social aspects is described in [2], which is a write-up of Goble’s presentation about the montagues and capulets at the SOFG’04 meeting. It argues that there are, mostly, three different types of people within the SWLS arena (it may just as well be applicable to another subject domain if they were to experiment with SWT, e.g., in public administration): the AI researchers, the philosophers, and the IT-savvy domain experts. They each have their own motivations and goals, which, at times, clash, but with conversation, respect, understanding, compromise, and collaboration, one will, and can, achieve the realisation of theory and ideas in useful applications.
The second part of the lecture will be devoted to a recap of the material of the past 11 lectures (there recap of the first part of the SWT course will be on 19-1).
References
[1] Good BM and Wilkinson MD. The Life Science Semantic Web is Full of Creeps! Briefings in Bioinformatics, 2006 7(3):275-286.
[2] Carole Goble and Chris Wroe. The Montagues and the Capulets. Comparative and Functional Genomics, 5(8):623-632, 2004. doi:10.1002/cfg.442
Note: reference 1 is mandatory reading, 2 is optional.
Lecture notes: none
72010 SemWebTech lecture 11: BioRDF and Workflows
After considering the background of the combination of ontologies, the Semantic Web, and ‘something bio’ and some challenges and successes in the previous three lectures, we shall take a look at more technologies that are applied in the life sciences and that use SWT to a greater or lesser extent. In particular, RDF and scientific workflows will pass the revue. The former has the flavour of “let’s experiment with the new technologies”, whereas the latter is more alike “where can we add SWT to the system and make things easier?”.
BioRDF
The problems of data integration were not always solved in a satisfactory manner with the ‘old’ technologies, but perhaps SWT can solve them; or so goes the idea. The past three years has seen several experiments to test if the SWT can live up to that challenge. To see where things are heading, let us recollect the data integration strategies that passed the revue in lecture 8, which can be chosen with the extant technologies as well as the newer ones of the Semantic Web: (i) Physical schema mappings with Global As View (GAV), Local As View (LAV), or GLAV, (ii) Conceptual model-based data integration, (iii) Data federation, (iv) Data warehouses, (v) Data marts, (vi) Services-mediated integration, (vii) Peer-to-peer data integration, and (viii) Ontology-based data integration, being i or ii (possibly in conjunction with the others) through an ontology or linked data by means of an ontology.
Early experiments focused on RDF-izing ‘legacy’ data, such as RDBMSs, excel sheets, HTML pages etc., and making one large triplestore out of it, i.e., an RDF-warehouse [1,2], using tools such as D2RQ and Sesame (renamed to Open RDF) as triple store (other triple stores are, e.g., Virtuoso and AllegroGraph, used by [3]). The Bio2RDF experiment took over 20 freely available data sources and converted them with multiple JSP programs into a total of about 163 million triples in a Sesame triplestore, added a myBio2RDF personalization step, and used extant applications to present the data to the users. The warehousing strategy, however, has some well-known drawbacks even in a non-Semantic Web setting. So, following the earlier gradual development of data integration strategies, the time had come to experiment with data federation, RDF-style [3], where the authors note at the end that perhaps the next step—services—may yield interesting results as well. You also may want to have a look at the winners’ solutions to the yearly Billion triple challenge and other Semantic Web challenges (all submissions, each with a paper describing the system and a demo, are filed under the ‘former challenges’ menu).
One of the problems that SWT and its W3C standards aimed to solve was uniform data representation, which can be done well with RDF. Another was locating an entity and identifying it, which can be done with URIs. An emerging problem now is that for a single entity in reality, there are many “semantically equivalent” URIs [1,3]; e.g., Hexokinase had three different URIs, one in the GO, in UniProt, and in the BioPathways (and to harmonise them, Bio2RDF added their own one and linked to the others using owl:sameAs). More generally than only the URI issue, is the observation made by the HCLS IG’s Linking Open Drug Data group, and was a well-know hurdle in earlier non-SWT data integration efforts: “A significant challenge … is the strong prevalence of terminology conflicts, synonyms, and homonyms. These problems are not addressed by simply making data sets available on the Web using RDF as common syntax but require deeper semantic integration.” and “For … applications that rely on expressive querying or automated reasoning deeper integration is essential” [4]. In parallel with request for “more community practices on publishing term and schema mappings” [4], the experimentation with RDF-oriented data integration continues.
Scientific Workflows
You may have come across Business Process Modelling and workflows in government and industry; scientific workflows are an extension to that (see its background and motivation). In addition to general requirements, such as service composition and reuse of workflow design, scalability, and data provenance, in practice, it turns out that such a scientific workflow system must have the ability to handle multiple databases and a range of analysis tools with corresponding interfaces to a diverse range of computational environments, deal with explicit representation of knowledge at different stages, customization of the interface for each researcher, and auditability and repeatability of the workflow.
To cut a long story short (in the writing here, not in the lecture on 11-1): where can we plug SWT into scientific workflows? One can, for instance, use RDF as common data format for linking and integration and SPARQL for querying that data, OWL ontologies for the representation of the knowledge across the workflow (at least the domain knowledge and the workflow knowledge), rules to orchestrate the service execution, and services (e.g., WSDL, OWL-S) to discover useful scripts that can perform a task in the workflow.
This still leaves to choose what to do with the provenance, which may be considered to be a component of the broader notion of trust. Recollecting the Semantic Web layer cake from lecture 1, trust is above the SPARQL, OWL, and RIF pieces. Currently, there is no W3C standard for the trust layer, yet users need it. Scientific workflow systems, such as Kepler and Taverna, invented their own way of managing it. For instance, Taverna uses experiment-, workflow-, and knowledge-provenance models represented using RDF(S) & OWL, and RDF for the individual provenance graphs of a particular workflow [5,6]. The area of scientific workflows, provenance, and trust is lively with workshops and, e.g., the provenance challenges; at the time of writing this post, it may be still too early to identify an established solution (to, say, have interoperability across workflow systems and its components to weave a web of provenance), be it a SWT one or another.
Probably, there will not be enough time during the lecture to also cover Semantic Web Services. In case you are curious how one can efficiently search for the thousands of web services and their use in working systems (i.e., application-oriented papers, not the theory behind it), you may want to have a look at [7, 8] (the latter is lighter on the bio-component than the former). The W3C activities on web services have standards, working groups, and an interest group.
References
[1] Belleau F, Nolin MA, Tourigny N, Rigault P, Morissette J. Bio2RDF: Towards A Mashup To Build Bioinformatics Knowledge System. Journal of Biomedical Informatics, 2008, 41(5):706-16. online interface: bio2RDF
[2] Ruttenberg A, Clark T, Bug W, Samwald M, Bodenreider O, Chen H, Doherty D, Forsberg K, Gao Y, Kashyap V, Kinoshita J, Luciano J, Scott Marshall M, Ogbuji C, Rees J, Stephens S, Wong GT, Elizabeth Wu, Zaccagnini D, Hongsermeier T, Neumann E, Herman I, Cheung KH. Advancing translational research with the Semantic Web, BMC Bioinformatics, 8, 2007.
[3] Kei-Hoi Cheung, H Robert Frost, M Scott Marshall, Eric Prud’hommeaux, Matthias Samwald, Jun Zhao, and Adrian Paschke. A journey to Semantic Web query federation in the life sciences. BMC Bioinformatics 2009, 10(Suppl 10):S10
[4] Anja Jentzsch, Bo Andersson, Oktie Hassanzadeh, Susie Stephens, Christian Bizer. Enabling Tailored Therapeutics with Linked Data. LDOW2009, April 20, 2009, Madrid, Spain.
[5] Tom Oinn, Matthew Addis, Justin Ferris, Darren Marvin, Martin Senger, Mark Greenwood, Tim Carver, Kevin Glover, Matthew R. Pocock, Anil Wipat and Peter Li. (2004). Taverna: a tool for the composition and enactment of bioinformatics workflows. Bioinformatics 20 (17): 3045-3055. The Taverna website
[6] Carole Goble et al. Knowledge Discovery for biology with Taverna. In: Semantic Web: Revolutionizing knowledge discovery in the life sciences. 2007, pp355-395.
[7] Michael DiBernardo, Rachel Pottinger, and Mark Wilkinson. (2008). Semi-automatic web service composition for the life sciences using the BioMoby semantic web framework. Journal of Biomedical Informatics, 41(5): 837-847.
[8] Sahoo., S.S., Shet, A. Hunter, B., and York, W.S. SEMbrowser–semantic biological web services registry. In: Semantic Web: revolutionizing knowledge discovery in the life sciences, Baker, C.J.O., Cheung, H. (eds), Springer: New York, 2007, pp 317-340.
Note: references 1 and (5 or 6) are mandatory reading, (2 or 3) was mandatory for an earlier lecture, and 4, 7, and 8 are optional.
Lecture notes: lecture 11 – BioRDF and scientific workflows
Thanks and best wishes for 2010
I would like to say many thanks to all of you dear readers for visiting my blog and especially those known and previously unknown visitors who took the effort to leave comments and comments on comments (as that’s the particular feature of blogs anyway)! I hope you have found it was time well spent.
Although it is that time of the year again for some reflection (though I do that during the year as well, but somehow one mentions this only around the new year), my 2009 had its ups and downs and more of the latter than the former, so I will not dwell on that here. Maybe I can reap the fruits of the sown seeds in the upcoming year (yeah, I know, I said that last year as well; patience is a virtue, right?).
As for the blog, the amount of posts increased considerably compared to previous years with the topics just as varied, which I shall try to keep up with in 2010.
I wish you all a happy, productive, and prosperous New Year!
72010 SemWebTech lecture 10: SWLS and text processing and ontologies
There is a lot to be said about how Ontology, ontologies, and natural language interact from a philosophical perspective up to the point that different commitments lead to different features and, moreover, limitations of a (Semantic Web) application. In this lecture on 22 Dec, however, we shall focus on the interaction of NLP and ontologies within a bio-domain from an engineering perspective.
During the bottom-up ontology development and methodologies lectures, it was already mentioned that natural language processing (NLP) can be useful for ontology development. In addition, NLP can be used as a component in an ontology-driven information system and an NLP application can be enhanced with an ontology. Which approaches and tools suit best depends on the goal (and background) of its developers and prospective users, ontological commitment, and available resources.
Summarising the possibilities for “something natural language text” and ontologies or ontology-like artifacts, we can:
- Use ontologies to improve NLP: to enhance precision and recall of queries (including enhancing dialogue systems [1]), to sort results of an information retrieval query to the digital library (e.g. GoPubMed [2]), or to navigate literature (which amounts to linked data [3]).
- Use NLP to develop ontologies (TBox): mainly to search for candidate terms and relations, which is part of the suite of techniques called ‘ontology learning’ [4].
- Use NLP to populate ontologies (ABox): e.g., document retrieval enhanced by lexicalised ontologies and biomedical text mining [5].
- Use it for natural language generation (NLG) from a formal language: this can be done using a template-based approach that works quite well for English but much less so for grammatically more structured languages such as Italian [6], or with a full-fledged grammar engine as with the Attempto Controlled English and bi-directional mappings (see for a discussion [7]).
Intuitively, one may be led to think that simply taking the generic NLP or NLG tools will do fine also for the bio(medical) domain. Any application does indeed use those techniques and tools—Paul Buitelaar’s slides have examples and many references to NLP tools—but, generally, they do not suffice to obtain ‘acceptable’ results. Domain specific peculiarities are many and wide-ranging. For instance, to deal with the variations of terms (scientific name, variant, common misspellings) and the grounding step (linking a term to an entity in a biological database) in the ontology-NLP preparation and instance classification side [5], to characterize the question in a question answering system correctly [1], and to find ways to deal with the rather long strings that denote a biological entity or concept or universal [4]. Some of such peculiarities actually generate better overall results than in generic or other domain-specific usages of NLP tools, but it requires extra manual preparatory work and a basic understanding of the subject domain and its applications.
References
[1] K. Vila, A. Ferrández. Developing an Ontology for Improving Question Answering in the Agricultural Domain. In: Proceedings of MTSR’09. Springer CCIS 46, 245-256.
[2] Heiko Dietze, Dimitra Alexopoulou, Michael R. Alvers, Liliana Barrio-Alvers, Bill Andreopoulos, Andreas Doms, Joerg Hakenberg, Jan Moennich, Conrad Plake, Andreas Reischuck, Loic Royer, Thomas Waechter, Matthias Zschunke, and Michael Schroeder. GoPubMed: Exploring PubMed with Ontological Background Knowledge. In Stephen A. Krawetz, editor, Bioinformatics for Systems Biology. Humana Press, 2008.
[3] Allen H. Renear and Carole L. Palmer. Strategic Reading, Ontologies, and the Future of Scientific Publishing. Science 325 (5942), 828. [DOI: 10.1126/science.1157784] (but see also some comments on the paper)
[4] Dimitra Alexopoulou, Thomas Waechter, Laura Pickersgill, Cecilia Eyre, and Michael Schroeder. Terminologies for text-mining: an experiment in the lipoprotein metabolism domain. BMC Bioinformatics, 9(Suppl4):S2, 2008
[5] Witte, R. Kappler, T. And Baker, C.J.O. Ontology design for biomedical text mining. In: Semantic Web: revolutionizing knowledge discovery in the life sciences, Baker, C.J.O., Cheung, H. (eds), Springer: New York, 2007, pp 281-313.
[6] M. Jarrar, C.M. Keet, and P. Dongilli. Multilingual verbalization of ORM conceptual models and axiomatized ontologies. STARLab Technical Report, Vrije Universiteit Brussels, Belgium. February 2006.
[7] R. Schwitter, K. Kaljurand, A. Cregan, C. Dolbear, G. Hart. A comparison of three controlled natural languages for OWL 1.1. Proc. of OWLED 2008 DC.
Note: references 4 and 5 are mandatory reading, and 1-3 and 6 are optional (recommended for the EMLCT students).
Lecture notes: lecture 10 – Text processing
72010 SemWebTech lecture 9: Successes and challenges for ontologies in the life sciences
To be able to talk about successes and challenges of SWT for health care and life sciences (or any other subject domain), we first need to establish when something can be deemed a success, when it is a challenge, and when it is an outright failure. Such measures can be devised in an absolute sense (compare technology x with an SWT one: does it outperform on measure y?) and relative (to whom is technology x deemed successful?) Given these considerations, we shall take a closer look at several attempts, being two successes and a few challenges in representation and reasoning. What were the problems and how did they solve it, and what are the problems and can that be resolved, respectively?
As success stories we take the experiments by Wolstencroft and coauthors about classifying protein phosphatases [1] and Calvanese et al for graphical, web-based, ontology-based data access applied to horizontal gene transfer data [2]. They each focus on different ontology languages and reasoning services to solve different problems. What they have in common is that there is an interaction between the ontology and instances (and that it was a considerable amount of work by people with different specialties): the former focuses on classifying instances and the latter on querying instances. In addition, modest results of biological significance have been obtained with the classification of the protein phosphatases, whereas with the ontology-based data analysis we are tantalizingly close.
The challenges for SWT in general and for HCLS in particular are quite diverse, of which some concern the SWT proper and others are by its designers—and W3C core activities on standardization—considered outside their responsibility but still need to be done. Currently, for the software aspects, the onus is put on the software developers and industry to pick up on the proof-of-concept and working-prototype tools that have come out of academia and to transform them into the industry-grade quality that a widespread adoption of SWT requires. Although this aspect should not be ignored, we shall focus on the language and reasoning limitations during the lecture.
In addition to the language and corresponding reasoning limitations that passed the revue in the lectures on OWL, there are language “limitations” discussed and illustrated at length in various papers, with the most recent take [3], where it might well be that the extensions presented in lecture 6 and 7 (parts, time, uncertainty, and vagueness) can ameliorate or perhaps even solve the problem. Some of the issues outlined by Schultz and coauthors are ‘mere’ modelling pitfalls, whereas others are real challenges that can be approximated to a greater or lesser extent. We shall look at several representation issues that go beyond the earlier examples of SNOMED CT’s “brain concussion without loss of consciousness”; e.g. how would you represent in an ontology that in most but not all cases hepatitis has as symptom fever, or how would you formalize the defined concept “Drug abuse prevention”, and (provided you are convinced it should be represented in an ontology) that the world-wide prevalence of diabetes mellitus is 2.8%?
Concerning challenges for automated reasoning, we shall look at two of the nine identified required reasoning scenarios [4], being the “model checking (violation)” and “finding gaps in an ontology and discovering new relations”, thereby reiterating that it is the life scientists’ high-level goal-driven approach and desire to use OWL ontologies with reasoning services to, ultimately, discover novel information about nature. You might find it of interest to read about the feedback received from the SWT developers upon presenting [4] here: some requirements are met in the meantime and new useful reasoning services were presented.
References
[1] Wolstencroft, K., Stevens, R., Haarslev, V. Applying OWL reasoning to genomic data. In: Semantic Web: revolutionizing knowledge discovery in the life sciences, Baker, C.J.O., Cheung, H. (eds), Springer: New York, 2007, 225-248.
[2] Calvanese, D., Keet, C.M., Nutt, W., Rodriguez-Muro, M., Stefanoni, G. Web-based Graphical Querying of Databases through an Ontology: the WONDER System. ACM Symposium on Applied Computing (ACM SAC’10), March 22-26 2010, Sierre, Switzerland.
[3] Stefan Schulz, Holger Stenzhorn, Martin Boekers and Barry Smith. Strengths and Limitations of Formal Ontologies in the Biomedical Domain. Electronic Journal of Communication, Information and Innovation in Health (Special Issue on Ontologies, Semantic Web and Health), 2009.
[4] Keet, C.M., Roos, M. and Marshall, M.S. A survey of requirements for automated reasoning services for bio-ontologies in OWL. Third international Workshop OWL: Experiences and Directions (OWLED 2007), 6-7 June 2007, Innsbruck, Austria. CEUR-WS Vol-258.
[5] Ruttenberg A, Clark T, Bug W, Samwald M, Bodenreider O, Chen H, Doherty D, Forsberg K, Gao Y, Kashyap V, Kinoshita J, Luciano J, Scott Marshall M, Ogbuji C, Rees J, Stephens S, Wong GT, Elizabeth Wu, Zaccagnini D, Hongsermeier T, Neumann E, Herman I, Cheung KH. Advancing translational research with the Semantic Web, BMC Bioinformatics, 8, 2007.
p.s.: the first part of the lecture on 21-12 will be devoted to the remaining part of last week’s lecture; that is, a few discussion questions about [5] that are mentioned in the slides of the previous lecture.
Note: references 1 and 3 are mandatory reading, 2 and 4 recommended to read, and 5 was mandatory for the previous lecture.
Lecture notes: lecture 9 – Successes and challenges for ontologies
72010 SemWebTech lecture 8: SWT for HCLS background and data integration
After the ontology languages and general aspects of ontology engineering, we now will delve into one specific application area: SWT for health care and life sciences. Its frontrunners in bioinformatics were adopters of some of the Semantic Web ideas even before Berners-Lee, Hendler, and Lassila wrote their Scientific American paper in 2001, even though they did not formulate their needs and intentions in the same terminology: they did want to have shared, controlled vocabularies with the same syntax, to facilitate data integration—or at least interoperability—across Web-accessible databases, have a common space for identifiers, it needing to be a dynamic, changing system, to organize and query incomplete biological knowledge, and, albeit not stated explicitly, it all still needed to be highly scalable [1].
Bioinformaticians and domain experts in genomics already organized themselves together in the Gene Ontology Consortium, which was set up officially in 1998 to realize a solution for these requirements. The results exceeded anyone’s expectations in its success for a range of reasons. Many tools for the Gene Ontology (GO) and its common KR format, .obo, have been developed, and other research groups adopted the approach to develop controlled vocabularies either by extending the GO, e.g., rice traits, or adding their own subject domain, such as zebrafish anatomy and mouse developmental stages. This proliferation, as well as the OWL development and standardization process that was going on at about the same time, pushed the goal posts further: new expectations were put on the GO and its siblings and on their tools, and the proliferation had become a bit too wieldy to keep a good overview what was going on and how those ontologies would be put together. Put differently, some people noticed the inferencing possibilities that can be obtained from moving from obo to OWL and others thought that some coordination among all those obo bio-ontologies would be advantageous given that post-hoc integration of ontologies of related and overlapping subject domains is not easy. Thus came into being the OBO Foundry to solve such issues, proposing a methodology for coordinated evolution of ontologies to support biomedical data integration [2].
People in related disciplines, such as ecology, have taken on board experiences of these very early adopters, and instead decided to jump on board after the OWL standardization. They, however, were not only motivated by data(base) integration. Referring to Madin et al’s paper [3] again, I highlight three points they made: “terminological ambiguity slows scientific progress, leads to redundant research efforts, and ultimately impedes advances towards a unified foundation for ecological science”, i.e., identification of some serious problems they have in ecological research; “Formal ontologies provide a mechanism to address the drawbacks of terminological ambiguity in ecology”, i.e., what they expect that ontologies will solve for them (disambiguation); and “and fill an important gap in the management of ecological data by facilitating powerful data discovery based on rigorously defined, scientifically meaningful terms”, i.e., for what purpose they want to use ontologies and any associated computation (discovery). That is, ontologies not as a—one of many possible—tool in the engineering/infrastructure means, but as a required part of a method in the scientific investigation that aims to discover new information and knowledge about nature (i.e., in answering the who, what, where, when, and how things are the way they are in nature).
What has all this to do with actual Semantic Web technologies? On the one hand, there are multiple data integration approaches and tools that have been, and are being, tried out by the domain experts, bioinformaticians, and interdisciplinary-minded computer scientists [4], and, on the other hand, there are the W3C Semantic Web standards XML, RDF(S), SPARQL, and OWL. Some use these standards to achieve data integration, some do not. Since this is a Semantic Web course, we shall take a look at two efforts who (try to) do, which came forth from the activities of the W3C’s Health Care and Life Sciences Interest Group. More precisely, we take a closer look at a paper written about 3 years ago [5] that reports on a case study to try to get those Semantic Web Technologies to work for them in order to achieve data integration and a range of other things. There is also a more recent paper from the HCLS IG [6], where they aimed at not only linking of data but also querying of distributed data, using a mixture of RDF triple stores and SKOS. Both papers reveal their understanding of the purposes of SWT, and, moreover, what their goals are, their experimentation with various technologies to achieve them, and where there is still some work to do. There are notable achievements described in these, and related, papers, but the sought-after “killer app” is yet to be announced.
The lecture will cover a ‘historical’ overview and what more recent ontology-adopters focus on, the very basics of data integration approaches that motivated the development of ontologies, and we shall analyse some technological issues and challenges mentioned in [5] concerning Semantic Web (or not) technologies.
References:
[1] The Gene Ontology Consortium. Gene ontology: tool for the unification of biology. Nature Genetics, May 2000;25(1):25-9.
[2] Barry Smith, Michael Ashburner, Cornelius Rosse, Jonathan Bard, William Bug, Werner Ceusters, Louis J. Goldberg, Karen Eilbeck, Amelia Ireland, Christopher J Mungall, The OBI Consortium, Neocles Leontis, Philippe Rocca-Serra, Alan Ruttenberg, Susanna-Assunta Sansone, Richard H Scheuermann, Nigam Shah, Patricia L. Whetzel, Suzanna Lewis. The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nature Biotechnology 25, 1251-1255 (2007).
[3] Joshua S. Madin, Shawn Bowers, Mark P. Schildhauer and Matthew B. Jones. (2008). Advancing ecological research with ontologies. Trends in Ecology & Evolution, 23(3): 159-168.
[4] Erhard Rahm. Data Integration in Bioinformatics and Life Sciences. EDBT Summer School, Bolzano, Sep. 2007.
[5] Ruttenberg A, Clark T, Bug W, Samwald M, Bodenreider O, Chen H, Doherty D, Forsberg K, Gao Y, Kashyap V, Kinoshita J, Luciano J, Scott Marshall M, Ogbuji C, Rees J, Stephens S, Wong GT, Elizabeth Wu, Zaccagnini D, Hongsermeier T, Neumann E, Herman I, Cheung KH. Advancing translational research with the Semantic Web, BMC Bioinformatics, 8, 2007.
[6] Kei-Hoi Cheung, H Robert Frost, M Scott Marshall, Eric Prud’hommeaux, Matthias Samwald, Jun Zhao, and Adrian Paschke. A journey to Semantic Web query federation in the life sciences. BMC Bioinformatics 2009, 10(Suppl 10):S10
Note: references 1, 2, and (5 or 6) are mandatory reading, and 3 and 4 are recommended to read.
Lecture notes: lecture 8 – SWLS background and data integration
72010 SemWebTech lecture 7: Dealing with uncertainty and vagueness
The third advanced ontology engineering topic concerns how to cope with uncertainty and vagueness in ontology languages and their reasoners—and what we can gain from all the extra effort.
For instance, consider information retrieval: to which degree is a web site, a page, a text passage, an image, or a video segment relevant to the information need and an acceptable answer to what the user was searching for? In the context of ontology alignment, one would want to know (automatically) to which degree the focal concepts of two or more ontologies represent the same thing, or are sufficiently overlapping. In an electronic health record system, one may want to classify patients based on their symptoms, such as throwing up often, having a high blood pressure, and yellowish eye colour. How can software agents do the negotiation for your holiday travel plans that are specified imprecisely, alike “I am looking for a package holiday of preferably less than 1000 euro, but really no more that 1150 euro, for about 12 days in a warm country”?
The main problem to solve, then, is what and how to incorporate such vague or uncertain knowledge in OWL and its reasoners. To clarify these two terms upfront:
- Uncertainty: statements are true or false, but due to lack of knowledge we can only estimate to which probability / possibility / necessity degree they are true or false;
- Vagueness: statements involve concepts for which there is no exact definition (such as tall, small, close, far, cheap, expensive), which are then true to some degree, taken from a truth space.
The two principal approaches regarding uncertainty and the semantic web are probabilistic and possibilistic languages, ontologies, and reasoning services, where the former way of dealing with uncertainty receives a lot more attention than the latter. The two principal approaches regarding vagueness and the semantic web are fuzzy and rough extensions, where fuzzy receives more attention compared to the rough approach. The lecture will cover all four approaches to a greater (probabilistic, fuzzy) and lesser (possibilistic, rough) extent.
None of the extant languages and automated reasoners that can cope with vague or uncertain knowledge have made it into ‘mainstream’ Semantic Web tools yet. There was a W3C incubator group on uncertainty, but it remained at that. This has not stopped research in this area; on the contrary. There are two principle strands in these endeavours: one with respect to extending DL languages and its reasoners, such as Pronto that combines the pellet reasoner with a probabilistic extension and FuzzyDL that is a reasoner for fuzzy SHIF(D), and another strand to uses different techniques underneath OWL, such as Bayesian networks and constraint programming-based reasoning for probabilistic ontologies (e.g., PR-OWL), and Mixed Integer Logic Programming for fuzzy ontologies. Within the former approach, one can make a further distinction between extensions of tableaux algorithms and rewritings to a non-uncertain/non-vague standard OWL language so that one of the generic DL reasoners can be used. For each of these branches, there are differences as to which aspects of probabilistic/possibilistic/fuzzy/rough are actually included—just like we saw in the previous lecture about temporal logics.
We shall not cover all such permutations in the lecture, but instead focus on general aspects of the languages and tools. A good introductory overview can be found in [1] (which also has a very long list of references to start delving into the topics [you may skip the DLP section]). Depending on your background education and the degree programme you are studying now, you may find the more technical overview [2] of interest as well. To get an idea of one of the more recent results on rough DL-based ontologies, you might want to glance over [3]. Last, I assume you have a basic knowledge of probability theory and fuzzy sets; if there are many people who do not, I will adjust the lecture somewhat, but you are warmly advised to look it up before the lecture if you do not know about it (even if it is only the respective Wikipedia entry here and here).
References
[1] Umberto Straccia. Managing Uncertainty and Vagueness in Description Logics, Logic Programs and Description Logic Programs. In Reasoning Web, 4th International Summer School, 2008.
[2] Thomas Lukasiewicz and Umberto Straccia. 2008. Managing Uncertainty and Vagueness in Description Logics for the Semantic Web. Journal of Web Semantics, 6:291-308.
[3] Jiang, Y., Wang, J., Tang, S., and Xiao, B. 2009. Reasoning with rough description logics: An approximate concepts approach. Information Sciences, 179:600-612.
Note: references 1 or 2 is mandatory reading, 3 optional.
Lecture notes: lecture 7 – Uncertainty and vagueness
