The WONDER system for ontology browsing and graphical query formulation

Did you ever not want to bother knowing how the data is stored in a database, but simply want to know what kind of things are stored in the database at, say, the conceptual or ontological layer of knowledge? And did you ever not want to bother writing queries in SQL or SPARQL, but have a graphical point-and-click interface with which you can compose a query using that what layer of knowledge and that the system generates automatically the SQL/SPARQL query for you, in the correct syntax? And all that not with a downloaded desktop application but in a Web browser?

Our domain experts in genetics as well as in healthcare informatics, at least, wanted that. We have designed and implemented it now [1], which we have enthusiastically named Web ONtology mediateD Extraction of Relational data (WONDER). Moreover, we have a working system for the use case about the 4GB horizontal gene transfer database [2] and its corresponding ‘application ontology’. (pdf)

Subscribers to this blog might remember I mentioned a that we were working towards this goal, using Ontology-Based Data Access tools to access a database through an ontology and learning from (and elaborating on) its preliminary case studies [3]. In short, we added a usability extension to the OBDA implementations so that not only savvy Semantic Web engineers can use it, but also—actually, moreover—that the domain experts who want to get information from their database(s) can do so. By building upon the OBDA framework [4], we can avail of its solid formal foundations; that is, WONDER is not merely a software application, but there is a logic-based representation behind both the graphics in the ontology browser and the query pane.

In addition, WONDER is scalable because the ontology language (roughly: OWL 2 QL) is ‘simple’. Yes, we had to drop a few things from the original ORM conceptual model, but they have—at least for our case study—no effect on querying the data. The ‘difficult’ constraints are (and generally: should be anyway) implemented in the database, so there will be no instances violating the constraints we had to drop. Trade-offs, indeed, but now one can use an ontology to access a large database over the Web and retrieve the results quickly.

For instance, take the query “For the Firmicutes, retrieve the organisms and their genes that have a GCtotal contents higher than 60”, which is for various reasons not possible through the current web interface of the source database.

Fig.1 shows the ontology pane with three relevant elements selected. (click on the figures to enlarge)

Fig.1. WONDER's ontology pane with three elements selected

Fig.2 shows the constrained adder, where I’m adding that the GCValue has to be > 60.

Fig. 2. WONDER's constraint adder, where I’m adding that the GCValue has to be > 60

Fig.3 shows the query ready for execution: the attributes with a green border are those that will appear in the query answer (I could have selected all, if I wanted to). In the menu bar on the right you can see I have customized the names of the attributes, so that the columns in the results pane will have a query-relevant name in your preferred language (not necessary to do), as well as the automatically generated query.

Fig.3. WONDER's query pane, where the query is ready for execution

Fig.4 shows a section of the results of the first page and Fig.5 of the second page; the “Family” column that has all the Firmicutes (out of about 500 organisms in the database) gives you the whole section of the species tree, because that is how the taxonomy information is stored in the database (refining the database is a separate topic). Alternatively, I could have selected the organism Name from the ontology browser (see Fig.1), de-selected the taxonomic classification in the query pane, and included the Name of the organism in the query answer to have the species name only but not all the taxonomic information; in this case, I wanted to have all that taxonomy information. The genes are the relevant selection (made with the other constraints) out of about the 2 million genes that are stored in the database.

Fig.4. Section of the results, the first page

Fig.5. Section of the results, the second page

There is also a constraint manager for the AND, OR, NOT and nesting. For instance, for the query “Give me the names of the organisms of which the abbreviation starts with a b, but not being a Bacillus, and the prediction and KEGG code of those organisms’ genes that are putatively either horizontally transferred or highly expressed” (Fig.6), we have the constraint manager as shown in Fig.7.

Fig.6. Graphical and textual representation of the second query

Fig.7. Constraint manager for the second query

You can also save and load queries when you’re logged in, and download the results set in any case.

For those who want to play with it: feel free to drop me a line and I will send you the URL. (The reason for not linking the URL here is that the current URL is still for the beta version, whereas the operational one is expected to have a more stable URL soon.)

Last, but not least, the “we” I used in the previous sentences is not some ‘standard writing in plural’, but several people were involved in various ways to realize the WONDER system. In alphabetical order, they are: Diego Calvanese, Marijke Keet, Werner Nutt, Mariano Rodriguez-Muro, and Giorgio Stefanoni, all at FUB. I also want to thank our domain experts of the case study (with whom we’re writing a bio-oriented paper): Santi Garcia-Vallvé (with the Evolutionary Genomics Group, ‘Rovira i Virgilli’ University, Tarragona, Spain) and Mark van Passel (with the Laboratory for Microbiology, Wageningen University and Research Centre, the Netherlands).

References

[1] 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.

[2] Garcia-Vallve, S, Guzman, E., Montero, MA. and Romeu, A. 2003. HGT-DB: a database of putative horizontally transferred genes in prokaryotic complete genomes. Nucleic Acids Research 31: 187-189.

[3] R. Alberts, D. Calvanese, G. De Giacomo, A. Gerber, M. Horridge, A. Kaplunova, C. M. Keet, D. Lembo, M. Lenzerini, M. Milicic, R. Moeller, M. Rodríguez-Muro, R. Rosati, U. Sattler, B. Suntisrivaraporn, G. Stefanoni, A.-Y. Turhan, S. Wandelt, M. Wessel. Analysis of Test Results on Usage Scenarios. Deliverable TONES-D27 v1.0, Oct. 10 2008.

[4] Diego Calvanese, Giuseppe De Giacomo, Domenico Lembo, Maurizio Lenzerini, Antonella Poggi, Mariano Rodriguez-Muro, and Riccardo Rosati. Ontologies and databases: The DL-Lite approach. In Sergio Tessaris and Enrico Franconi, editors, Semantic Technologies for Informations Systems – 5th Int. Reasoning Web Summer School (RW 2009), volume 5689 of Lecture Notes in Computer Science, pages 255-356. Springer, 2009.

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New blogpost index and updated list of top posts

To facilitate browsing and not to let the older posts be entirely hidden in the archive, I have added a separate page with a list of all posts of the past 3.5 years. For those of you curious which of those posts have received most traffic over time, I have updated the vox populi page. I also added a list of my most cited research articles; for the time being, the topics covered by both the top posts and most cited papers have an empty intersection. Ok, stretching it a bit, then post 2 and paper numbers 4 and 8 are somewhat related, but that is easy with post 2’s topic on ‘computers science with/for biology’, of which the papers are just two examples (conceptual modeling for biology and ontologies in ecology).

One can only speculate why this is so… It may well be that the population of blog-readers is disjoint from the population who consider my scientific contributions of some value. Or perhaps the highly informal posts about my research do not attract the precious time of the researchers (which does not entail any consequences regarding the translocation of the negation in that statement!); however, perhaps scientists should read more blogs and start one of their own. John Baez has posted an interesting draft article about the usefulness of blogs in research, and (update addition on 15-10-’09:) Gowers and Nielsen describe their take on online collaborative research based on the Polymath project experience in the openly accessible Nature opinion article [1]. Maybe we can dish up examples for research in computer science, or the Semantic Web in particular, to demonstrate there are some benefits to it. Anyone has an example?

References

[1]  Timothy Gowers and Michael Nielsen. (2009). Massively collaborative mathematics. Nature 461, 879-881.

Metadata and Semantics conference MTSR’09 and semantics for the agricultural domain

The third international conference on Metadata and Semantics Research (MTSR’09) was held 1-2 October in Milan at the DiSCO faculty at the University of Milano-Bicocca. The conference focuses on the engineering aspects of metadata and semantics in applications: usage of ontologies and ontology-like artifacts, applications using Semantic Web and other technologies, metrics, surveys, and two recurring application areas, being eLearning and a whole day (1/3 of the conference) on semantics in agriculture, food & the environment.

The general impression I had was that while everyone sees the advantages of the semantic layer with ontologies (and thesauri, structured controlled vocabularies etc.) and Semantic Web technologies, it is still more difficult to implement it than initially perceived regarding (i) the time and effort it takes to get a system going and (ii) the learning curve of the people involved. In fact, quite a few people did not hide the hurdles they stumbled upon, which either were solved with additional human resources for the manual interventions, or fixed with a new engineering solution or a workaround, or left for future works by themselves or as thinly veiled hint to others. The hurdles include, but are not limited to: compatibility issues with the format of the original data and the desired or required format, dealing with maintaining backward compatibility, use of multiple ontologies in one system in some way, usage of only very basic querying (where they immediately seemed to settle for the limitations), and generally that Semantic Web tools—being predominantly academic prototypes—are too immature and unstable for deployment in operational information systems. On the positive side, there are also success stories where one can see the usefulness of ontologies and Semantic Web technologies.

Agriculture & Semantics Track

Within the Semantics & Agriculture, Food & Environment track, another large software application was presented, which was developed within the Seamless IP project. Ioannis Athanasiadis et al’s paper [1] focuses on the 11 small ontologies—preferred over one large ontology—they had developed, which are used in a scientific workflow that combines agricultural data and knowledge from different applications with the EU common agricultural Policy (CAP) to compute consequences of possible policies and to devise suggestions for farmers. Noteworthy is that they reused the RDFS label for language (lang) not for natural language translations of the concepts in the ontologies, but for programming languages. For instance, the concept Crop is represented in the OWL ontology and it is used across applications in the workflow: the owl:Class then has nested an <rdfs:label xml:lang=”gms”>C</rdfs:label> so that Crop in the ontology is linked to C that represents Crop in the application that was programmed in GAMS, and with <rdfs:label xml:lang=”aps”>Crop</rdfs:label> it is linked to the Agricultural Production and Externalities Simulator (APES) model written in C#. This solution also would address Daniel Martini’s [2] complaint that there are too few tools for OWL for other programming languages than Java, which are used more often in agriculture applications (Martini preferred C). The paper about networked ontologies by the Food and Agriculture Organisation of the UN discusses ongoing work and challenges for their networked ontologies case study about the fisheries domain within the NeOn project [3]; unfortunately, the FAO employees were shining in their absence to discuss the latest results of their experiments.

On the bright side, clear and unambiguous feel-good results thanks to the use of ontologies were presented as well. Ziemba, Cornejo and Beck developed and used ontologies for urban water conservation in Florida by linking the ontologies to digital libraries as well as using the ontologies to generate dynamically website content about events and news [4]. Ferrández of the University of Alicante presented the experiment done with Katia Vila of the University of Matanzas to enhance a question answering (QA) system with an ontology. They had a so-called Open Domain QA system for Spanish, AliQAn, which did not work for retrieving the correct answer when it was applied to scientific articles in the agricultural domain (about 2000 articles of the RCCA journal). So, Vila and Ferrández developed an ontology and dressed it up with WordNet and AGROVOC so as to have a Restricted Domain QA, and, lo and behold, it performed well; i.e., the correct answers were retrieved in 6 out of 8 samples (cf. 0 out of 8 without the ontology). When Vila and Ferrández looked into why this was the case, it appeared that the benefit of the ontology was in correct identification of the question, not specifically the information retrieval part. For instance, the question “¿Cuáles son los metabolitos principales que vienen del tracto digestivo?” [“which are the main metabolites that come from the digestive tract?”] was incorrectly classified as an entity-profession question with the open domain QA, whereas with the ontology-supported one, it was correctly classified as an entity-substance question. Put differently, the Open Domain QA system does not understand the complexities of questions in the agricultural domain.

Zschocke of the United Nations University talked about quality education and centralized learning repositories for an integrated eLearning system for the Consultative Group on International Agricultural Research (CGIAR), and the associated metadata creation for dissemination activities in particular [6]. Last, some explorative survey results and general scope of the EU 5-year Trace project were presented by Kathryn Donnelly of Nofima [7]. Products and ingredients have to be traceable to the source, so there will have to be some passing on of data among the companies, even though there is little standardization and idea how to do it other than the TraceCore XML. They are still pondering where and how to use ontologies, if at all (now they used a simple data list). The presentation of my own paper [8] was well-received, although perceived to be a bit overloaded with information; here’s the outline and summary posted earlier.

Main track and keynote

Luciano Serafini of the FBK was invited to give the keynote speech, which was about the APOSDLE project for e-learning and work-integrated learning in particular. Among the activities within the project was the development of “an integrated modelling methodology”, which resulted in a wiki, called MOKI. The approach consists of, primarily, splitting up the ontology development into informal modelling for the domain experts, a light formalization, and full axiomatization for the knowledge engineers so that one ends up with three different views for each element in the ontology. In addition, they found it useful to identify three roles in the ontology development process: the domain expert role, the knowledge engineer role, and the coach role to mediate between the former two.

Papers in the main track included, among others, the one of Federica Viti and colleagues of ItalBioNet about the system they developed to have a systems biology approach to breast cancer (G2SBC), focusing on the enrichment of the system with 9 ontologies and ontology-like artifacts [9]. Matteo Palmonari of the University Milano-Bicocca presented database schema integration into a “Semantic Peer Data Ontology”, which was augmented with a “Global Light Services Ontology” to deal with the services so that the whole system can be queried to retrieve the data from the original sources (the queries are limited to attributes of a concept, i.e., projections on a table) [10].

Overall, it was useful to attend and listen to what people are doing with the idea of, and technologies for, adding more semantics to their applications. As for the agricultural domain, I have the impression that it has, like ecology, lots of ‘legacy systems’ (i.e., that are currently operational but which may do better with more explicit semantics) in more diverse information systems than Semantic Web theory and tools researchers assume and that it also runs into more issue to make those systems “semantic web enabled” than medical and genomics information systems.

References

Note: the references that do not have a URL are only online on the Springer website at the time of writing this post.

[1] I.N. Athanasiadis, A. E. Rizzoli, S. Janssen, E. Andersen, F. Villa. Ontology for seamless integration of agricultural data and models. In: Proceedings of MTSR’09. Springer CCIS 46, 282-293.

[2] D. Martini, M. Schmitz, J. Frisch, M. Kunisch. A Service Architecture for Facilitated Metadata Annotation and Resource Linkage Using agroXML and ReSTful Web Services. In: Proceedings of MTSR’09. Springer CCIS 46, 239-244.

[3] C. Caracciolo, J. Heguiabehere, M. Sini, J. Keizer. Networked Ontologies from the Fisheries Domain. In: Proceedings of MTSR’09. Springer CCIS 46, 257-262.

[4] L. Ziemba, C. Cornejo, H. Beck. A Water Conservation Digital Library Using Ontologies. In: Proceedings of MTSR’09. Springer CCIS 46, 263-269.

[5] 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.

[6] T. Zschocke, J. Beniest. Assuring the Quality of Agricultural Learning Repositories: Issues for the Learning Object Metadata Creation Process of the CGIAR. In: Proceedings of MTSR’09. Springer CCIS 46, 226-238.

[7] K. A.-M. Donnelly, J. van der Roest, S. T. Hoskuldsson, P. Olsen, K. M. Karlsen. Improving Information Exchange in the Chicken Processing Sector Using Standardised Data Lists. In: Proceedings of MTSR’09. Springer CCIS 46, 312-321.

[8] Keet, C.M. Ontology design parameters for aligning agri-informatics with the Semantic Web. In: Proceedings of MTSR’09. Springer CCIS 46, 239-244.

[9] F. Viti, E. Mosca, I. Merelli, A. Calabria, R. Alfieri,L. Milanesi. Ontological enrichment of the Genes-to-Systems Breast Cancer database. In: Proceedings of MTSR’09. Springer CCIS 46, 171-182.

[10] D. Beneventano, F. Guerra, A. Maurino, M. Palmonari, G. Pasi, A. Sala. Unified Semantic Search of Data and Services. In: Proceedings of MTSR’09. Springer CCIS 46, 95-107.