Standardising Syndromic Classification in Animal Health Data

Transcription

1 Standardising Syndromic Classification in Animal Health Data Veterinary syndromic surveillance (VSS) has been developing fast in the past 5 years (Dórea et al., 2011). An inventory made by the Triple-S project (syndromic surveillance systems in Europe) in 2013 documented 27 VSS systems or initiatives in 12 European countries (Dupuy et al., 2013). This inventory also showed a great variety in the sources of animal health data explored, including drug sales, website hits or help line calls, clinical data, laboratory test requests, mortality reported (or rendering plant activity records) and meat inspection data (Dupuy et al., 2013). As the field of veterinary syndromic surveillance continues to develop, the adoption of syndromic classification standards becomes essential in order to promote maturity of the field and allow comparability of outputs from systems using different animal health data sources from different origins. The National Veterinary Institute in Sweden (SVA), in collaboration with researchers from the Centre for Veterinary Epidemiological Research (CVER) in the Atlantic Veterinay College in Canada, the Veterinary Public Health Institute (VPHI) from the University of Bern, Switzerland, and the French National Institute of Agronomic Research (INRA), have launched a project entitled Standardising Syndromic Classification in Animal Health Data (SSynCAHD). Recognizing that institutions will continue to record animal health data according to their own standards and vocabularies, and recognizing also that data sharing across countries is not easily achievable, SSynCAHD proposes to harmonise syndromic surveillance data use rather than data recording. That would be achieved by standardising the classification of records into syndromes. The advantages would include: an ability to achieve syndromic classification from different sources of data which are (and will continue to be) recorded using the institution s own vocabulary; the ability to compare VSS system outputs; and the more timely development of VSS systems. In order to achieve that, SSynCAHD will make use of current technologies of information management and sharing, which are aimed at promoting intelligent access to data. In particular, SSynCAHD will be informed by the latest thinking relating to the Semantic Web, which supports the development of frameworks to maximize the potential of data sharing and reuse. This document contains two parts. The first is a brief review of the concepts of Semantic Web and ontologies, intended as background information to some of the concepts and technologies we plan to explore within the SSynCAHD framework. The second is the documentation of the first workshop gathering experts interested in the project, which was held after the second International Conference of Animal Health Surveillance (ICAHS) in Havana, Cuba, on May 10 th, The results of this workshop will be used to organize the next steps, and actively search for funding for the project. Future developments of the project and invitations for future workshops will be actively disseminated to a list of researchers and animal health stakeholders who expressed interest in the project. Interested parties are welcome to join the list by sending an to fernanda.dorea@sva.se. 1

2 Intelligent data management and retrieval: Sematic Web, ontologies and other concepts Epidemiology is an inherently multi-disciplinary field, and the sources of data used in animal health vary greatly. Not only the types of animal health data sources are varied, as also the practices used for recording data between countries and even institutions within the same country. Building a framework to improve consistency and integration of those data seems like a daunting task, but it is still a small challenge compared, for instance, to the integration of data sources over the world wide web ( the Web ). While it is a rich content-sharing platform, the contents on the Web adhere to no common data format, and are intended for understanding by human users, not by computers (Ferreira et al., 2013). The Semantic Web is an initiative to promote smarter integration and retrieval of data on the Web (Allemang and Hendler, 2011). In the words of Ferreira et al. (2013): To achieve machine-readability, the Semantic Web perceives information as resources (datasets, documents, etc), which are characterised with links to other resources. Each of these links, also called metadata or annotations, can be seen as a description of the information contained in the resource, that is, its metadata. For instance, a resource about a disease can link to the concept of Europe through the property occurs-in, while a resource about a person can link to Europe through the property born-in. In the Semantic Web, everything is a resource, so the concept of Europe, used above, can also be described through links to other concepts, like Europe contains Portugal. Ferreira et al. (2013) further illustrates this concept with an example: Figure 1 - reproduced from Ferreira et al. (2013): The authors illustrate how a resource (p.e. a document about the occurrence of Influenza in Portugal) would be annotated with its respective metadata, in order to comply with the ideas of a Semantic Web. Up to here, and looking only at the Layer 1 in Figure 1, we can understand this idea of a smarter Web as tagging resources with the appropriate labels. This simpler idea would be solved with the development of standard vocabularies to tag the resources. Standard vocabularies already exist in 2

3 medicine (such as ICD, SNOMED and UMLS), but they don t solve the problems we have stated for organization of animal health data sources for two reasons: they require data to be manually coded into these vocabularies; and they ignore relationships between the terms included in the vocabulary. We can illustrate the benefits and limitations of controlled vocabularies by referring to an example that may be familiar. Medical Subject Headings (MeSH) is a controlled vocabulary used to index articles in biomedical sciences (Lipscomb, 2000). MeSH terms are organized into a hierarchy, similarly to the available clinical vocabularies. Documents are then tagged with the relevant terms from this hierarchical vocabulary, and users looking for articles, for instance, in PubMed, can use the standard set of terms in MeSH to set up their query. Again using an example provided by Ferreira et al. (2013) (emphases not in the original document): Axial length and Eyebrow are categorized under Eye, but one is a property and the other is a nearby structure. Likewise, Eye is both categorized under Sense Organs and Face, but while it is a sense organ, it is part of the face. MeSH makes no distinction between these semantic relations, which we consider one of the main drivers for the use of ontologies. There are other limitations with MeSH: since they have a generic and broad domain, the addition of new concepts is non-trivial, and there is a high risk of introducing errors and inconsistencies. The semantic relations the author refers to are exemplified in Figure 1 in the second layer of the annotation. Capturing these relationships and, as stated above, modelling them into a formalised representation that prevents the introduction of inconsistencies, requires the use of ontologies. Formally, an ontology defines a common vocabulary for researchers who need to share information in a domain. It includes machine-interpretable definitions of basic concepts in the domain and relations among them (Noy and McGuinness, 2001). Figure 2, reproduced from Ferreira et al. (2013), shows an example of how the relationships can be used to connect concepts in epidemiology, and represent knowledge that could not be contained simply in a hierarchical structure of terms. Figure 2 - reproduced from Ferreira et al. (2013), only part of the original figure is reproduced: Illustration of the relationships between concepts in ontologies. As seen in the very simple representation shown in Figure 2, ontologies model the relationships between classes using properties. Figure 2 exemplified the property has symptom, while Layer II of 3

4 Figure 1 exemplified properties such as is a and caused by. This makes it possible to describe epidemiological relationships, which would connect a varied number of classes. In syndromic surveillance for instance we can envision the need to connect several concepts, each of which could be individually organized into hierarchical classes, such as: pathogens, clinical system, anatomical organ systems, laboratory test requests, causes of death, veterinary drugs, etc. Suppose we want to model the medical knowledge that allows a veterinarian to classify a bacteriological test for Brucellosis as representing a case of a clinical reproductive syndrome. Extensive taxonomies of pathogen exist, so we would know that Brucella sp are bacteria of the genus Brucella, family Brucellaceae, etc. But the knowledge that allows us to correctly classify a suspicion of Brucellosis as a reproductive syndromes comes from the property pathogen:brucella sp affects OrganSystem:reproductive. Moreover, if the data we had was of laboratory test requests we would actually be modelling the knowledge: test:bacteriolical test for Brucella sp identifies pathogen:brucella sp pathogen:brucella sp affects OrganSystem:reproductive. Noy and McGuinness (2001) identify the following reasons for the development of an ontology in a specific domain: To share common understanding of the structure of information among people or software agents; To enable reuse of domain knowledge; To make domain assumptions explicit; To separate domain knowledge from the operational knowledge; To analyse domain knowledge. Referring back to our syndromic surveillance example above, we can see how modelling the knowledge explicitly, in the form of an ontology, indeed leads to achieve objectives such as: making the assumptions explicit; share a common understanding; and reuse domain knowledge. The latter goal is particularly important in a field as multidisciplinary as epidemiology. The Brucelllosis example above showed how we could in fact make use of existing knowledge representations, such as the existing taxonomies for pathogens or ontologies detailing anatomical organ systems. Pesquita et al. (2014) showed that although several ontologies cover some of the subdomains of epidemiology, [there is] a lack of semantic resources for epidemiology-specific terms. The authors demonstrated how existing ontologies can be integrated in order to construct an Epidemiology Ontology. This is possible due to the endorsement of organizations such as the OBO Foundry (Smith et al, 2007). The Open Biomedical Ontologies (OBO) Foundry is an open community that has established a set of principles for ontology development with the goal of creating a suite of interoperable reference ontologies in the biomedical domain (Smith et al, 2007). These principles require that member ontologies be open, orthogonal, expressed in a common shared syntax, and designed to possess a common space of identifiers. One way of meeting the goal of interoperability is to reuse existing resources by importing them into the to-becreated ontology. (Courtot,2014). In other words, the OBO Foundry defines a common set of relations in order to facilitate consistency across multiple ontologies, which in turn facilitates cross-community collaboration and reuse of 4

5 ontologies by other similar domains. The concept mentioned above of orthogonal ontologies refers to no duplication of work and concepts across ontologies, which is ensured in the OBO endorsed ontologies by the requirement that each term is defined only once among all ontologies. Using this framework to gather ontologies of multiple sub-domains related to epidemiology was demonstrated by Pesquita et al. (2014), as said earlier, and can also support the construction of an ontology for syndromic surveillance. We have so far highlighted how the construction of an ontology would support explicit modelling of the knowledge and assumptions used to classify animal health data into syndromes, assuring harmonisation of such classification among veterinary syndromic surveillance systems. It is also important to highlight, that annotation of animal health resources according to such an ontology would not be expected to be performed manually. As stated in the introduction, we expect that institutions will continue to generate resources using their own vocabularies, just as it happens with all manuscripts indexed by PubMed, or all websites in the Web. These data would however then be annotated according to harmonised standards in order to allow syndromic classification. Once again at this step, by using ontologies, the process of annotating a resource can be facilitated through the identification of concept labels using text mining for analysis of the content of the resource (Ferreira et al. 2013). In order to discuss how the use of ontologies could help the development of syndromic surveillance in animal health, and promote the use of modern information technologies in order to increase quality, quantity and speed of conversion of data into surveillance information, a workshop was held in May 2014, the results of which are documented in the following section. 5

6 Minutes from the first SSynCAHD workshop, 10/05/2014, Havana Organizers Fernanda Dórea, National veterinary Institute, Sweden Crawford Revie, Centre for Veterinary Epidemiological Research, Canada Ann Lindberg, National veterinary Institute, Sweden Flavie Vial, Veterinary Public Health Institute, Switzerland (apologies): Céline Dupuy, Anses, France Participants (15) Name Affiliation Country Anne Bronner Anses FR Aurélien Madouasse INRA France Daniela Hadorn BLV Switzerland Petter Hopp Norwegian Veterinary Norway Institute John Berezowski VPH Switzerland José Cortiñas Abrahantes EFSA Italy a.europa.eu Judy Akkina CEAH-USDA USA Laura Streichert International Society for USA Disease Surveillance (ISDS) Michele Anholt University of Calgary Canada Sara Schaerrer BLV Switzerland Fernando Sánchez- Liverpool University UK Viscaino Erik Rattenborg VFL Cattle Denmark Kaspar Krogh VFL Cattle Denmark Damarys Relova CENSA Cuba Elena Arsevska CIRAD France Introduction: background, the SSynCAHD idea & workshop goals The organizers welcomed all participants and emphasized that the idea of Standardising Syndromic Classification in Animal Health Data (SSynCAHD) is not mainly about the way people record animal health data but rather is an attempt to explore how semantic web-based standards can facilitate the more effective analysis of surveillance data. Initially we will focus specifically on the context of syndromic surveillance, but later plan to assess how the approaches developed can be extended to harmonise other animal health surveillance activities. The idea is to agree on ways to store knowledge related to animal health, specifically the knowledge that allows us to classify animal health records into syndromes for syndromic surveillance, in a way that is transparent and flexible so that we can both encourage the sharing of data and analyses in the short-term, as well as provide a framework that can adapt to future changes in our knowledge. 6

7 Our group of animal health researchers is complemented by Dr. Crawford Revie, a computer scientist who will guide us through an assessment of which computer science tools might be most suited to: 1. Storing agreed upon, harmonised concepts of syndromic classification into a common ontology; 2. Developing tools capable of using this ontology to classify animal health records into syndromes in an automated, harmonised way. The pathway from gathering expert opinion, to defining a working ontology, and then to building tools for automated classification of data cannot yet be fully mapped out; we will learn together as we go each step making the next step clearer. However, from the expertise within our group, and the documented experiences of others who have applied such ontology-based approaches, we know that the idea is viable and can provide major benefits. The pre-workshop survey we conducted revealed that we are all more or less starting from the same level. None of the participants had been in the situation where controlled vocabularies were used to mark-up/record their surveillance data, though a number had tried to separate their data into syndromes (with varying degrees of success). This initiative provides a great opportunity to learn and advance together. The survey also indicated that the expectations of participants were realistic and that first steps will be covered during this initial workshop. Here are some expectations noted by participants in the pre-workshop survey: BACKGROUND What are the current issues in this domain? Understand previous work done on syndrome classification SHARING Bring in some of the general concepts of developing syndrome definitions that we have learned from human health as a point of reference METHODS What methods are available for data classification? FUTURE Start a collaboration Discuss funding «Discuss ambitions Move forward with ontology Introduction to ontologies, knowledge base and practical examples Dr. Revie gave an overview of the theory behind ontologies, and exemplified the expected benefits to the field of animal syndromic surveillance. The following themes were covered: What are vocabularies (a fixed set of terms, e.g. MESH terms); thesauri (vocabulary and the relationships between the terms, e.g. hierarchy and synonyms); and ontologies. An ontology stores not only terms, but the relationships among them. (see the first part of this document). A logic-based ontology is what we are ultimately aiming for, to support an appropriate level of machine-automation/interpretability. Such an ontology must be defined in a language underpinned by a logic, giving it precisely specified semantics and computable relationships between terms. Who is using ontologies? Almost everybody in the information space : Google, Facebook, Bing, anyone making money out of knowledge management and search. 7

8 What are ontologies being used for? W3C is the organisation that looks after web standards. On their website ( they list a range of web semantics case studies. They can be used for anything from content discovery to data integration, semantic annotation, etc. Non-unique naming: speakers on the web won t coordinate their naming efforts, the same entity could be known by more than one name in different vocabularies. For this reason entities will usually refer to the vocabulary in which they are defined (their namespace ). These concepts are key to understanding the importance of uniform resource identifiers (URIs). SSynCAHD could create its own namespace or we could use some elements already defined in other vocabularies; such as Dublin Core, SKOS, etc. RDF-Resource Description Framework. Used for describing the properties of resources on the web (the nuggets of knowledge in our data) which are described in terms of triples : subject-predicate-object (e.g. :anthrax :hassign :fever, or, umlsc :fever skos:altlabel :febrile). What is RDF good for? For storing (semi-structured) data, facts about things. There are offthe-shelf tools for storing and querying so-called RDF triple-stores, with a standard query language (SPARQ, which can be thought of as the equivalent of SQL for RDF). They are graphbased, and use IRIs (Internationalised Resource IDs). RDF is used to represent sets of triples. It s not an ontology in itself but it provides the basic building blocks by which ontologies, and much else of the Web, can be represented. OWL: Web ontology language. A language created for expressing ontologies. Formally: OWL is a Semantic Web language designed to represent rich and complex knowledge about things, groups of things, and relations between things. OWL is a computational logic-based language such that knowledge expressed in OWL can be exploited by computer programs, e.g., to verify the consistency of that knowledge or to make implicit knowledge explicit. Examples from the medical domain of particular relevance to our initiative - ESSO (Conway, Dowling & chapman 2011), which stands for Extended Syndromic Surveillance Ontology (as it is an expansion of previous work, on SSO, by Buckeridge & Chapman, 2009). ESSO contains 8 syndromes, and 279 concepts (concepts are for instance fever and can be associated with more than one syndrome). The focus is very much on text-mining within clinical data. As such ESSO does not attempt to be a complex ontology, it is primarily based on SKOS, i.e. it uses the structure of the classic thesaurus, with a few added relationships. Language independence should be a core element of a good ontology design. OWL-based tools (Protégé). Protégé is a software platform that can be used to build and manage ontologies. It facilitates structuring the knowledge and checking consistency among the rules input. There is also a Web version, which encourages collaboration over the Web. Collaborators can track who makes changes, what/when and can discuss or disagree at various levels, all the way down to the most specific term. Open discussions After Crawford s presentation the main questions from the participants related to two main themes: WHAT exactly would be the job of SSynCAHD and of those developing it; and HOW can this be integrated with existing surveillance data collection and analysis tasks. 8

9 With respect to the WHAT question, a trivial example taken from a common tutorial developed for Protégé, was used by way of illustration. Imagine that we want to classify pizzas. If it serves our purpose, we might simply classify pizzas as something that contains two elements: a pizza base and a pizza topping. Pizza toppings can be meat, cheese or vegetables. A tomato topping is a kind of vegetable pizza topping, but we are not concerned with all the tomatoes in the world, only on tomatoes as a pizza topping. So our ontology is restricted to our domain. We must figure out how detailed we need to go into classifying toppings, in order to attend our goals. Do we need to also classify toppings based on caloric content? On weight? Or is meat versus vegetables enough? The ontology makers will define that, and ultimately build an ontology that is able to describe any pizza in the desired domain. Then the ontology will also store any classification, such as for instance vegetarian pizzas contain only non-meat toppings. Once this piece of knowledge is stored in the ontology, the ontology is capable of inferring that a margarita pizza (which has toppings mozzarella, tomato and basil) is a vegetarian pizza. This parallel was used to stress that the ontology builders should be concerned about (i) how to describe the cases how many groups of things (base, toppings) are necessary to describe all pizzas uniquely; (ii) what classes of these units do we want to define (vegetarian pizza, spicy pizza, etc); (iii) the rules needed to classify things, given their components, into these classes. Using this simple example, it was also clarified that for instance, if a pizza producer chain developed their own ontology to classify pizzas into the classes given, weight watchers could then add an attribute called calories and assign values to every pizza. Then a further low calorie versus high calorie class could be created. This demonstrates the great potential of ontologies for more than one goal. The idea of an ontology for animal health data classification was depicted, given this context, as a set of BOXES, each representing one type of health related concepts we will need to handle such as clinical signs, organ systems, laboratory tests, abattoir inspection, etc; and a set of RULES which connect the boxes. These rules relate to the concept of triples explained by Crawford, and are for instance a serological test identifies a pathogen ; a pathogen affects organ systems, test specimens belong to an organ system. The figure below, from a presentation Fernanda made, exemplifies the idea of boxes and rules using Reproductive syndrome as an example: 9

10 Suspicions Cases Organ System Clinical Signs Threats LabTestOrdered TestResults Abattoir finding Reproductive Female Genitalia Vulva Vagina Female Internal Reproducitive Organs Uterus Ovary Male External Genitalia Male Internal Reproductive Organs OTHER SYSTEMS Reproductive Abortion Conception failure OTHER SYNDROMES... Non-Infectious Threats Metabolic Disorders Infectious agents Brucella sp Brucella abortus Brucella suis Brucella canis Neospora caninum BVD IBR UnspecificEtiology BacterialGrowth Histology SerologicalTests Brucellosis BVDV PathogenIdentification Brucellosis BVDV Salmonella Serology Agent Identification Serological Result Ante-Mortem Inspection Post-Mortem Inspection Partial Condemnations Total condemnations Causes Identifies Identifies Affects Reflected as Related to Causes Regarding the HOW questions, it was clarified that the construction of the ontology, on itself, is a MANUAL task. Collaborators will get help from data sources available to understand what levels of detail are needed to characterize animal health data sources, but the ontology is built manually inputting the rules created into (Web) Protégé. The task will not be easy or fast, and the benefits will only come on the long term. But the benefits are worth the effort, as they mean that in the future we could achieve an harmonised syndromic classification of animal health data sources, comparable both vertically (across different types of data sources) and horizontally (same data source from different institutions or even countries). The questions regarding how to use the ontology (once available) to classify data seemed to assume that we d build a text mining tool. It was clarified that this is not entirely true. Once the ontology exists, we will be able to create automated tools that use the ontology knowledge to classify animal health data into syndromic groups. This tool will require pre-processing of the data with varied degrees of text mining, depending on the complexity of the data source. This automated tool, however, will be the last product we will be able to deliver. Before that, as long as we have a functioning ontology, people can already harmonise the way they classify their data, even if not automate the step completely yet. Other discussion points that arose: How to deal with uncertainty, for instance the difference between a suspicion and a case? Or even tests with different specificity/sensitivity? Part of it can be built into the ontology, for instance definition of what constitutes a case and what constitutes as suspicion. For other aspects we will have to accept the uncertainty in the original data (a positive test is accepted 10

11 as positive regardless of the sensitivity, as long as in the data of origin it was considered positive). Ontologies will allow us to have syndromic classifications that are parallel for instance we can create a reproductive syndrome to reflect strictly medical problems related to the reproductive organ system, and in parallel a reproductive failure syndrome more focused on production related problems. Some diseases can be classified both into a specific syndrome, and into the general calf disorders syndrome of more interest to production management. At the same time, the ontology should also be hierarchical. Data with different specificities will allow us to provide varying levels of details in the classification. Different data sources can be compared by generalizing the more specific information up to the level of the more generic information available. What about herd level versus individual data? We will need to remember to deal with this issue not only deal with various animal SPECIES, but also with various levels of animal groupings. It is not clear at this point how (using an extra box?), but it s important to keep the issue in mind. The ontology will never be finished it is a live knowledge-base. But it should reasonably quickly (within a year or so, given some dedicated resources) reach a stage where it can be useful. Our first step is to agree on the logic (a pretty big step). Nobody indicated that they felt this was a fool s errand. Nobody expects it will be easy, but we all agreed that it is a problem we can tackle, and we agreed on the steps to tackle it, as described below in the agreements section. Are there some best practices on how to build ontologies? In the field of ontology building, through tools such as the OWL language and the Protégé software, we will find a lot of guidance and be able to share experience with other ontology builders. However the knowledge is always domain specific. Some basic rules of thumb will apply, but we will still have to figure out some basic issues such as what is a class/how many classes do we need. We need it to be detailed enough to capture the complexity of our domain, but not so detailed that it cannot be generalized for use by all users that can benefit from it. Considerations related to dynamic data and new knowledge: the core of the ontology is not expected to change over a short time frame but the nice thing about RDF model is that it allows you to keep all the information as a RDF triple which you may choose to use or not. This is a by-product of how the data are stored. Agreements It was proposed to start building the ontology: 1. Focusing on syndromic surveillance in particular, and not generalize the idea, at this first moment, into surveillance in general 2. Vertically, focusing on one specific syndrome at a time. Reproductive was proposed as a start because besides the medical interest, it is highly relevant for production data. 3. Data-driven. That is, as different collaborators will have access to different types of animal health data sources, we will be able to progressively identify more boxes, and more rules, which need to be added to the ontology to allow classification of the data sources being evaluated. In this sense collaborators don t need to share data, only use their own data to 11

12 guide the growth of the ontology, and propose new pieces (new boxes and/or new rules) needed to accommodate their data. 4. Intercalate the data-driven growth with rounds of expert opinion. Workshops and digital elicitation will serve to solve any conflicts, take decisions when needed, and ensure completeness of the boxes. We may need to create (parallel to the data-driven growth) working groups to focus on specific boxes. For instance use existing controlled vocabularies and classification standards to build boxes such as clinical signs (existing ICAR standards), organ systems, etc. For the development of the ontology we will use Web Protégé, a tool that allows collaboration over the web to create pieces of the ontology. Collaborators of the SSynCAHD are not expected to learn how to work on the software. The SSynCAHD organizing committee will serve to guide the IT/tool implementation aspects, and to facilitate the various working groups. Collaborators are only expected to contribute with their expertise, either participating only as advisors in the expert elicitation rounds, or actively as collaborators in the data-driven process. As a result, three levels of organization will exist: A. the organizing committee, which will seek funding for its work; B. the active collaborators, which will need to seek funding on their own, for the work they will do with their respective data sources; the organizing committee will provide support when needing to write proposals, providing for instance technical details of the methods to be used; C. experts involved in the elicitation rounds, answering questions and helping to take decisions and solve conflicts. Collaborations are welcomed into any of these roles. The organizing committee will keep up to date documentation of all activities. It was agreed not to restrict, at this stage, our idea of what data sources to use we will continue to grow as more active collaborators want to join. The same goes for animal species ideally we would like to include also aquatic animals, as long as we have active collaborators. Actions 1. The organizing committee will work on a concrete example to more clearly demonstrate the concepts discussed during this workshop: ontologies, Web-Protégé and what we hope can be an effective mode of collaboration. Fernanda Dórea (SVA, Sweden), Céline Dupuy (ANSES, France) and Flavie Vial (VPHI, Switzerland) have created a little trial of such a collaboration by trying to create the boxes and the rules that would be needed to classify their data (laboratory test requests, test results and clinical signs in Sweden, and abattoir condemnation data in France and Switzerland) as regards a reproductive syndrome. This concrete example will be described in a word document and in Web-Protégé. In this way, participants will be able to visualize how data translates into a structured idea of boxes and rules. Collaborators will then be able to use this example to draw their own structures from their data. The step of then adding this structure into Protégé can be facilitated by the organizing committee on the collaborators behalf (it will feature in the ontology as added by 12

13 the respective collaborators, giving proper credit to all those who participate). In other words, a template for how collaboration should work will be documented and disseminated. 2. Given discussions of how we can best learn from the public health domain, and considering the example presented by Crawford of ESSO, Laura Streichert has offered support from the ISDS, and suggested that the authors of ESSO will be approached by ISDS regarding their availability to present a Webinar on the subject. This webinar will be organized and hosted by the ISDS. 3. Laura also suggested that the ISDS could help in other ways, such as by providing a forum for discussions, especially at the interface with public health professionals. She suggested that the next ISDS meeting in December can be a good venue for discussions, especially considering the option of having a roundtable discussion. The organizing committee will make sure that our concrete example can be presented and discussed before that meeting (December 2014), and they will submit an abstract proposing a roundtable discussion. 4. Fernanda Dórea and Ann Lindberg will set up a space on SVA s cloud for the group to share files. 5. The documentation from the workshop will be edited to attempt to create a paper for submission as part of the special issue of Preventive Veterinary Medicine which will publish the abstracts/papers from ICAHS. 6. Fernanda Dórea and Ann Lindberg will continue to seek funding in Sweden to produce the next round of expert elicitation, after the ISDS meeting in December

14 References Allemang, D. & Hendler, J. Semantic Web for the Working Ontologist: effective modeling in RDFS and OWL Morgan Kaufmann, 2011, 2nd ed. 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, and Suzanna Lewis. The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nature biotechnology, 25(11): , Carolyn E. Lipscomb. Medical Subject Headings (MeSH). Bull Med Libr Assoc. Jul 2000; 88(3): Courtot, M. Semantic models in biomedicine: Building interoperating ontologies for biomedical data representation and processing in pharmacovigilance. The University of British Columbia, Dórea, F. C.; Sanchez, J. & Revie, C. W. Veterinary syndromic surveillance: Current initiatives and potential for development. Preventive Veterinary Medicine, 2011, 101, 1-17 Dupuy, C.;, A. B.; Watson, E.; Wuyckhuise-Sjouke, L.; Reist, M.; Fouillet, A.; Calavasa, D.; Hendrikx, P. & Perrin, J.-B. Inventory of veterinary syndromic surveillance initiatives in Europe (Triple-S project): Current situation and perspectives Preventive Veterinary Medicine, 2013, 111, Ferreira, J. D.; Paolotti, D.; Couto, F. M. & Silva, M. J. On the usefulness of ontologies in epidemiology research and practice Journal of Epidemiology and Community Health, 2013, 65,5. Noy, N. F. & Mcguinness, D. L. Ontology Development 101: A Guide to Creating Your First Ontology Available at Accessed on May 14 th, Pesquita, C.; Ferreira, J.; Couto, F. & Silva, M. The epidemiology ontology: an ontology for the semantic annotation of epidemiological resources Journal of Biomedical Semantics, 2014, 5, 4. W3 Semantic web, available at: Accessed on May 14 th,