Spring House, PA 19477, USA 3 School of Biomedical Informatics, The University of Texas Health Science. Center at Houston, Houston, TX 77030, USA

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1 Editorial: International Workshop on Biomedical Data Mining, Modeling, and Semantic Integration: A Promising Approach to Solving Unmet Medical Needs (BDM2I 2015), Co-located with the 2015 International Semantic Web Conference Dezhao Song 1, Adam Fermier 2, Cui Tao 3 and Frank Schilder 1 1 Research and Development, Thomson Reuters, Eagan, MN 55123, USA 2 Strategic Operations, Janssen Pharmaceutical Research and Development, Spring House, PA 19477, USA 3 School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA 1 Introduction The goal of our BDM2I workshop is to address end to end research problems in mining and understanding free text, data integration, data linking between structured and unstructured data, and in particular how the research in these fields could be utilized by the medicine manufacturing industry for faster and more agile drug development and for monitoring their patient efficacy and safety. The amount of data published in Semantic Web formats has been increasing dramatically in the biomedical field, such as DrugBank, DailyMed, Diseasome, SIDER, LinkedCT, etc. If a medical researcher etc. wants to use this information, however, one is faced with the challenge of linking the same entity across multiple datasets, because each real-world entity (e.g., drug, gene, company, etc.) may be described and published by many data publishers with syntactically distinct identifiers. The same goes for the product dose and formulation thereof. For example, DrugBank and SIDER assign different identifiers to Sutent, a cancer drug developed by Pfizer, and describe it in different ways with complementary information. Such identifiers from different data sources are often not linked to each other and thus prevent end users (e.g., drug manufacturers, government agencies, patients, clinicians, etc.) from obtaining relatively comprehensive information of the entities, such as drug, gene, company, etc. Structured data is only one part of the current problem for there is an ever increasing amount of unstructured data available in electronic laboratory notebooks, clinical notes, medical/engineering literature, and even social media. Textual data covers a variety of important aspects of the biomedical domain, such as drug patenting, clinical trials, drug side effects and adverse reactions. Mining information from free text is non-trivial and can be extremely challenging because clinical notes, laboratory reports, and micro text (e.g., twitter

2 2 Dezhao Song, Adam Fermier, Cui Tao, and Frank Schilder tweets) can differ significantly from standard English text for which most NLP approaches have been developed. As such our workshop has attracted proposals in dealing with this semantic data modeling, mining and integration problem specifically in the biomedical domain on a variety of topics: Biomedical Data Integration and Presentation Integration of heterogeneous data sources Data Integration using crowd sourcing techniques Large-scale Data Integration Schema and Ontology matching Biomedical Knowledge Representation and Reasoning Biomedical Data Mining and Machine Learning Machine Learning and statistical approaches for biomedical data mining Rule-based systems for analyzing and mining biomedical text Semantic annotation of biomedical text Named Entity Recognition and Relation Extraction for biomedical text Entity Linking for/between free text and structured data Data Mining and Machine Learning for social media and their application to the biomedical and clinical domain Applications Semantic Data Modeling, Mining and Integration for drug design and manufacturing Drug repurposing using semantic web technologies Pharmacovigilance and drug/vaccine safety signal identification Novel tools, ontologies and strategies for data interpretation, visualization and presentation Novel tools for visualizing ontologies and reasoning paths to domain experts 2 Workshop Format Our workshop was organized in the following format: Paper presentations: Our workshop program included both regular and short paper presentations [13]. Selected best papers are currently being reviewed for publication to the BioDataMining journal. During the conference, our workshop attracted about 20 to 30 attendees.

3 3 Overview of Accepted Papers Editorial: BDM2I2015, ISWC 2015 Workshop 3 Modeling. Only inconsistent and substantially different guidance about how to manage potential drug-drug interactions is provided in current clinical drug compendia, due to difficulty of synthesizing a complex primary literature (product labeling, the scientific literature, and case reports). Schneider et al. [12] present a standard way to represent potential drug-drug interaction (PDDI) knowledge claims and associated evidence in a computable form. By augmenting the previously created Drug Interaction Knowledge Base [2, 3] (an evidence-focused knowledge base designed to support pharmacoepidemiology and clinical decision support), Schneider et al. enhance maintainability, computability, and also the ability to link to biological processes while retaining logical consistency. Quality. Xing, Cui and Zhang [15] try to clean redundant paths in large ontologies. Large, comprehensive terminological systems such as SNOMED CT and the Gene Ontology (GO) continue to evolve over time. Ontology Quality Assurance (OQA) is an indispensable part of the ontological engineering lifecycle while redundant paths between two concepts can cause methods and algorithms to produce inaccurate results. Two human annotators were adopted to evaluate the results. The system was able to achieve an accuracy of 100% for detecting redundant edges. Among 30 samples evaluated in the SNOMED CT, 80% should have the direct edge removed, and 20% should have an indirect edge removed. Among 50 samples evaluated in the Gene Ontology, 90% should have the direct edge removed, and 10% should have an indirect edge removed. Rather than evaluating a specific ontology, Amith and Tao [1] developed a web application for evaluating the quality of ontologies. The authors proposed the Semiotic-based Evaluation Management System (SEMS). First, the system can automatically generate various quality scores of an uploaded ontology and provide recommendations for improvement. Second, via the web interface, domain experts can conduct manual review and provide feedback. Future plans include integrating this system into existing well-known services (e.g., BioPortal) for broader use and evaluation. Reasoning. To deal with knowledge base incompleteness, Van Woensel et al. [14] complements deductive inference with plausible reasoning, which relies on weaker forms of inferencing such as inductive generalization and analogical reasoning. In contrast to regular Semantic Web reasoning, which requires a priori, explicit knowledge (e.g., rules or ontology constructs), these mechanisms leverage data similarities and tentative, plausible knowledge. Of note, the proposed system provides easily understandable query explanations to medical experts. The authors illustrated the usefulness of the proposed system via a real-world, medical use case. Information Extraction and Integration. Dumitrache, Aroyo and Welty [5] proposed the CrowdTruth method for collecting medical ground truth through crowdsourcing, based on the observation that disagreement between annotators can be used to capture ambiguity in text. The results show that, with appropriate processing, the crowd performs just as well as medical experts in terms of the quality and efficacy of annotations. Also, rather than employing a small

4 4 Dezhao Song, Adam Fermier, Cui Tao, and Frank Schilder number of annotators for collecting ground truth, more annotators per sentence are needed to get the highest quality annotations. Grasso, Joshi and Siegel [6] try to provide more semantics of medical concepts in free text. By doing this, unstructured data (e.g., medical concepts in clinical data/records) are enhanced with more semantics to allow physicians to have a better view of a patient s situation over time. The current approach focuses more on pain and thus future expansions on other concepts would make the system more generally applicable to various domains. In a related effort, Cardillo et al. [4] propose a coding support system (CSS) based on formalized coding rules to help GPs in the coding of health conditions in patient summaries. Such an automated coding support system is especially useful for guaranteeing semantic interoperability across healthcare information systems that exchange patient summary s data and for improving cross-border care. One challenge in the Semantic Web is to appropriately interlink different datasets. Rather than performing the actual linking, Mehdi et al. [9] try to find the linkable datasets. Given a new domain, a few query terms are firstly generated. Such terms are then checked against different SPARQL endpoints in order to find relevant ones as candidates for linking. The current work focused on the Life Sciences domain. Applications. Özgür, Hur and He [11] try to find keyword patterns for classes in an ontology. They augmented the Interaction Network Ontology (INO) [8] by adding multiple literature mining keywords to related INO interaction classes. This augmented ontology is further used to mine the literature for related gene-gene interactions. The authors performed a comprehensive analysis using the Learning Logic in Language (LLL) dataset [10] and identified 27 gene regulation interaction types, each of which was associated with multiple interaction keywords. Hong et al. [7] present a modular architecture for creation of rule-based clinical diagnostic criteria leveraging Semantic Web technologies. The authors evaluated the domain coverage of their proposed ontology model by annotating 20 randomly selected diagnostic criteria, and also tested the transformation algorithms using 6 Quality Data Model (QDM) templates for ontology population and 15 QDM-based criteria data for rule generation. The audience showed interest to this framework and also provided suggestions for further development, so that the proposed architecture could be used in practice. 4 Outlook We believe we have held a successful workshop on biomedical modeling, mining and integration. As we can see that this is a diverse field with many interesting research directions. In the future, first, we would expect that many of the proposed research will become more mature and thus become applicable for realworld use, as many of the them are in prototype stage. Second, we expect to see more work in the integration aspects in the future, i.e., how to interlink the vast amount of data available in the biomedical domain. This is expected to

5 Editorial: BDM2I2015, ISWC 2015 Workshop 5 lead to novel algorithms, since existing linking algorithms may not work well in this domain. Finally, crowdsourcing is a promising field and could be useful in many scenarios. How to best utilize the results from such services still remains a challenging research topic. Acknoledgement We would like to thank all authors for contributing to our workshop and for their great presentation at the workshop. Furthermore, we thank all reviewers for their time and efforts in helping us build an interesting program. References 1. Amith, M., Tao, C.: A web application towards semiotic-based evaluation of biomedical ontologies. In: Proceedings of International Workshop on Biomedical Data Mining, Modeling, and Semantic Integration: A Promising Approach to Solving Unmet Medical Needs (BDM2I2015) (2015) 2. Boyce, R.D., Collins, C., Horn, J.R., Kalet, I.: Computing with evidence: Part I: A drug-mechanism evidence taxonomy oriented toward confidence assignment. Journal of Biomedical Informatics 42(6), (2009) 3. Boyce, R.D., Collins, C., Horn, J.R., Kalet, I.: Computing with evidence: Part II: an evidential approach to predicting metabolic drug-drug interactions. Journal of Biomedical Informatics 42(6), (2009) 4. Cardillo, E., Chiaravalloti, M.T., Eccher, C., Pasceri, E., Mea, V.D., Frattura, L., Guarasci, R.: Towards a rule-based support system for the coding of health conditions in the patient summary. In: Proceedings of International Workshop on Biomedical Data Mining, Modeling, and Semantic Integration: A Promising Approach to Solving Unmet Medical Needs (BDM2I2015) (2015) 5. Dumitrache, A., Aroyo, L., Welty, C.: Achieving expert-level annotation quality with crowdtruth: The case of medical relation extraction. In: Proceedings of International Workshop on Biomedical Data Mining, Modeling, and Semantic Integration: A Promising Approach to Solving Unmet Medical Needs (BDM2I2015) (2015) 6. Grasso, C., Joshi, A., Siegel, E.: Beyond NER: towards semantics in clinical text. In: Proceedings of International Workshop on Biomedical Data Mining, Modeling, and Semantic Integration: A Promising Approach to Solving Unmet Medical Needs (BDM2I2015) (2015) 7. Hong, N., Jiang, G., Pathak, J., Chute, C.G.: Developing a modular architecture for creation of rule-based clinical diagnostic criteria. In: Proceedings of International Workshop on Biomedical Data Mining, Modeling, and Semantic Integration: A Promising Approach to Solving Unmet Medical Needs (BDM2I2015) (2015) 8. Hur, J., Özgür, A., Xiang, Z., He, Y.: Identification of fever and vaccine-associated gene interaction networks using ontology-based literature mining. J. Biomedical Semantics 3, 18 (2012) 9. Mehdi, M., Iqbal, A., Khan, Y., Decker, S., Sahay, R.: Detecting inner-ear anatomical and clinical datasets in the linked open data (LOD) cloud. In: Proceedings of International Workshop on Biomedical Data Mining, Modeling, and Semantic Integration: A Promising Approach to Solving Unmet Medical Needs (BDM2I2015) (2015)

6 6 Dezhao Song, Adam Fermier, Cui Tao, and Frank Schilder 10. Ndellec, C.: Learning language in logic - genic interaction extraction challenge. In: Proceedings of the Learning Language in Logic 2005 Workshop at the International Conference on Machine Learning (2005) 11. Özgür, A., Hur, J., He, Y.: Extension of the interaction network ontology for literature mining of gene-gene interaction networks from sentences with multiple interaction keywords. In: Proceedings of International Workshop on Biomedical Data Mining, Modeling, and Semantic Integration: A Promising Approach to Solving Unmet Medical Needs (BDM2I2015) (2015) 12. Schneider, J., Brochhausen, M., Rosko, S., Ciccarese, P., Hogan, W.R., Malone, D., Ning, Y., Clark, T., Boyce, R.D.: Formalizing knowledge and evidence about potential drug-drug interactions. In: Proceedings of International Workshop on Biomedical Data Mining, Modeling, and Semantic Integration: A Promising Approach to Solving Unmet Medical Needs (BDM2I2015) (2015) 13. Song, D., Fermier, A., Tao, C., Schilder, F. (eds.): Proceedings of International Workshop on Biomedical Data Mining, Modeling, and Semantic Integration: A Promising Approach to Solving Unmet Medical Needs (BDM2I2015) co-located with The 14th International Semantic Web Conference (ISWC 2015), Bethlehem, United States, October 11th, 2015, CEUR Workshop Proceedings, vol CEUR-WS.org (2015) 14. Woensel, W.V., Mohammadhassanzadeh, H., Abidi, S.R., Abidi, S.S.R.: Multistrategy semantic web reasoning for medical knowledge bases. In: Proceedings of International Workshop on Biomedical Data Mining, Modeling, and Semantic Integration: A Promising Approach to Solving Unmet Medical Needs (BDM2I2015) (2015) 15. Xing, G., Cui, L., Zhang, G.: FEDRR: fast, exhaustive detection of redundant hierarchical relations in large biomedical ontologies. In: Proceedings of International Workshop on Biomedical Data Mining, Modeling, and Semantic Integration: A Promising Approach to Solving Unmet Medical Needs (BDM2I2015) (2015)