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Science aims to develop an accurate understanding of reality through a variety of rigorously empirical and formal methods. Ontologies are used to formalize the meaning of terms within a domain of discourse. The Basic Formal Ontology is an ontology of particular importance in the biomedical domains, where it provides the top-level for numerous ontologies, including those admitted as part of the OBO Foundry collection. The Basic Formal Ontology requires that all classes in an ontology are actually instantiated in reality. Despite the fact that it is hard to show whether entities of some kind exist or do not exist in reality (especially for unobservable entities like elementary particles), this criterion fails to satisfy the need of scientists to communicate their findings and theories unambiguously. We discuss the problems that arise due to the Basic Formal Ontology’s realism criterion and suggest viable alternatives.

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Access to consistent, high-quality metadata is critical to finding, understanding, and reusing scientific data. This document describes a consensus among participating stakeholders in the Health Care and the Life Sciences domain on the description of datasets using the Resource Description Framework (RDF). This specification meets key functional requirements, reuses existing vocabularies to the extent that it is possible, and addresses elements of data description, versioning, provenance, discovery, exchange, query, and retrieval.

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With its focus on investigating the nature and basis for the sustained existence of living systems, modern biology has always been a fertile, if not challenging, domain for formal knowledge representation and automated reasoning. Over the past 15 years, hundreds of projects have developed or leveraged ontologies for entity recognition and relation extraction, semantic annotation, data integration, query answering, consistency checking, association mining and other forms of knowledge discovery. In this talk, I will discuss our efforts to build a rich foundational network of ontology-annotated linked data, discover significant biological associations across these data using a set of partially overlapping ontologies, and identify new avenues for drug discovery by applying measures of semantic similarity over phenotypic descriptions. As the portfolio of Semantic Web technologies continue to mature in terms of functionality, scalability and an understanding of how to maximize their value, increasing numbers of biomedical researchers will be strategically poised to pursue increasingly sophisticated KR projects aimed at improving our overall understanding of the capability and behavior of biological systems.

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Ontologies are quickly becoming a core part of biomedical infrastructure, where they serve as a means to standardize terminology, to enable access to domain knowledge, to verify data consistency and to facilitate integrative analyses over heterogeneous biomedical data. Given the increased use of ontologies in scientific research, we must first consider the consistent evaluation of ontology-powered research so as to quantitatively evaluate the contribution of the ontology to the effort. Quantitative evaluation of research could then lead to systematic improvement of the application and performance of an ontology (as a key measures of quality) and enable the comparison of any ontology to the overall result. With the emergence of vast amounts of relatively schema-light biomedical Linked Open Data such as that provided by the open source Bio2RDF project, new opportunities arise for applying, evaluating and increasing the utility of ontologies in biomedical research.

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The Translational Medicine Ontology provides terminology that bridges diverse areas of translational medicine including hypothesis management, discovery research, drug development and formulation, clinical research, and clinical practice. Designed primarily from use cases, the ontology consists of essential terms that are mapped to other ontologies. It serves as a global schema for data integration while simultaneously facilitating the formulation of complex queries across heterogeneous sources. We demonstrate the utility of the ontology through question answering over a prototype knowledge base composed of sample patient data integrated with linked open data. This work forms a basis for the development of a computational platform for managing information relevant to personalized medicine.

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In the quest to translate the results of life science research into effective clinical applications, many are now turning their attention to and also trying to make sense of the large and rapidly growing amount of biological and biomedical data. Indeed, getting a grip on and keeping on top of the daily flood of new information, whether it be the latest in clinical reviews, scientific reports, or raw data is an ever-present and widely-recognized challenge. The limited access to structured, integrated and citable data limits our ability to exploit a rich source of scientific knowledge for clinical and translational research. While keeping the dual goals of increasing our understanding of how living systems respond to chemical agents and translating our combined knowledge into clinical applications, I will discuss our efforts to leverage SemanticWeb technologies to facilitate the formulation, publication, integration, and discovery of biological facts, expert knowledge and services of value to pharmaceutical and clinical research, and more recently, with applications for the patient-centric delivery of health care.

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Effective data discovery is crucial for maintaining compliance and mitigating risks in today's rapidly evolving privacy landscape. However, traditional manual approaches often struggle to keep pace with the growing volume and complexity of data. Join us for an insightful webinar where industry leaders from TrustArc and Privya will share their expertise on leveraging AI-powered solutions to revolutionize data discovery. You'll learn how to: - Effortlessly maintain a comprehensive, up-to-date data inventory - Harness code scanning insights to gain complete visibility into data flows leveraging the advantages of code scanning over DB scanning - Simplify compliance by leveraging Privya's integration with TrustArc - Implement proven strategies to mitigate third-party risks Our panel of experts will discuss real-world case studies and share practical strategies for overcoming common data discovery challenges. They'll also explore the latest trends and innovations in AI-driven data management, and how these technologies can help organizations stay ahead of the curve in an ever-changing privacy landscape.

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