Ontologies in Quantitative Biology: A Basis for Comparison, Integration, and Discovery

As biology is becoming a data-driven discipline, ontologies become increasingly important for systematically capturing the existing knowledge. This essay discusses current trends and how ontologies can also be used for discovery

Classificatory Theory in Data-Intensive Science: The Case of Open Biomedical Ontologies

publication-status: Publishedtypes: ArticleThis is the author's version of a paper that was subsequently published in International Studies in the Philosophy of Science. Please cite the published version by following the DOI link.Knowledge-making practices in biology are being strongly affected by the availability of data on an unprecedented scale, the insistence on systemic approaches and growing reliance on bioinformatics and digital infrastructures. What role does theory play within data-intensive science, and what does that tell us about scientific theories in general? To answer these questions, I focus on Open Biomedical Ontologies, digital classification tools that have become crucial to sharing results across research contexts in the biological and biomedical sciences, and argue that they constitute an example of classificatory theory. This form of theorizing emerges from classification practices in conjunction with experimental know-how and expresses the knowledge underpinning the analysis and interpretation of data disseminated online.Economic and Social Research Council (ESRC)The British AcademyLeverhulme Trus

Theoretical and technological building blocks for an innovation accelerator

The scientific system that we use today was devised centuries ago and is inadequate for our current ICT-based society: the peer review system encourages conservatism, journal publications are monolithic and slow, data is often not available to other scientists, and the independent validation of results is limited. Building on the Innovation Accelerator paper by Helbing and Balietti (2011) this paper takes the initial global vision and reviews the theoretical and technological building blocks that can be used for implementing an innovation (in first place: science) accelerator platform driven by re-imagining the science system. The envisioned platform would rest on four pillars: (i) Redesign the incentive scheme to reduce behavior such as conservatism, herding and hyping; (ii) Advance scientific publications by breaking up the monolithic paper unit and introducing other building blocks such as data, tools, experiment workflows, resources; (iii) Use machine readable semantics for publications, debate structures, provenance etc. in order to include the computer as a partner in the scientific process, and (iv) Build an online platform for collaboration, including a network of trust and reputation among the different types of stakeholders in the scientific system: scientists, educators, funding agencies, policy makers, students and industrial innovators among others. Any such improvements to the scientific system must support the entire scientific process (unlike current tools that chop up the scientific process into disconnected pieces), must facilitate and encourage collaboration and interdisciplinarity (again unlike current tools), must facilitate the inclusion of intelligent computing in the scientific process, must facilitate not only the core scientific process, but also accommodate other stakeholders such science policy makers, industrial innovators, and the general public

Compositionality in Quantitative Semantics. A Theoretical Perspective on Text Mining

Mehler A, Köhler R. Compositionality in Quantitative Semantics. A Theoretical Perspective on Text Mining. In: Mehler A, Köhler R, eds. Aspects of Automatic Text Analysis. Festschrift in Honour of Professor Burghard B. Rieger. Berlin/New York: Springer; 2007: 139-167.This chapter introduces a variant of the principle of compositionality in quantitative text semantics as an alternative to the bag-of-features approach. The variant includes e®ects of context-sensitive interpretation as well as proces- ses of meaning constitution and change in the sense of usage-based semantics. Its starting point is a combination of semantic space modeling and text structure anal- ysis. The principle is implemented by means of a hierarchical constraint satisfac- tion process which utilizes the notion of hierarchical text structure superimposed by graph-inducing coherence relations. The major contribution of the chapter is a conceptualization and formalization of the principle of compositionality in terms of semantic spaces which tackles some well known de¯cits of existing approaches. In particular this relates to the missing linguistic interpretability of statistical meaning representations

Educating Educating medical students to evaluate the quality of health information on the web

Google and googling pose an array of challenges for information professionals. The Google search engine deskills information literacy, so that many people can find some information. Yet the great challenge is knowing what we do not know. We cannot put words into Google that we do not know. Therefore the instruments for diagnosis are blunt and brutal. The field of e-health has great possibilities, yet the lack of information literacy undermines the expertise of professionals and creates misinformation and confusion. This chapter analyzes the means of assessing the quality of health information and describes an approach to improve the ability of medical students to navigate through the various health information available and to critically evaluate a research publication. Improving Internet literacy is required not only to meet the standards for medical education but also to prepare future doctors to deal with patients exposed to an information overload