Anita de Waard

A topic-based browser for large online resources

The exploration of large information spaces is a difficult task, especially if the user is not familiar with the terminology used to describe information. Conceptual models of a domain in terms of thesauri or ontologies can leverage this problem to some extend. In order to be useful, there is a need for interactive tools for exploring large information sets based on conceptual knowledge. We present a thesaurus based browser that supports a mixed-initiative exploration of large online resources that provides support for thesaurus-based search and topic-based exploration of query results. We motivate the chosen exploration strategy the browser functionality, present the results of user studies and discuss future improvements of the browser.

A pragmatic structure for research articles

Our goal is to develop a new format for scientific research articles, which will facilitate their use in a computer-assisted environment. By explicitly marking up rhetorical and knowledge elements in the text, an attempt is made to optimally represent the argumentation contained within the article. To this end, a structure of discourse segments and their relations is proposed. Applying this structure to a set of research articles could enable the creation or population of a system to visualize and access scientific argumentation within a corpus of research articles.

Dynamic enhancement of drug product labels to support drug safety, efficacy, and effectiveness

Out-of-date or incomplete drug product labeling information may increase the risk of otherwise preventable adverse drug events. In recognition of these concerns, the United States Federal Drug Administration (FDA) requires drug product labels to include specific information. Unfortunately, several studies have found that drug product labeling fails to keep current with the scientific literature. We present a novel approach to addressing this issue. The primary goal of this novel approach is to better meet the information needs of persons who consult the drug product label for information on a drug’s efficacy, effectiveness, and safety. Using FDA product label regulations as a guide, the approach links drug claims present in drug information sources available on the Semantic Web with specific product label sections. Here we report on pilot work that establishes the baseline performance characteristics of a proof-of-concept system implementing the novel approach. Claims from three drug information sources were linked to the Clinical Studies, Drug Interactions, and Clinical Pharmacology sections of the labels for drug products that contain one of 29 psychotropic drugs. The resulting Linked Data set maps 409 efficacy/effectiveness study results, 784 drug-drug interactions, and 112 metabolic pathway assertions derived from three clinically-oriented drug information sources (ClinicalTrials.gov, the National Drug File – Reference Terminology, and the Drug Interaction Knowledge Base) to the sections of 1,102 product labels. Proof-of-concept web pages were created for all 1,102 drug product labels that demonstrate one possible approach to presenting information that dynamically enhances drug product labeling. We found that approximately one in five efficacy/effectiveness claims were relevant to the Clinical Studies section of a psychotropic drug product, with most relevant claims providing new information. We also identified several cases where all of the drug-drug interaction claims linked to the Drug Interactions section for a drug were potentially novel. The baseline performance characteristics of the proof-of-concept will enable further technical and user-centered research on robust methods for scaling the approach to the many thousands of product labels currently on the market.

Improving The Future of Research Communications and e-Scholarship (Dagstuhl Perspectives Workshop 11331)

Research and scholarship lead to the generation of new knowledge. The dissemination of this knowledge has a fundamental impact on the ways in which society develops and progresses, and at the same time it feeds back to improve subsequent research and scholarship. Here, as in so many other areas of human activity, the internet is changing the way things work: it opens up opportunities for new processes that can accelerate the growth of knowledge, including the creation of new means of communicating that knowledge among researchers and within the wider community. Two decades of emergent and increasingly pervasive information technology have demonstrated the potential for far more effective scholarly communication. However, the use of this technology remains limited; research processes and the dissemination of research results have yet to fully assimilate the capabilities of the web and other digital media. Producers and consumers remain wedded to formats developed in the era of print publication, and the reward systems for researchers remain tied to those delivery mechanisms. Force11 (the Future of Research Communication and e-Scholarship) is a community of scholars, librarians, archivists, publishers and research funders that has arisen organically to help facilitate the change toward improved knowledge creation and sharing. Individually and collectively, we aim to bring about a change in scholarly communication through the effective use of information technology. Force11 has grown from a small group of like-minded individuals into an open movement with clearly identified stakeholders associated with emerging technologies, policies, funding mechanisms and business models. While not disputing the expressive power of the written word to communicate complex ideas, our foundational assumption is that scholarly communication by means of semantically-enhanced media-rich digital publishing is likely to have a greater impact than communication in traditional print media or electronic facsimiles of printed works. However, to date, online versions of ‘scholarly outputs’ have tended to replicate print forms, rather than exploit the additional functionalities afforded by the digital terrain. We believe that digital publishing of enhanced papers will enable more effective scholarly communication, which will also broaden to include, for example, better links to data, the publication of software tools, mathematical models, protocols and workflows, and research communication by means of social media channels. This document, also known as the Force 11 Manifesto, highlights the findings of the Force11 workshop on the Future of Research Communication and e-Scholarship held at Schloss Dagstuhl, Germany, in August 2011: it summarizes a number of key problems facing scholarly publishing today, and presents a vision that addresses these problems, proposing concrete steps that key stakeholders can take to improve the state of scholarly publishing. More about Force11 can be found at http://www.force11.org. This White Paper is a collaborative effort that reflects the input of all Force11 attendees at the Dagstuhl Workshop 1, and is very much a living document 2 . We see it as a starting point that will grow and be updated and augmented by individual and collective efforts by the participants and others. We invite you to join and contribute to this enterprise.

Automated detection of discourse segment and experimental types from the text of cancer pathway results sections

Automated machine-reading biocuration systems typically use sentence-by-sentence information extraction to construct meaning representations for use by curators. This does not directly reflect the typical discourse structure used by scientists to construct an argument from the experimental data available within a article, and is therefore less likely to correspond to representations typically used in biomedical informatics systems (let alone to the mental models that scientists have). In this study, we develop Natural Language Processing methods to locate, extract, and classify the individual passages of text from articles’ Results sections that refer to experimental data. In our domain of interest (molecular biology studies of cancer signal transduction pathways), individual articles may contain as many as 30 small-scale individual experiments describing a variety of findings, upon which authors base their overall research conclusions. Our system automatically classifies discourse segments in these texts into seven categories (fact, hypothesis, problem, goal, method, result, implication) with an F-score of 0.68. These segments describe the essential building blocks of scientific discourse to (i) provide context for each experiment, (ii) report experimental details and (iii) explain the data’s meaning in context. We evaluate our system on text passages from articles that were curated in molecular biology databases (the Pathway Logic Datum repository, the Molecular Interaction MINT and INTACT databases) linking individual experiments in articles to the type of assay used (coprecipitation, phosphorylation, translocation etc.). We use supervised machine learning techniques on text passages containing unambiguous references to experiments to obtain baseline F1 scores of 0.59 for MINT, 0.71 for INTACT and 0.63 for Pathway Logic. Although preliminary, these results support the notion that targeting information extraction methods to experimental results could provide accurate, automated methods for biocuration. We also suggest the need for finer-grained curation of experimental methods used when constructing molecular biology databases

Elsevier’s approach to the bioCADDIE 2016 Dataset Retrieval Challenge

Abstract We developed a two-stream, Apache Solr-based information retrieval system in response to the bioCADDIE 2016 Dataset Retrieval Challenge. One stream was based on the principle of word embeddings, the other was rooted in ontology based indexing. Despite encountering several issues in the data, the evaluation procedure and the technologies used, the system performed quite well. We provide some pointers towards future work: in particular, we suggest that more work in query expansion could benefit future biomedical search engines. Database URL: https://data.mendeley.com/datasets/zd9dxpyybg/1

Changing modes of scientific discourse analysis, changing perceptions of science

Only New information technologies from text extraction, to data visualisation and semantic technologies, introduce a knowledge representation that reflects the view of the actors building the tools of the knowledge they are trying to represent. In the case of technologies applied to scientific knowledge, the tools thus represent a view of the core tenets, goals, and results of science, which are embodied in the standards, models and tools built to manipulate scientific data, rhetoric, and knowledge. We are interested in identifying a few trends in recent data modelling developments that, we believe, represent an increasingly ‘human-centric’ view of scientific discourse.

Research data management at Elsevier: Supporting networks of data and workflows

Sharing research data has the potential to make research more reproducible and efficient. Scientific research is a complex process and it is crucial that at the different stages of this process, researchers handle data in a way that will allow sharing and reuse. In this paper, we present a framework for the different steps involved in managing research data: a hierarchy of research data needs, and describe some of our own ongoing efforts to support these needs. Creating a good data ecosystem that supports each of these data needs requires collaboration between all parties that are involved in the generation, storage, retrieval and use of data: researchers, librarians, institutions, government offices, funders, and also publishers. We are actively collaborating with many other participants in the research data field, to develop a data ecosystem that enables data to be more useful, and reusable, throughout science and the humanities.