We present a new summarisation task, taking scientific articles and producing journal tableof-contents entries in the chemistry domain.These are one-or two-sentence author-written summaries that present the key findings of a paper.This is a first look at this summarisation task with an open access publication corpus consisting of titles and abstracts, as input texts, and short author-written advertising blurbs, as the ground truth.We introduce the dataset and evaluate it with state-of-the-art summarisation methods.

When are two entries about a small molecule in different datasets the same? If they have the same drug name, chemical structure, or some other criteria? The choice depends upon the application to which the data will be put. However, existing Linked Data approaches provide a single global view over the data with no way of varying the notion of equivalence to be applied.In this paper, we present an approach to enable applications to choose the equivalence criteria to apply between datasets. Thus, supporting multiple dynamic views over the Linked Data. For chemical data, we show that multiple sets of links can be automatically generated according to different equivalence criteria and published with semantic descriptions capturing their context and interpretation. This approach has been applied within a large scale public-private data integration platform for drug discovery. To cater for different use cases, the platform allows the application of different lenses which vary the equivalence rules to be applied based on the context and interpretation of the links.

We present a method which exploits automatically generated scientific discourse annotations to create a content model for the summarisation of scientific articles.Full papers are first automatically annotated using the CoreSC scheme, which captures 11 contentbased concepts such as Hypothesis, Result, Conclusion etc at the sentence level.A content model which follows the sequence of CoreSC categories observed in abstracts is used to provide the skeleton of the summary, making a distinction between dependent and independent categories.Summary creation is also guided by the distribution of CoreSC categories found in the full articles, in order to adequately represent the article content.Finally, we demonstrate the usefulness of the summaries by evaluating them in a complex question answering task.Results are very encouraging as summaries of papers from automatically obtained CoreSCs enable experts to answer 66% of complex content-related questions designed on the basis of paper abstracts.The questions were answered with a precision of 75%, where the upper bound for human summaries (abstracts) was 95%.

MOTIVATION: Scholarly biomedical publications report on the findings of a research investigation. Scientists use a well-established discourse structure to relate their work to the state of the art, express their own motivation and hypotheses and report on their methods, results and conclusions. In previous work, we have proposed ways to explicitly annotate the structure of scientific investigations in scholarly publications. Here we present the means to facilitate automatic access to the scientific discourse of articles by automating the recognition of 11 categories at the sentence level, which we call Core Scientific Concepts (CoreSCs). These include: Hypothesis, Motivation, Goal, Object, Background, Method, Experiment, Model, Observation, Result and Conclusion. CoreSCs provide the structure and context to all statements and relations within an article and their automatic recognition can greatly facilitate biomedical information extraction by characterizing the different types of facts, hypotheses and evidence available in a scientific publication. RESULTS: We have trained and compared machine learning classifiers (support vector machines and conditional random fields) on a corpus of 265 full articles in biochemistry and chemistry to automatically recognize CoreSCs. We have evaluated our automatic classifications against a manually annotated gold standard, and have achieved promising accuracies with 'Experiment', 'Background' and 'Model' being the categories with the highest F1-scores (76%, 62% and 53%, respectively). We have analysed the task of CoreSC annotation both from a sentence classification as well as sequence labelling perspective and we present a detailed feature evaluation. The most discriminative features are local sentence features such as unigrams, bigrams and grammatical dependencies while features encoding the document structure, such as section headings, also play an important role for some of the categories. We discuss the usefulness of automatically generated CoreSCs in two biomedical applications as well as work in progress. AVAILABILITY: A web-based tool for the automatic annotation of articles with CoreSCs and corresponding documentation is available online at http://www.sapientaproject.com/software http://www.sapientaproject.com also contains detailed information pertaining to CoreSC annotation and links to annotation guidelines as well as a corpus of manually annotated articles, which served as our training data. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Biochemical ‘small molecules’ are involved in all living processes across all biological domains. Chemical ontologies provide structured chemical data and thereby support cross-disciplinary and integrative research across systems biology, chemogenomics and metabolomics. Efforts are underway to align ChEBI with upper level ontologies such as BFO, but confusion persists as to the ontological status of the ChEBI ‘role’ entities, which refer to continuants which inhere in chemical entities by virtue of the activity of the chemical entities. We provide a formal classification of these ‘role’ entities according to the continuants and endurants on which they ontologically depend, discuss the nature of chemical dispositions and the relevance of institutional reality, and address granularity issues in modelling chemical activity.

In chemistry, advances in computational technologies have allowed research into molecules that have not been synthesized yet, and may never be, to become widespread. These are described in terms of their structures, which are expressed as chemical graphs. Chemical graphs are a representational artifact, and as such are of a different ontological nature than the molecular entities which they describe.

Chemical entities are the foundation of biochemistry and biology, but until now there have been few coherent attempts to produce a top-level ontology for chemistry to connect ontological descriptions of reality at the molecular level, such as ChEBI, with upper-level ontologies such as BFO, or indeed familiar laboratory-scale concepts such as mixtures. We work out relationships between chemical types that are compatible with the OBO Relation Ontology, describe macroscopic chemical systems in terms of grains and collectives, and propose a top-level ontology for chemically-relevant continuants and discuss it in relation to BFO and BioTop.