SKIMMR: Facilitating knowledge discovery in life sciences by machine-aided skim reading

Background:                   Unlike full reading, 'skim-reading' involves the process of looking quickly over information in an attempt to cover more material whilst still being able to retain a superficial view of the underlying content. Within this work, we specifically emulate this natural human activity by providing a dynamic graph-based view of entities automatically extracted from text. For the extraction, we use shallow parsing, co-occurrence analysis and semantic similarity computation techniques. Our main motivation is to assist biomedical researchers and clinicians in coping with increasingly large amounts of potentially relevant articles in life sciences.                                                     Methods:                   To construct the high-level network overview of articles, we extract weighted binary statements from the text. We consider two types of these statements, co-occurrence and similarity, both organised in the same distributional representation (i.e., in a vector-space model). For the co-occurrence weights, we use point-wise mutual information that indicates the degree of non-random association between two co-occurring entities. For computing the similarity statement weights, we use cosine distance based on the relevant co-occurrence vectors. These statements are used to build fuzzy indices of terms, statements and provenance article identifiers, which support fuzzy querying and subsequent result ranking. These indexing and querying processes are then used top construct a graph-based interface for searching and browsing entity networks extracted from articles, as well as articles relevant to the networks being browsed.                                                     Results:                   We provide a web-based prototype (called `SKIMMR') that generates a network of inter-related entities from a set of documents which a user may explore through our interface. When a particular area of the entity network looks interesting to a user, the tool displays the documents that are most relevant to those entities of interest currently shown in the network. We present this as a methodology for browsing a collection of research articles. To illustrate the practical applicability of SKIMMR, we present examples of its use in the domains of Spinal Muscular Atrophy and Parkinson's Disease. Last but not least, we describe a methodology for automated experimental evaluation of SKIMMR instances. The method uses formal comparison of the graphs generated by our tool to relevant gold standards based on manually curated PubMed, TREC challenge and MeSH data. The results of experiments performed on three different instances of SKIMMR show that the presented method for machine-aided skim reading outperforms state of the art tools like PubMed regarding focused browsing and informativeness of the browsing context.                                                     Conclusions:                   In preliminary trials, users find new, interesting and non-trivial facts with SKIMMR. Our evaluation showed a high potential of the presented work for facilitating knowledge discovery in life sciences.