title: SKIMMR: facilitating knowledge discovery in life sciences by machine-aided skim reading.
abstract: 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 that are being published ongoingly 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 to construct a graph-based interface for searching and browsing entity networks extracted from articles, as well as articles relevant to the networks being browsed. Last but not least, we describe a methodology for automated experimental evaluation of the presented approach. 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. 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 the 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. Finally, we report on the results of experimental evaluation using the two domains and one additional dataset based on the TREC challenge. The results show how the presented method for machine-aided skim reading outperforms tools like PubMed regarding focused browsing and informativeness of the browsing context.
authors.affiliation: Insight Centre (formerly DERI), National University of Ireland Galway , Galway , Ireland.
authors.forename: Vít
authors.initials: V
authors.lastname: Nováček
authors.affiliation: Information Sciences Institute, University of Southern California , Marina del Rey, CA , USA.
authors.forename: Gully A P C
authors.initials: GA
authors.lastname: Burns
keywords: Information visualisation; Machine reading; Publication search; Skim reading; Text mining
references.citation: PLoS Biol. 2004 Nov;2(11):e309
references.pmid: 15383839
references.citation: Science. 2009 Aug 14;325(5942):828-32
references.pmid: 19679805
references.citation: Mov Disord. 2011 Feb 15;26(3):527-33
references.pmid: 21287599
references.citation: Proc AMIA Annu Fall Symp. 1997;:480-4
references.pmid: 9357672
references.citation: AMIA Annu Symp Proc. 2006;:349-53
references.pmid: 17238361
references.citation: AMIA Annu Symp Proc. 2011;2011:1514-23
references.pmid: 22195216
references.citation: AMIA Annu Symp Proc. 2012;2012:587-95
references.pmid: 23304331
references.citation: Nature. 1998 Jun 4;393(6684):440-2
references.pmid: 9623998
references.citation: J Am Med Inform Assoc. 2004 Sep-Oct;11(5):392-402
references.pmid: 15187068
references.citation: BMC Bioinformatics. 2007 Oct 30;8:423
references.pmid: 17971238
references.citation: Proc AMIA Symp. 2000;:655-9
references.pmid: 11079965
references.citation: Nat Methods. 2011 Jun;8(6):443-4
references.pmid: 21623347
references.citation: J Am Med Inform Assoc. 2010 May-Jun;17(3):229-36
references.pmid: 20442139
references.citation: J Biomed Discov Collab. 2006 Mar 14;1:4
references.pmid: 16722582