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Semantic e-Science pp 309–345Cite as

Towards Knowledge-Based Life Science Publication Repositories

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Part of the book series: Annals of Information Systems ((AOIS,volume 11))

Abstract

Despite being a flourishing field, the contemporary online scientific publishing properly exploits mostly raw publication data (rather meaningless bags of words) and shallow meta-data (authors, keywords, citations, etc.) regarding search. The much needed economical mass exploitation of the knowledge implicitly contained in publication texts is still largely an uncharted territory. The way towards filling this gap leads through (1) extraction of asserted publication meta-data together with the knowledge implicitly present in the respective text; (2) integration, refinement and extension of the emergent content; (3) release of the processed content via a meaning-sensitive search&browse interface catering for services complementary to the current full-text search. This chapter addresses the scientific and engineering challenges related to the suggested approach and introduces a particular solution that tackles them – CORAAL, a prototype for knowledge-based life science publication search.

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Notes

  1. 1.

    CORAAL stands for COntent extended by emeRgent and Asserted Annotations of Linked publication data.

  2. 2.

    ACE stands for Addition, Closure, Extension. See Section 9.4.2 for details.

  3. 3.

    Cf. http://www.simile-widgets.org/exhibit/. Details on how to use the CORAAL user interface are given in Section 9.5.

  4. 4.

    Note that without loss of generality, URIs may serve as concept indices in the statements. Consequently, \(\textrm{ind}^{-1}\) de facto plays a role of the URI dereference. To facilitate readability, we provide simply lexical terms instead of indices or URIs in the examples throughout the chapter, though.

  5. 5.

    Defined in [28] as \(F(a_1, \dots,a_n) = \sum_{j=1}^n w_jb_j\), where b j is the jth largest of the a i and w j are a collection of weights (also called a weight vector) such that \(w_j \in [0,1]\) and \(\sum_{j=1}^n w_j = 1\). Note that we use the additional u, v weights in order to explicitly capture the relative relevance of the \(\varDelta_{u,v}\) first and second argument independently from their relative sizes.

  6. 6.

    Essentially only one value w fully dependent on \(u, v, x, y\) is to be derived, since the remaining element of the OWA weight vector of size 2 is equal to \(1-w\).

  7. 7.

    The duality w.r.t. the distance is ensured by the conformance to two intuitive conditions – inverse proportionality and equality to 1 when the distance is 0.

  8. 8.

    By iterating through a respective knowledge base and/or by similar concept retrieval.

  9. 9.

    Computed as \(|\mathbf{A}| = |{(i,j)|a_{i,\,\,j} \ne 0}|\) for a concept A.

  10. 10.

    Very large means hundreds or thousands of concepts and millions of respective statements, or more.

  11. 11.

    Each of the journals was associated with a specific context identifier to maintain the sub-domain provenance of the respective extracted information and reflect it later on in the CORAAL user interface.

  12. 12.

    The NCI and EMTREE thesauri – see http://www.cancer.gov/cancertopics/terminologyresources and http://www.embase.com/emtree/, respectively.

  13. 13.

    These results were achieved on a single server machine (which is not exclusively dedicated to CORAAL). There are still reserves regarding scalability even with the current implementation; however, for processing data two and more orders of magnitude larger, a distributed solution would be much better.

  14. 14.

    See http://www.elseviergrandchallenge.com/.

  15. 15.

    See http://salt.semanticauthoring.org/onto/. An extracted RDF file example is given at http://resources.smile.deri.ie/coraal/2008/11/ee7c3ec2536e6754ad424c9f95a0d8dce7059a4e.rdf.

  16. 16.

    The heuristics is quite similar to the technique described in [39]. We use the Python NLTK library for NLP (see http://nltk.sourceforge.net). We also experimented with state-of-the-art ontology learning solutions (such as Text2Onto, see http://ontoware.org/projects/text2onto/). The respective tools performed rather poorly in larger scale, though, while providing not that significant improvement in quality when compared to our simple approach. However, we do plan to include more sophisticated as well as domain-specific methods of knowledge extraction (cf. [4, 7, 40]) into our light-weight implementation at some stage.

  17. 17.

    \(^{*}, ^+\) and ? mean zero or more, one or more and zero or one repetitions of the preceding expression, respectively.

  18. 18.

    We exclude the by far most common is a predicate from the set considered for the \(f_P(t)\) computation (the value of \(\nu_1f_P(t_{\textrm{p}})\) was set to 0.8 for the is a statements). We also do not include the statements with \(f_P(t_{\textrm{p}}) = 1\) at all. Note that \(f(t_{\textrm s})f_D(t_{\textrm s}), \; f(t_{\textrm o})f_D(t_{\textrm o})\) are relevance scores of the particular s, o terms, respectively.

  19. 19.

    See http://en.wikipedia.org/wiki/SHA_hash_functions.

  20. 20.

    We use the Sesame repository (see http://www.openrdf.org/).

  21. 21.

    A comprehensive Java search engine library (see http://lucene.apache.org).

  22. 22.

    Modulo mapping the terms to indices and neglecting the infinite number of columns and rows with zero-only elements. We consider not a as a negation of is a.

  23. 23.

    See http://www.embase.com/emtree/ and http://www.cancer.gov/cancertopics/terminologyresources, respectively. EMTREE terms and relations were used in case of conflicts, since they cover more general domain. Synonyms defined in the thesauri were reflected in the lexicon data structure accordingly.

  24. 24.

    Note that the pipeline can be executed even as (ACE)+, i.e. as a search for a global fixed point of the respective operations; however, for the CORAAL prototype we employed only single iteration, since the results were already sufficient for the presented proof-of-concept.

  25. 25.

    Detailed Lucene syntax description can be found at http://lucene.apache.org/java/2_3_2/queryparsersyntax.html. Note that even though the meaning of the AND, NOT keywords is intuitively similar for both types of search in CORAAL, the knowledge and full-text variants are based on completely different principles. For instance, NOT indicates documents not containing the query expression for the full-text search, while in the knowledge search, it leads to documents containing a negation of the respective query statement; similarly for the AND keyword.

  26. 26.

    Note that you can watch a video comprehensively illustrating the essential CORAAL capabilities at http://resources.smile.deri.ie/coraal/videos/coraal_web.mp4 before starting to play with the tool itself.

  27. 27.

    Note that the HAS PART relation has rather general semantics in the knowledge extracted by CORAAL, i.e. its meaning is not strictly mereological in the physical sense, it can refer also to, e.g. conceptual parts or possession of entities. Similarly for the PART OF relation.

  28. 28.

    For instance, the users were asked to find all authors who support the fact that the acute granulocytic leukemia and T-cell leukemia concepts are disjoint, or to find which process is used as a complementary method, while being different from the polymerase chain reaction, and identify publications that support their findings.

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Acknowledgments

This work has been supported by the EU IST 6th framework’s project “Nepomuk” (FP6-027705) and the “Líon” and “Líon II” projects funded by Science Foundation Ireland under Grant No. SFI/02/CE1/I131, SFI/08/CE/ I1380, respectively. We would like to thank the employees of Masaryk Oncology Institute for their feedback and to Ioana Hulpus for her work on the former CORAAL user interface. Very special thanks goes to the people who have actively participated in the continuous prototype evaluation and testing, namely to (in alphabetical order) Doug Foxvog, Peter Gréll, MD, Miloš Holánek, MD, Matthias Samwald, Holger Stenzhorn and Jiří Vyskočil, MD. We also acknowledge the valuable comments from the anonymous reviewers who helped to improve the final shape of the chapter.

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  1. Digital Enterprise Research Institute (DERI), National University of Ireland, Galway, Ireland

    Vít Nováček, Tudor Groza & Siegfried Handschuh

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  1. Vít Nováček
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Correspondence to Vít Nováček .

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Editors and Affiliations

  1. College of Computer Science, Zhejiang University, Zheda Road 38, Hangzhou, 310058, China, People's Republic

    Huajun Chen

  2. Drug Discovery Pte Ltd., Lilly Singapore Centre for, Biomedical Grove 8A, Singapore, 138648, Singapore

    Yimin Wang

  3. Center for Medical Informatics, Yale University School of Medicine, Cedar St. 333, New Haven, 06520-8009, Connecticut, USA

    Kei-Hoi Cheung

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Nováček, V., Groza, T., Handschuh, S. (2010). Towards Knowledge-Based Life Science Publication Repositories. In: Chen, H., Wang, Y., Cheung, KH. (eds) Semantic e-Science. Annals of Information Systems, vol 11. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-5908-9_9

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