Semantic Journal Mapping for Search Visualization in a Large Scale Article Digital Library
•
3 j'aime•713 vues
This document summarizes research on using semantic vectors to map journals in a large digital article library onto a semantic vector space. Key points: 1) The researchers used semantic vectors to map over 2,000 journals from a corpus of over 5 million articles onto a 2D semantic space, generating a visualization of journal relationships. 2) They were able to scale this approach to the large corpus size, with the semantic vector index building in under an hour. 3) The resulting visualization grouped journals by discipline reasonably well and identified some cataloging errors, showing potential for interactive discovery tools. 4) Future work proposed exploring precision/recall, non-metric multidimensional scaling, and projecting articles onto the
Recommandé
Recommandé
Contenu connexe
Similaire à Semantic Journal Mapping for Search Visualization in a Large Scale Article Digital Library
Similaire à Semantic Journal Mapping for Search Visualization in a Large Scale Article Digital Library (20)
Dernier
Dernier (20)
Semantic Journal Mapping for Search Visualization in a Large Scale Article Digital Library
- 1. Semantic Journal Mapping for Search Visualization in a Large Scale Article Digital Library Glen Newton1,2, Alison Callahan1, Michel Dumontier2 1 National Research Council Canada, 2Carleton University Second Workshop on Very Large Digital Libraries (VLDL) 2009 at ECDL 2009 Oct 2 2009 Corfu, Greece
- 2. Outline • Maps of Science • Background • Research Interests • Research Goals • Process • Scalability issues • Environment • Results • Conclusions • Future Work
- 3. From Bollen et al 2009 PLOS1
- 4. From Leydesdorff From Leydesdorff & Rafols 2006 & Rafols 2006
- 5. From Leydesdorff & Rafols 2006
- 6. Background • Canada Institute of Science and Technical Information (CISTI) == Canadian national science library • ~3000 active researchers at NRC • Large full text collection of ~8.4m full-text + metadata articles, in science, technology, medicine (STM) • 4100 journal titles • ~1995 to 2009
- 7. Research Interests • Domain-specific discovery • Improved discovery in STM domains through results visualization and contextualization, browse/explore/refine • Results set visualization: “mapping”
- 8. Research Goals • Find way to extract journal (& article) semantic vector space • Latent Semantic Analysis (LSA) works for small/medium sized corpora, does not scale to large scale of items and/or terms • New alternative: Semantic Vectors (SV): uses random vectors & avoids expensive singular value decomposition (SVD) • Can SV scale & generate sensible semantic vector space of journals on corpus of this size? • Can the visualization produced be useful for results query visualization, refinement, discovery?
- 9. Corpus • Licensed journal articles from STM publishers: Elsevier, Springer, etc • ~4100 journal titles, classified into 23 categories (by librarians) • ~8.4m journal articles • Selection of articles/journals: – Only those with authors, abstract (no notices, obituaries, etc) – Only English language articles – Only journals with >50 articles in corpus – Resulting corpus: 5,733,721 articles from 2231 journals – Categories overlapping: 1.53 categories per journal
- 10. Category # Journals per category Agriculture & Biological Sciences 358 Arts and Humanities 70 Biochemistry, Genetics and Molecular Biology 240 Business, Management and Accounting 106 Chemical Engineering 126 Chemistry 226 Civil Engineering 64 Computer Science 218 Decision Science 50 Earth and Planetary Science 146 Economics, Econometrics and Finance 112
- 11. Category # Journals per category Energy and Power 73 Engineering and Technology 328 Environmental Science 138 Immunology and Microbiology 104 Materials Science 160 Mathematics 205 Medicine 671 Neuroscience 103 Pharmacology, Toxicology and 73 Pharmaceutics Physics and Astronomy 210 Psychology 126 Social Science 222
- 12. Process • Index full-text (only) with Lucene 2.4, aggressive stopword list, Porter stemming using LuSql tool • Build Semantic Vectors (v1.18, parallelized) index from Lucene index, with 512 semantic dimensions • Find item x item distance matrix from SV index of 512- dimensional vectors • Using R, use multidimensional scaling (MDS) to reduce from 512- D to 2-D
- 13. Scalability Issues • #items, #unique terms – #unique terms: SV easily handles very well – #items: SV handles fairly well – #items: impacts size of distance matrix (#items x #items) – R cannot handle huge article distance matrix in MDS (i.e. millions of articles vs. thousands of journals) • Instead of using articles for items, use journals for items • Make single large full-text document from concatenation of all articles of particular journal & index these
- 14. Environment • Dell PowerEdge 1955 Blade server, 2 x dual-core Xeon 5050 processors with 2x2MB cache, 3.0 Ghz 64bit, 32GB RAM, attached to a Dell EMC AX150 storage arrays via SilkWorm 200E Series 16-Port Capable 4Gb Fabric Switch. • Operating system: Linux openSUSE 10.2 (64-bit X86-64), kernel 2.6.18.8-0.10-default #1 SMP • Java version 1.6.0.07 (build 1.6.0 07-b06) Java HotSpot 64-Bit Server VM (build 10.0-b23, mixed mode). • Processing 1.0 (processing.org)
- 15. Results: Scalability • Corpus: ~600GB full-text • Lucene index: 43GB – LuSql: 13 hours 51 minutes to produce • SV index: 58 minutes, 885 MB, 21.6m terms – Distance matrix: 6 minutes
- 16. Results: Visualization • Using Processing environment, built simple validation/visualization tool
- 19. Harder sciences and engineering categories
- 20. Chemistry
- 21. Material Science
- 24. Mathematics
- 25. Computer Science
- 30. Biochemistry, Genetics and Molecular Biology
- 32. Pharmacology
- 33. Neuroscience
- 35. Psychology
- 39. Energy and Power
- 40. Decision Science
- 42. Social Sciences
- 45. Examination of outliers, extrema and cataloging errors
- 46. Ecotoxicology and Environmental Safety Organic Geochemistry Corporate Environmental Strategy Environmental Science
- 47. Journal of Biomolecular NMR Journal of X-Ray Science and Technology Medicine Medicine
- 48. Colloidal and Polymer Science Annales Henri Poincare Medicine Medicine
- 49. Medicine Medicine French language Medical & Psychology Journals
- 50. Bulletin of Mathematical Biology Journal of Medical Ultrasonics Mathematics
- 51. Conclusions • Reasonable mapping results • Full-text only (no citations, metadata) gives good results • Scalable to significant size
- 52. Future Work • Proper precision and recall evaluation using same corpus • Validate with NetNews-20 collection for P & R • Evaluate non-metric MDS • Project articles onto semantic journal space & build interactive discovery interface & evaluate – Index journal 'documents' and journal articles – SV on all – Distance matrix only on journals – Do MDS – Use eigenvectors to transform N-d article vector to 2-D • Explore 3-D interface (MDS N-d → 3D)
- 53. Acknowledgements • Greg Kresko, Andre Vellino, Jeff Demaine @ NRC-CISTI
- 54. Demo • Link to project demo page
- 55. License Creative Commons Attribution-Noncommercial-No Derivative Works 2.5