SubSift web services and workflows for profiling and comparing scientists and their published works
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subsift is a prototype application designed to assist in academic peer review by matching submitted papers with potential reviewers based on published works. The paper discusses the underlying vector space model, the subsift REST API, and various workflows demonstrating its capabilities, such as expert finding and visualizing similarities among authors. Future plans include scaling the application and expanding its use to map research landscapes across universities.
SubSift web services and workflows for profiling and comparing scientists and their published works
- 1. SubSift web services and workflows for profiling and comparing scientists and their published works Simon Price, Peter Flach, Sebastian Spiegler, Christopher Bailey and Nikki Rogers
- 2. 2 Outline of this paper 1. SubSift – submission sifting 2. Background Theory: Vector Space Model 3. SubSift REST API 4. Demonstration Workflows 5. Conclusions
- 3. 3 1. SubSift – submission sifting 1. SubSift – submission sifting 2. Background Theory 3. SubSift REST API 4. Demonstration Workflows 5. Conclusions
- 4. 4 SubSift SubSift is a prototype application to support academic peer review. SubSift matches submitted conference/journal papers to potential peer reviewers based on similarity to published works. Website: http://subsift.ilrt.bris.ac.uk
- 5. 5 SubSift has been used for... 15
- 6. 6 Contribution of this work SubSift RESTful web services: • Open Source software (on Google Code) • Hosted open web service at University of Bristol Re-usable workflows for profiling and comparing scientists and their published works. Tool for constructing, manipulating and publishing document-centric datasets.
- 7. Related Work • SubSift uses techniques more normally associated with Information Retrieval • Full text search tools support text matching on large-scale document collections e.g. Apache Lucene, PostgreSQL, Oracle UltraSearch Designed for 1:M matching but can also to do Cartesian product M:M matching. • How SubSift differs: • Exposes detailed metadata throughout. • Partly a research tool: need to plug in + instrument new algorithms. • Fewer licensing restrictions and dependencies for open source. 7
- 8. 8 2. Background Theory: Vector Space Model 1. SubSift – submission sifting 2. Background Theory 3. SubSift REST API 4. Demonstration Workflows 5. Conclusions
- 9. 9 Vector Space Model (from Information Retrieval) Vector Space Model consists of: • bag-of-words representation • cosine similarity • tf-idf weighting For a query (q), rank the documents (dj) in collection (D) by descending similarity to the query.
- 10. 10 Vector Space Model: bag-of-words representation no. terms in each abstract no. terms in DBLP author page of each PC member
- 11. 11 Vector Space Model: cosine similarity
- 12. 12 Vector Space Model: tf-idf weighting
- 13. 13 Representational State Transfer (REST) “RESTful” web services: • URIs to represent resources • HTTP POST/GET/PUT/DELETE correspond to usual Create/Read/Update/Delete (CRUD) operations • Response formats typically include: XML, JSON, CSV REST is a design pattern for web services based on HTTP using its familiar URIs, requests, responses, authentication, etc.
- 14. 14 3. SubSift REST API 1. SubSift – submission sifting 2. Background Theory 3. SubSift REST API 4. Demonstration Workflows 5. Conclusions
- 15. 15 SubSift System Archicture SUBSI FT REST API XML CSV TermsJSON YAML RDF WEB FILESTORE SUBSIFT HARVESTER XSLT CLIENT
- 17. 17 Profiles
- 18. 18 Matches
- 19. 19 SubSift – canonical workflow
- 20. 20 4. Demonstration Workflows 1. SubSift – submission sifting 2. Background Theory 3. SubSift REST API 4. Demonstration Workflows 5. Conclusions
- 21. 21 Workflow 1 – Submission Sifting
- 22. Workflow 1 – Web 2.0 Client Implementation 22
- 23. Workflow 1 – Papers is just a list of URLs (e.g. Yahoo! Pipes) 23
- 24. 24 Workflow 2 – Finding an Expert
- 26. 26 Workflow 3 –Visualising Similarity
- 27. 27 Clustering staff based on homepage similarity Dendrogram produced in Matlab from SubSift generated similarity matrix
- 28. 28 Precision-recall at different thresholds
- 29. 29 Similarity networks Diagram created by Graphvis from SubSift generated dot file
- 30. 30 Connectivity Diagram created by Graphvis from SubSift generated dot file
- 31. 31 Workflow 4 – Profiling Reading Lists
- 32. 32 Profiling a research group by its publications Diagram produced in Wordle using SubSift profile data
- 33. 33 Workflow 5 – Ranking News Stories
- 35. Future Work • Scaling-up • Currently a small-scale web application running on modest hardware. • Plans to migrate to a larger-scale HPC application at Bristol. • ExaMiner project • Mining and mapping the University of Bristol’s research landscape. • Crawling the University’s web pages to profile and visualise research interests of and similarities between faculty, departments, research groups and researchers. • Plans to apply to websites of other Universities. 35
- 36. 36 5. Conclusions 1. SubSift – submission sifting 2. Background Theory 3. SubSift REST API 4. Demonstration Workflows 5. Conclusions
- 37. 37 Conclusion • SubSift Services useful outside of peer review domain • Workflows for profiling/comparing scientists Promising e-Science and e-Research use cases for profiling and comparing scientists and their published works. • Tool for constructing, manipulating and publishing document-centric datasets E.g. information retrieval, data mining, pattern analysis research. Publication of datasets in this way supports reproducibility of science. Connects data through Linked Data and the Semantic Web.