subsift web services and workflows for profiling and comparing scientists and their published works

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

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Outline of this paper

1. SubSift – submission sifting

2. Background Theory: Vector Space

Model

3. SubSift REST API

4. Demonstration Workflows

5. Conclusions

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2. Background Theory

3. SubSift REST API


5. Conclusions

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SubSiftSubSift 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

http://subsift.ilrt.bris.ac.uk/

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SubSift has been used for...15

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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.

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 UltraSearchDesigned 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.

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2. Background Theory: Vector Space Model



3. SubSift REST API


5. Conclusions

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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.

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Vector Space Model: bag-of-words representation

no. terms in each abstract

no. terms in DBLP author page of each PC member

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Vector Space Model: cosine similarity

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Vector Space Model: tf-idf weighting

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Representational State Transfer (REST)

“RESTful” web services:• URIs to represent resources

• HTTP POST/GET/PUT/DELETE correspond to usualCreate/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.

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3. SubSift REST API



3. SubSift REST API


5. Conclusions

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SubSift System Archicture

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SubSift REST API

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Profiles

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Matches

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SubSift – canonical workflow

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3. SubSift REST API


5. Conclusions

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Workflow 1 – Submission Sifting

Workflow 1 – Web 2.0 Client Implementation

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Workflow 1 – Papers is just a list of URLs (e.g. Yahoo! Pipes)

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Workflow 2 – Finding an Expert

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Finding an expert

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Workflow 3 – Visualising Similarity

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Clustering staff based on homepage similarity

Dendrogram produced in Matlab from SubSift generated similarity matrix

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Precision-recall at different thresholds

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Similarity networks

Diagram created by Graphvis from SubSift generated dot file

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Connectivity

Diagram created by Graphvis from SubSift generated dot file

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Workflow 4 – Profiling Reading Lists

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Profiling a research group by its publications

Diagram produced in Wordle using SubSift profile data

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Workflow 5 – Ranking News Stories

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And finally...

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.

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5. Conclusions



3. SubSift REST API


5. Conclusions

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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.

subsift web services and workflows for profiling and comparing scientists and their published works

Data & Analytics