This document discusses research on applying text mining and information retrieval techniques for fact finding in regulatory investigations from electronic documents. The researchers are developing methods for semantic search in e-discovery to iteratively retrieve relevant evidence from emails, forums, and other sources by integrating structural context and extracting knowledge from unstructured text. Their current work includes using Twitter mining as a form of conversational search and entity linking to semantically enrich documents.

1. Semantic Search in E-Discovery
Research on the application of text mining and information retrieval
for fact finding in regulatory investigations
David Graus

2. Who’s Involved?
Prof. dr. Maarten de Rijke Dr. Hans Henseler
Lector E-Discovery, CREATE-IT applied
Director Intelligent Systems Lab, UvA research
David Graus, MSc. David van Dijk, MSc.
PhD Candidate, Semantic Search Researcher E-Discovery, CREATE-IT
applied research
in E-Discovery, UvA
Zhaochun Ren, MSc. Menno Israël, MSc.
Teamleader Knowledge and Expertise
PhD Candidate, Semantic Search Centre for Intelligent Data Analysis
in E-Discovery, UvA (Kecida), NFI
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3. Introduction
£ Semantic Search in E-Discovery
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4. What is
£ Semantic Search in E-Discovery
˜ retrieving and securing digital forensic evidence
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5. What is
£ Semantic Search in E-Discovery
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6. What is
£ Semantic Search in E-Discovery
˜ retrieving and securing digital forensic evidence
˜ from emails, forums, etc...
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7. What is
£ Semantic Search in e-Discovery
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8. Challenge
¢ Finding out who knew what, from whom, and when
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9. Challenge
¢ Finding out who knew what, from whom, and when
¢ Generic search is not the answer
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10. Finding evidence for E-Discovery
¢ We don’t know what we’re looking for
¢ What we’re looking for might be deliberately hidden
¢ Communication might be very domain-specific,
contextualized or incomplete
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11. Task
¢ Retrieve all relevant traces
¢ Highly iterative search process
¢ Support (re)formulating questions and hypotheses
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12. How do we approach this?
¢ Two subprojects:
£ Information Retrieval
˜ Finding material of unstructured nature from large collections
£ Information Extraction/Text Mining
˜ Discovering patterns in data
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13. How do we approach this?
¢ Information Retrieval
£ Integrating structure/context of data in retrieval models
˜ Capturing forum and email context
˜ Conversational search
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14. How do we approach this?
¢ Information Extraction/Text Mining
£ Extracting structured knowledge from user generated
content
˜ Semantic pre-processing
˜ Social network inference
˜ Information maps
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15. How do we approach this?
¢ Information Retrieval <-> Information Extraction
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16. Current work (first steps)
¢ Information Retrieval
£ Twitter Mining (as a form of conversational search)
¢ Information Extraction/Text Mining
£ Entity linking (for semantic document enrichment)
¢ TREC/TAC benchmarking events
£ TREC Legal Track 2011 (2013?)
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17. Contributions
¢ xTAS: Open source text analysis toolkit
¢ iColumbo: Internet monitoring framework
¢ Used by:
£ Internet Recherche Netwerk
£ Koninklijke Bibliotheek
£ Beeld en Geluid
£ ... You?
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18. Semantic search in E-discovery
¢ David Graus
¢ d.p.graus@uva.nl
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