The document discusses the need for improved information retrieval in enterprise environments, focusing on integrating structured and unstructured data to meet strategic and business development needs. It highlights the inefficiencies caused by incomplete data and the implementation of effective search strategies using various organizational systems. The workshop emphasizes leveraging existing resources like ERP, CMS, and institutional repositories to enhance document representation and facilitate better access to information across different projects and collaborations.

1. May 31st, 2013 First SICSA MMI Information Retrieval Workshop
Looking beyond plain text for
document representation in
the enterprise
Arjen P. de Vries
arjen@acm.org
Centrum Wiskunde & Informatica
Delft University of Technology
Spinque B.V.

2. Outline
 Motivation
 Mixed structured and unstructured
sources
 Search by strategy
 Equip
 Open ends

3. Enterprise Information Needs
Hang Li et al. A new approach to intranet search based on information extraction. CIKM’05

4. Strategic and business
development needs
 What funding schemes are the primary source
of income?
 E.g., can we move to Europe when Dutch funding
dries up?
 Who has active relations with partner X?
 “Valorisation”; new national funding requirements
 What industry sectors do we depend upon?
 E.g., how many projects in smart cities? Green
energy? Cloud computing? Etc.
 How are strategic decisions implemented?
 E.g., has objective “move from Telecom toward ICT”
been achieved, and how does it develop over time?

5. A week in the life

6. Date: Wed, 15 May 2013 15:14:49 +0200
From: Theme Coordinator “INFORMATION”
To: Group Leaders Information Theme
Subject: List of company relations for internal CWI
distribution
Dear Information Theme Group Leaders,
The theme coordinators have been asked whether they: "een
lijstje kan maken met de bedrijfscontacten en daarbij aan te
geven van welke aard de contacten zijn".
Could you send me the names of Dutch companies you are currently
working with or have worked with in the recent past by the end
of Friday 17th May.
The Theme Coordinator

7. Date: Fri, 24 May 2013 11:33:04 +0200
From: Theme Coordinator Life Sciences
To: Group Leaders Life Sciences Team
Subject: Life Sciences: contacts with NL companies?
Dear all,
The CWI themes are currently collecting all contacts we have
with Dutch industry and companies (but also hospitals and TNO
etc.) in order to get an overview. I am doing this for
the theme "Life Sciences".
Can you please send me a list of your contacts with short
description?
Life Sciences Theme Coordinator

8. From: Project Leader Project X
Date: Sun, 26 May 2013 17:34:15 +0200
To: Project X
Subject: [Project X: 33] @WP-leiders
X-BeenThere: Project X @ Y.org
Beste WP-leiders,
Ik kreeg van Het Programma Management het volgende verzoek:
> Mag ik je vragen me een lijstje te sturen van welk EU
onderzoek en welk internationaal onderzoek er loopt bij de
partners gerelateerd aan Project X (internationale inbedding).
Dit is mijn meest urgente punt. Kunnen jullie zsm aan mij sturen
een lijstje met de volgende punten:
- lijst van lopende EU projecten waarbij mensen uit jouw WP
betrokken zijn; geef aub aan wi de partners zijn,
financieringsbron, of het een STREP (of NoE of ...) is, en of
jouw WP een participant of coordinator levert;
- lijst van aangevraagde EU projecten, met zelfde extra's
- lijst van eventuele andere internationale samenwerkingen die
niet door een formeel project zijn afgedekt
Stuur me de lijstjes aub zsm maar niet later dan dinsdag
18u. Bedankt voor jullie hulp. De Projectleider

9. Surely, academia is not like…

10. The High Cost of Not Finding Info
 If you employ 1000 knowledge workers:
 50% of content unindexed  $2.5
million/year
 6.25% of effort is spent reproducing
information that already exists 
$5 million/year
 Knowledge workers spend 15-25% of
their time on non-productive
information-related activities
Feldman and Sherman.
IDC Technical Report #29127, 2003
Butler Group Report: Enterprise Search and Retrieval. Oct-2006
“many organisations are frittering away up to 10% of their staff
costs on wasted effort because employees simply can’t find
the right information to do their jobs.”

11. So… “the real world”
 “Real” companies (as opposed to
academic institutions) attempt to address
these information needs a priori, by
setting up a Customer Relationship
Management system (CRM)
Shan L. Pan and Jae-Nam Lee, "Using e-CRM for a unified view of
the customer", Communications of the ACM 46(4) (2003): 95-99

13. However…
 So-called “Professionals” are well known
to focus on their own expertise
 They do not have (or take) the time to
maintain adequate descriptions of their
network, skills, projects etc. – neither for
most other types of “management
overhead”

14. We only need to organize ourselves!!

15. Funding Proposals
 Proposals submitted (are supposed to)
pass by the faculty’s (TUD) “contract
managers” or the institute’s (CWI)
“project bureau”
 E.g., checks for liability, IPR and valid budget
 Proposal and (partial) metadata are added to
a content management system (CMS)
 The CMS used at my faculty at TUD is DECOS; a
few other faculties plan to use Microsoft
Sharepoint; CWI deploys BSCW

17. Step 1
 Index all the proposals submitted with
your favourite IR system

18. Incompleteness
 The DECOS metadata entered is usually
incomplete from the start
 For many projects for example, only the coordinator
is entered as partner
 Also, a proposal’s metadata does not reflect
subsequent change; e.g., as in PuppyIR:
 People hired after funding secured
 Partner change when key person moved job
 Teams evolved
 Priorities shifted
 New tasks introduced and tasks (re-)assigned
 …

19. Incompleteness
 In general:
 A project’s proposal or even the contract
seldomly represents the project’s exact future

20. Inaccuracy
 Key information necessary for strategy &
business development scenarios missing
 Adding those is error-prone
 Infer domain (big data, green energy, cloud
computing, …) from keywords or content
 Extract names automatically
 Copy amounts manually; inconsistencies in
tables in proposal text are not uncommon

21. Incomplete & inaccurate Data
 Ambiguity
 When describing domain, e.g., cloud
computing vs. clouds in environmental models
 Names of people and companies involved
 Typos & OCR mistakes
 Entity resolution
 Amounts of funding per partner, own
contribution
 Funding request may not equal funding
received

22. The real world to rescue (1)
 Not much work gets done without
payments…

23. ERP
 All large organisations deploy Enterprise
Resource Planning (ERP) systems
 Typical modules include accounting, human
resources, manufacturing, and logistics
 ERP integrates the modules, data
storing/retrieving processes, and
management and analysis functionalities
 Baan, Oracle, PeopleSoft, SAP, …

24. More complete and more
accurate data from ERP
 Financial details of each project as executed
 Project leader
 People who are reimbursed from the project
 Exact duration of project activities
 ...

25. Step 2
 Index all the ERP data with your favourite
IR system
 Link the ERP project identifiers to the CMS
proposal identifiers
 Surprisingly, an n:m relationship…
DB +

26. The real world to rescue (2)

27. Institutional Repository
 Publication metadata helps validate
existing (and may even extend) the
management info required:
 Authors
 Author affiliations
 Projects and funding schemes (from
acknowledgements)?
 Again incomplete data though…
 Especially my faculty notoriously bad at
maintaining their part of the institutional
repository

28. Step 3
 Crawl the Institutional Repository using
the Open Archives Initiative (OAI)
harvesting protocol
 Index all the publications data with your
favourite DB + IR system
 Relate projects to publications by author
name, similar title, etc.

29. Result: Unified Access
 Proposals
 from an XML dump of the CMS
 Actual project administration
 from CSVs extracted from ERP
 Publications
 crawled using OAI, from the IRP

30. Schema

31. Heterogeneous content!
 BAAN-project (ERP)
 Decos-project (CMS)
 Decos-document (CMS attachments)
 Publication (Institutional Repository)
 Publication-document (Institutional Repository PDFs)
 Person (adress lists, ERP + CMS mentions)
 Company (CMS + ERP + document mentions)
 Subsidy (CMS)
 Department (address lists, CMS)
 Web addresses (extracted from documents)
 Topic (assigned to publications)
 Research programme (dependent on funding scheme)

32. Schema V2

33. How to search that graph???!
 Rank (un-/semi-)structured data to deal
with incompleteness & inaccuracies
 Structured data representation for
attributes including project revenu,
people’s names, starting dates, etc.
 Use cases varying from “expert search” to
“data cleaning” and “visual analytics”

34. Search by Strategy
 First, visually construct search strategies
by connecting “building blocks”

35. Search by Strategy
 First, visually construct search strategies
by connecting “building blocks”
 Next, generate the search engine specified
by that search strategy

36. Strategies: DB+IR query plans
 Database
Spinque: RDBMS (MonetDB)
BB1(in1,in2,in3, u1,u2)
in1 in2 in3
out
BB2(in1)
in1
out
• Data flow
Spinque: strategy
• Query: strategy made operational
Spinque: PRA
CREATE VIEW a AS
SELECT ..
CREATE VIEW b AS
SELECT ..
CREATE VIEW c AS
SELECT ..
Strategy
Relational DB

37. Probabilistic Relational Algebra
Strategy
Relational DB
• SQL
explicit probabilities
CREATE VIEW x AS
SELECT a1, a3,
1-prod(1-prob) AS prob
FROM y
GROUP BY a1, a3;
• PRA: probabilistic
relational algebra
(Fuhr and Roelleke,
TOIS 2001)
x = Project DISTINCT
[$1,$3](y);

38. Rank by Text

39. Expert Finding

41. Search User Interface

42. Search results

43. Result List Interactions
 Zoom in on item using “+”:
 Open item in left pane
 Shows results of item as query, using a
result-type specific search strategy
 Goal to provide contextually most related nodes
from underlying graph
 Marking any item red/yellow/green for
later usage

46. Browse by facet

48. Strategic and business
development needs
 What are our industry relations?
 Who of these partners collaborate with
more than one group?
 What funding schemes support these
collaborations?

49. Note: relations between partners and departments, edge strength represents revenue

50. Note: relations between partners and departments, edge strength represents revenue

51. Multi party relations
Grouping of external relations
Foreign
Univ.
NL Univ.
Funding
agency
Public NL
Public
foreign
Private
sector
Multi party relations
Grouping of external relations
Foreign
Univ.
NL Univ.
Funding
agency
Public NL
Public
foreign
Private
sector
Note: External relations with at least two departments; node size w.r.t. number of relations

52. Initial Findings
 The integrated search helps improve
recall, reducing the effort involved and
leading to higher quality analyses
 Many things that could be done even
more automatically (albeit not perfectly)
seem less important than expected
 We use very simple rules to extract URIs and
companies; no information extraction yet
 Information professional will always look into
results in detail

53. Open issues
 Integrate visualization
 Idea: select result list and facet
 Too many facets
 Idea: group facets
 Result explanations
 Idea: describe path through graph
 Entity support ++

54. Open issues
 What strategy is good? Why?
 Idea: test using past usage data
 What are the right user roles?
 Who should do the searches?
 Who should write strategies?
~ who writes the SQL queries in traditional DB?
 Human in the loop for retrieval, but not
yet for indexing…

55. Questions?