Presented by Professor Srinath Srinivasa

1. Utilitarian Aggregation of Open Data
Srinath Srinivasa, Sweety Agrawal
Chinmay Jog, Jayati Deshmukh
IIIT Bangalore

2. OSL@IIIT­B
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Started in 2002
Current strength: 3 PhD students, 5 MS (by Research) students, 24 MTech project
students
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Research Graduates: 4 PhDs, 1 MS
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Part of the Intel PlanetLab grid between 2003—2006
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Broad research areas: data, systems and cognition
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Specific research areas over the years
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Data models for graph databases
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Distributed query processing
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Data management in ad hoc networks
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Semantics mining from text data
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Analytics of online social spaces
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Community knowledge management
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Linked Open Data
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Computational Cognition
Current focus areas

3. OSL Releases
Agama: A graph database for
storing large undirected graphs
for efficient traversal (not
structure­based retrieval)
Currently Agama powers a co­
occurrence graph of all noun­
phrases from Wikipedia articles
hosted in OSL, managing 10s of
millions of nodes and 100s of
millions of edges

4. OSL Releases
Topical Anchors: Given
a list of noun phrases,
identify a semantic
topic for these terms.
Powered by Wikipedia
co­occurrence graph
hosted by Agama
Web APIs enable use of
Topical Anchors in
third party applications

5. OSL Releases
Topic Expansion: Given a
term, expands it into
semantically relevant topical
clusters with different
senses.
Uses co-occurrence
datasets from Wikipedia
2006 or 2011.
Web APIs enable use by
third party applications

6. OSL Releases
Silverfish: A social space for
managing and discussing
research papers
Supports automatic
indexing, recommendations
and social networking
features

7. Utilitarian Aggregation of Open Data

8. Open Data
Data hosted publicly for use and re­
publication with a free or open license
Usually comprising of structured
datasets in the form of tables
Major government, NGO and corporate
players in the open data space

9. Open Data in India: A Summary [Agrawal et al. 2013]

10. Sandesh
A “semantic data mesh” over Indian Open Data
Connecting elements from different datasets under an
overarching semantic structure
Challenges
Open data about no single topic in particular, fits into no
single ontology
Contextual boundaries of open data assertions unable to
model using LinkedData standards
The problem of “open­ended” data

11. Challenges in Open Data Aggregation
Fragmentation

12. Challenges in Open Data Aggregation
Bounded validity of utilitarian data
Consider the following RDF statements:
( Einstein , HasWon , NobelPrize)
(Wheat , PricePerKilo , 50)
Encyclopedic knowledge
Utilitarian knowledge
Valid everywhere without contextual boundaries
Valid only within specific
contextual boundaries (market,
place, time, etc.)
No immediate or specific utility
Has immediate and/or specific
utility

13. Challenges in Open Data Aggregation
The “divergent” nature of utilitarian aggregation
The “convergent” nature of encyclopedic aggregation like Wikipedia articles

14. Challenges in Open Data Aggregation
The “divergent” nature of utilitarian aggregation
Utilitarian
aggregation
involves creation
of several “utility
worlds” each of
which combine a
given data with
different other
data sets for
different
utilitarian goals.

15. Challenges in Open Data Aggregation
Open Data and Credibility
Open data portals hosting utilitarian data (Ex: Data.gov.in) requires
credential checks from data sources for establishing trust, which is
not so critical for open data portals hosting encyclopedic data (Ex:
Wikipedia).

16. Challenges in Open Data Aggregation
The problem of “open­ended” data
Data containing private information about entity p, but which may need
to be (legitimately) disseminated and used by several entities unrelated
to p
Owner (p) of data may not have knowledge or control over consumers of
data; but trusts the system to disseminate this data to legitimate
consumers
Example case studies:
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ICSE marks data
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BPL data

17. Many Worlds on a Frame (MWF)
A trusted, distributed middleware for utilitarian aggregation
and dissemination of open data
Users
as
knowledge
elements
Users
MWF
Datasets
Formal model of MWF developed independently, but representable as a
superposition of two Frames in Kripke Semantics
Aggregated
knowledge in
utilitarian
“worlds”

18. MWF: Conceptual World
Place
Person
Conceptual World: A semantic context to host data about something
Institution
Crop

19. City
is-a
is-in
is-a
is-in
State
is-a
is-in
MWF: Frame
Place
Every conceptual world has a “type” and a
“location” specified by an “is­a” parent and “is­in”
parent respectively. The data structure formed by
is­a and is­in connections is called the Frame

20. MWF: World Structure and Participation
Institution
Person
Office
Location
Member
Components
Member
Member
Associations
Heads
Office
Location
ReportsTo
Member
Place

21. MWF: Privileges
Institution
User ::
Person
Credentials of a Person (User) defined
by the roles played by the Person in
different worlds
Administrator
Schema Manager
Data owner
Casual user
Public
Credentials determine privilege level in
a target world

22. MWF: Inheritances
Place
City
is-a
is-in
State
is­a hierarchy inherits:
● World structure
● Attributes
● Participations
● Constraints
is­in hierarchy inherits:
● Privilege levels
● Visibility
● Construction
● Destruction

23. MWF: Other Features
(ongoing work)
Constraints
Uniqueness constraints
Dual Associations
Bulk loading of data
Cognitive gap­fillers
Query semantics
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Select­in
Answer a query by matching query condition inside a world and its contained worlds
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Select­on
Answer a query by matching query condition on a set of worlds of a given type
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Select­world
Answer a query about the participation of a given world in other worlds

24. MWF: Future Work
Distributed MWF with proxy worlds
From privileges and constraints to an
integrity management subsystem

25. Some Screenshots

26. Some Screenshots

27. Some Screenshots

28. Some Screenshots

29. Thank You!

30. References
[Agrawal et al, 2013] Sweety Agrawal, Jayati Deshmukh, Srinath Srinivasa, Chinmay Jog, Sri
Sayi Bhavani Kakarla, Rahul Dhek, Sneha Deshpande, Sana Javed and Vikas Mohandoss. A
Survey of Indian Open Data. Proceedings of IBM ICARE 2013. ACM Press. New Delhi, India. Oct
2013
[Srinivasa et al, 2014] Srinath Srinvasa, Sweety Agrawal, Chinmay Jog, Jayati Deshmukh.
Characterizing Open Utilitarian Knowledge. Proceedings of CoDS 2014, New Delhi, India, March
2014.