Utilitarian aggregation of open data

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Published on March 4, 2014

Author: sinindia

Source: slideshare.net


Presented by Professor Srinath Srinivasa

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

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

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 

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 

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

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

Utilitarian Aggregation of Open Data

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

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

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

Challenges in Open Data Aggregation Fragmentation

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

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

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.

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). 

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: ● ICSE marks data ● BPL data

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” 

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

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

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

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

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

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

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

Some Screenshots

Some Screenshots

Some Screenshots

Some Screenshots

Thank You!

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. 

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