Category Archives: Research Project

Research (unsponsored) projects at WSL.

The Cogno – Web Observatory: Characterize Online Social Cognition

It is important to occasionally remember that the World Wide Web (WWW) is the largest information network the world has ever seen. Just about every sphere of human activity has been altered in some way, due to the web. Our understanding of the web has been evolving over the past few decades ever since it was born. In its early days, the web was
seen just as an unstructured hypertext document collection. However, over time, we have come to model the web as a global, participatory, socio-cognitive space. One of the consequences of modeling the web as a space rather than as a tool, is the emergence of the concept of Web observatories. These are application programs that are meant to observe and curate data about online phenomena. This paper details the design of a Web observatory called Cogno, that is meant to observe online social cognition. Social cognition refers to the way social discourses lead to the formation of collective worldviews. As part of the design of Cogno, we also propose a computational model for characterizing social cognition. Social media is modeled as a “marketplace of opinions” where different opinions come together to form “narratives” that not only drive the discourse, but may also bring some form of returns to the opinion holders. The problem of characterizing social cognition is defined as breaking down a social discourse into its constituent narratives, and for each narrative, its key opinions, and the key people driving the narrative.

  • Demonstration:
  • Current Project Members
  • Previous Project Members
  • Publications
    • Raksha Pavagada Subbanarasimha. 2019. Designing the Cogno-Web Observatory: To Characterize the Dynamics of Online Social Cognition. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining (WSDM ’19). ACM, New York, NY, USA, 814-815. DOI: https://doi.org/10.1145/3289600.3291600.
    • Srinivasa S., Pavagada Subbanarasimha R. (2018) Design of the Cogno Web Observatory for Characterizing Online Social Cognition. In: Anirban Mondal, Himanshu Gupta, Jaideep Srivastava, P.Krishna Reddy, D.V.L.N. Somayajulu. (eds) Big Data Analytics. BDA 2018. Lecture Notes in Computer Science. Springer, Cham. (To appear)
    • Raksha Pavagada Subbanarasimha, Lokesh Todwal, Mamillapalli Rachana, Aditya Naidu, and Srinath Srinivasa. 2018. Mithya: A Framework For Identifying Opinion Drivers On Social Media. Demo at ACM IKDD Conference on Data Science and International Conference on Management of Data, Goa, India, Jan 2018 (CODS-COMAD 2018).
    • Anish Bhanushali, Raksha Pavagada Subbanarasimha, and Srinath Srinivasa. 2017. Identifying Opinion Drivers on Social Media. In On the Move to Meaningful Internet Systems. OTM 2017 Conferences: Confederated International Conferences: CoopIS, C&TC, and ODBASE 2017, Rhodes, Greece, October 23-27, 2017, Proceedings, Part II. Springer International Publishing, Cham, 242–253.

Tweet Summarization

The project involves extracting and collating important information from large volume of  short reports.

  •  Characterization of important entities and actions
  • Mine and associate semantics into entities and actions
  • Semi-automate the summary generation process by generating a set of candidate sentences
  • Based on key entities and key actions of interest
  •  User feedback to refine the sentences

SANDESH

 

Sandesh is Semantic Data Mesh for publishing of Knowledge aggregated from Indian Open Data. Open structured data is published by several agencies like World Health Organization (WHO), United Nations Organization (UNO), private firms, NGOs, governmental bodies etc. Government of India publishes open data on its data portal called data.gov.in. To aggregate and integrate data from disparate datasets,  a framework called Many Worlds on a Frame (MWF) is proposed. The framework is partially implemented in software called RootSet on top of which, the module Sandesh is implemented.

Inferencing in the Large: Semantic Integration of Open-Data Tables

Inferencing in the Large (ITL) is a research problem encompassing knowledge extraction, knowledge organisation and knowledge retrieval from open structured data, especially from the Indian Open Government Data.

With vast amounts of tabular data freely available under several Open-Data initiatives, consumption of information depends upon the perspectives of the consumer. These perspectives can be viewed as various contexts the data can be placed in and analysed. Extraction and Organisation of these contexts are non-trivial and we address the problem using semantic integration of open structured data. A collection of open datasets can map to similar contexts (themes) and a single table can map to different themes. ITL presents a model that semantically integrates and aggregates open data in a data mesh of applicable inter-related contexts. Sandesh 1.0 are Sandesh-RDF (v 2.0) are implementations of ITL using open government data from the Indian Open Government Data portal. We use the Linked Open Data (LOD) to associate semantics to the data. The MWF (Many Worlds on a Frame) knowledge framework has been implemented using RDF N-Quads to represent the knowledge extraction in Sandesh-RDF (v 2.0). Sandesh-RDF queries the knowledge graph created from the N-Quads which is the semantic representation of data from data.gov.in. The previous version of Sandesh used the default SQLlite implementation of the MWF framework.