Entity Co-occurence and Entity Reputation scoring from Unstructured data using Semantic Knowledge graph

schedule Aug 9th 02:45 - 03:05 PM place Jupiter people 94 Interested

Knowledge representation has been a research for many years in AI world and its continuing further too. Once knowledge is represented, reasoning from that extracted knowledge is done by various inferencing techniques. Initial knowledge bases were built using rules from domain experts and different inferencing techniques like Fuzzy inference, Bayesian inference were applied to extract reasoning from those knowledge bases. Semantic networks is another form of knowledge representation which can represent structured data like WordNet, DBpedia which solves problems in a specific domain by storing entities and relations among entities using onotologies.

Knowledge graph is another representation technique deeply researched in academia as well as used by businesses in production to augment search relevancy in information retrieval(Google knowledgegraph), improve recommender systems, semantic search applications and also Question answering problems.In this talk i will illustrate the benefits of semantic knowledge graph, how it differs from Semantic ontologies, different technologies involved in building knowledge graph, how i built one to analyse unstructured (twitter data) to discover hidden relationships from the twitter corpus. I will also show how Knowledge graph is data scientist's tool kit to discover hidden relationships and insights from unstructured data quickly.

In this talk i will show the technology and architecture used to determine entity reputation and entity co-occurence using Knowledge graph.Scoring an entity for reputation is useful in many Natural language processing tasks and applications such as Recommender systems.

 
 

Outline/Structure of the Talk

Outline/Structure of the Talk

Difference between semantic networks and Knowledge graphs(2 mins)

Technologies used in building the knowledge graph like syntactic parsing, information extraction, entity linking, named entity recognition, relationship extraction, semantic parsing, semantic role labelling, entity disambiguation, (5 min)

Architecture used in building knowledge graph and scoring in real time(10 mins)

Challenges involved in building the product(3 mins)

Learning Outcome

Audience will learn how the relations can be extracted from unstructured data as quickly as possible using Knowledge graph.

Target Audience

Any engineer who is interested in mining unstructred data

Prerequisites for Attendees

Basic Graph algorithms, search algorithms

schedule Submitted 9 months ago

Public Feedback

comment Suggest improvements to the Speaker
  • Dr. Vikas Agrawal
    By Dr. Vikas Agrawal  ~  8 months ago
    reply Reply

    Dear Venkat: I like the topic. Could you please describe in detail the specific problem you solved and how you solved it? We would love to see the depth and breadth of the application. Warm Regards, Vikas

    • Venkatraman J
      By Venkatraman J  ~  8 months ago
      reply Reply

      Dear Vikas, 

      Thanks for the comment. We were working on a project to score/weigh an Entity's reputation in a particular domain from tweets. In that research we figured this way of representation can answer more questions on data.Hope its clear now.

  • Anoop Kulkarni
    By Anoop Kulkarni  ~  8 months ago
    reply Reply

    Thanks for your proposal. Looking foward to learning more on the topic. Do you have any demo as part of this? If yes, how would the time breakup be?

    ~anoop

    • Venkatraman J
      By Venkatraman J  ~  8 months ago
      reply Reply

      Thanks Annop for taking your time to review. Yes i am planning to do a demo if i could get it working on my cloud infrastructure. I will update the proposal with my time break up for the same.


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