Capture User Interest from Social Media using Semantic Web
Today more than 3 billion people are using social media and using it as a medium to express their real feelings which makes different social media platform like Facebook, Twitter etc. an ideal source for capturing interest of users. Obviously, data mined from social media alone cannot be used to achieve target i.e. predict user's Interest, it needs some form of supervision.
Our talk propose how Semantic web a.k.a Knowledge bases add supervision into system and can prove helpful to predict user's Interest given social media data. Once, User's Interest is captured, it can be widely used for many purposes like Recommendation system, campaigning, analytics over user interests etc.
Keywords: Knowledge systems, linked data, OpenIE, NLP, Semantic Web, User Interest, SPARQL.
Outline/Structure of the Talk
- Problem Definition
- Proposed Solution
- Candidate will get understanding of Semantic Web/ Knowledge bases (DBpedia) / Linked data
- How we can make benefit of free text
- How to capture user Interest from user's social media posts
- Concepts related to Semantic Web like RDF, SPARQL
Data Scientists, Data Engineers, Data Science Enthusiasts, NLP Engineer
Prerequisites for Attendees
- Interest in Information Retrieval Techniques, NLP
- Excited to understand how OpenIE can address real time big problems
- Have used any Social Media platform (Twitter, Facebook)
schedule Submitted 7 months ago
People who liked this proposal, also liked:
Venkatraman J - Entity Co-occurence and Entity Reputation scoring from Unstructured data using Semantic Knowledge graphVenkatraman JSr. data Software engineerMetapack
schedule 8 months agoSold Out!
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.
Aakash Goel / Ankit Kalra - Detect Workout Pose for Virtual Gym using CNNAakash GoelData ScientistFractal AnalyticsAnkit KalraData Scientistfractal
schedule 7 months agoSold Out!
Approximately 80% of the people across globe do not use gym, yet they pay $30 to $125/month.Attrition from gym is linked with discouraging results and lack of engagement. Traditional gym users don’t know proper exercise regimen and users prefer workout regimens that are fun, customizable and social.
To combat above problem, we came up with idea to provide customized fitness solutions using Artificial Intelligence. In this talk, we showcase how we can leverage Deep Learning based Architectures like CNN to develop "Workout pose detection" that tracks user movement and classify it corresponding to specific trained workout and will determine whether the performed pose is correct or wrong.
Keywords: CNN, Deep Learning, Image classification Model, Computer Vision.