Scalable Knowledge Graph Construction from Twitter
This addresses the problem of filtering noise from Twitter for researchers or developers seeking high-quality content insights, but it appears incremental as it applies existing methods to new data without clear novelty claims.
The paper tackled the problem of constructing a knowledge graph from Twitter data to discover relationships between people, links, and topics, resulting in a scalable graph that allows users to query and traverse the structure for new applications.
We describe a knowledge graph derived from Twitter data with the goal of discovering relationships between people, links, and topics. The goal is to filter out noise from Twitter and surface an inside-out view that relies on high quality content. The generated graph contains many relationships where the user can query and traverse the structure from different angles allowing the development of new applications.