An Approach For Scalable First-Order Rule Learning On Twitter Data

Scalable Rule Learning (SRLearn) is a scalable divide-and-conquer approach with graph-based modeling of social media data, to scale up first-order rule learning through Markov Logic Networks on a commodity cluster on large scale Twitter data. SRLearn takes advantage of distributed systems to partition large-scale data into smaller but meaningful partitions based on user interaction and incorporates a gradient boosting approach with a tool called BoostSRL for first-order rule mining. We show how this scalable solution on first order predicates is more accurate and efficient than existing systems, such as ProbKB (a scalable system to construct probabilistic knowledge base) and XGBoost (extreme gradient boosting) on relational data.

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Computer Science (UMKC)

Grantor

University of Missouri--Kansas City

Year dc:date.issued

2021

Author dc:creator

• Senapati, Monica

Advisors dc:contributor.advisor

• Rao, Praveen R.
• Choi, Baek-Young

Handle dc:identifier.uri

https://hdl.handle.net/10355/89577

OAI identifier oai:identifier

oai:mospace.umsystem.edu:10355/89577