21 citations · 21 across the 4 of their papers we have counts for
4 papers
ODKE+: Ontology-Guided Open-Domain Knowledge Extraction with LLMs
Samira Khorshidi, Azadeh Nikfarjam, Suprita Shankar +9
Knowledge graphs (KGs) are foundational to many AI applications, but maintaining their freshness and completeness remains costly. We present ODKE+, a production-grade system that a…
APE: Active Learning-based Tooling for Finding Informative Few-shot Examples for LLM-based Entity Matching
Kun Qian, Yisi Sang, Farima Fatahi Bayat +11
Prompt engineering is an iterative procedure often requiring extensive manual effort to formulate suitable instructions for effectively directing large language models (LLMs) in sp…
Open Domain Knowledge Extraction for Knowledge Graphs
Kun Qian, Anton Belyi, Fei Wu +15
The quality of a knowledge graph directly impacts the quality of downstream applications (e.g. the number of answerable questions using the graph). One ongoing challenge when build…
Saga: A Platform for Continuous Construction and Serving of Knowledge At Scale
Ihab F. Ilyas, Theodoros Rekatsinas, Vishnu Konda +3
We introduce Saga, a next-generation knowledge construction and serving platform for powering knowledge-based applications at industrial scale. Saga follows a hybrid batch-incremen…