LINQS

STATISTICAL RELATIONAL LEARNING GROUP @ UMD



 

Large-Scale Knowledge Graph Identification using PSL

ICML Workshop on Structured Learning (SLG) - 2013
Download the publication : pujara_slg13.pdf [284Ko]  
Building a web-scale knowledge graph, which captures information about entities and the relationships between them, represents a formidable challenge. While many large-scale information extraction systems operate on web corpora, the candidate facts they produce are noisy and incomplete. To remove noise and infer missing information in the knowledge graph, we propose knowledge graph identification: a process of jointly reasoning about the structure of the knowledge graph, utilizing extraction confidences and leveraging ontological information. Scalability is often a challenge when building models in domains with rich structure, but we use probabilistic soft logic (PSL), a recently-introduced probabilistic modeling framework which easily scales to millions of facts. In practice, our method performs joint inference on a real-world dataset containing over 1M facts and 80K ontological constraints in 12 hours and produces a high-precision set of facts for inclusion into a knowledge graph.

BibTex references

@InProceedings{pujara:slg13,
  author       = "Pujara, Jay and Miao, Hui and Getoor, Lise and Cohen, William",
  title        = "Large-Scale Knowledge Graph Identification using PSL",
  booktitle    = "ICML Workshop on Structured Learning (SLG)",
  year         = "2013",
}

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