KnowledgeNet: A Benchmark for Knowledge Base Population

EMNLP 2019 paper , dataset ,  leaderboard  and code

Knowledge bases (also known as knowledge graphs or ontologies) are valuable resources for developing intelligence applications, including search, question answering, and recommendation systems. However, high-quality knowledge bases still mostly rely on structured data curated by humans. Such reliance on human curation is a major obstacle to the creation of comprehensive, always-up-to-date knowledge bases such as the Diffbot Knowledge Graph .

The problem of automatically augmenting a knowledge base with facts expressed in natural language is known as Knowledge Base Population (KBP). This problem has been extensively studied in the last couple of decades; however, progress has been slow in part because of the lack of benchmark datasets. 

KnowledgeNet is a benchmark dataset for populating Wikidata with facts expressed in natural language on the web. Facts are of the form (subject; property; object), where subject and object are linked to Wikidata. For instance, the dataset contains text expressing the fact ( Gennaro Basile ; RESIDENCE; Moravia ), in the passage:

“Gennaro Basile was an Italian painter, born in Naples but active in the German-speaking countries. He settled at Brunn, in Moravia, and lived about 1756…”

KBP has been mainly evaluated via annual contests promoted by TAC . TAC evaluations are performed manually and are hard to reproduce for new systems . Unlike TAC, KnowledgeNet employs an automated and reproducible way to evaluate KBP systems at any time, rather than once a year. We hope a faster evaluation cycle will accelerate the rate of improvement for KBP.

Please refer to our EMNLP 2019 Paper for details on KnowlegeNet, but here are some takeaways:

• State-of-the-art models (using BERT ) are far from achieving human performance (0.504 vs 0.822).
• The traditional pipeline approach for this problem is severely limited by error propagation.
• KnowledgeNet enables the development of end-to-end systems, which are a promising solution for addressing error propagation.