Papers with Code 아카이브

Open Knowledge Graph Canonicalization

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크
0
평가표
1
보존된 평가 기록
1

Open Information Extraction approaches leads to creation of large Knowledge bases (KB) from the web. The problem with such methods is that their entities and relations are not canonicalized, which leads to storage of redundant and ambiguous facts. For example, an Open KB storing <Barack Obama, was born in, Honolulu> and <Obama, took birth in, Honolulu> doesn't know that Barack Obama and Obama mean the same entity. Similarly, took birth in and was born in also refer to the same relation. Problem of Open KB canonicalization involves identifying groups of equivalent entities and relations in the KB.

<span style="color:grey; opacity: 0.6">( Image credit: CESI: Canonicalizing Open Knowledge Bases using Embeddings and Side Information )</span>

데이터셋

출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.

ReVerb45K

Open KB canonicalization dataset. ReVerb45K increases the entity number to 7.5K and has 45K triples in total. ReVerb45K extract a source sentence for each triple from ClueWeb09

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.