Retrieving Textual Evidence for Knowledge Graph Facts

By Gonenc Ercan, Shady Elbassuoni, Katja Hose. 2019

13 citations recorded by the source corpus.

Knowledge graphs have become vital resources for semantic search and provide users with precise answers to their information needs. Knowledge graphs often consist of billions of facts, typically encoded in the form of RDF triples. In most cases, these facts are extracted automatically and can thus be susceptible to errors. For many applications, it can therefore be very useful to complement knowledge graph facts with textual evidence. For instance, it can help users make informed decisions about the validity of the facts that are returned as part of an answer to a query. In this paper, we therefore propose Open image in new window , an approach that given a knowledge graph and a text corpus, retrieves the top-k most relevant textual passages for a given set of facts. Since our goal is to retrieve short passages, we develop a set of IR models combining exact matching through the Okapi BM25 model with semantic matching using word embeddings. To evaluate our approach, we built an extensive benchmark consisting of facts extracted from YAGO and text passages retrieved from Wikipedia. Our experimental results demonstrate the effectiveness of our approach in retrieving textual evidence for knowledge graph facts.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. Haopeng Ren
2. Yi Cai
3. Guohua Wang
4. Xiaofeng Chen
5. Qing Li

Citations in the corpus

• A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification (2020, background): “Relation classification (RC) is one of the most important techniques in natural language processing (NLP) and has various applications such as information retrieval (Ercan et al., 2019), question answering (Tong et al.”