Improving Human Annotation Effectiveness for Fact Collection by Identifying the Most Relevant Answers
This paper was accepted at the Workshops on Data Science with Human in the Loop at EMNLP 2022 Identifying and integrating missing facts is a crucial task for knowledge graph completion to ensure robustness towards downstream applications such as question answering. Adding new facts to a knowledge graph in real world system often involves human verification effort, where candidate facts are verified for accuracy by human annotators. This process is labor-intensive, time-consuming, and inefficient since only a small number of missing facts can be identified. This paper proposes a simple but…
Knowledge Graphs represent real-world entities and the relationships between them. Multilingual Knowledge Graph Construction (mKGC) refers to the task of automatically constructing or predicting missing entities and links for knowledge graphs in a multilingual setting. In this work, we reformulate the mKGC task as a Question Answering (QA) task and…
Multilingual knowledge graphs (KGs) provide high-quality relational and textual information for various NLP applications, but they are often incomplete, especially in non-English languages. Previous research has shown that combining information from KGs in different languages aids either Knowledge Graph Completion (KGC), the task of predicting missing relations between entities, or…