ELAD: An Entity Linking Based Affiliation Disambiguation Framework

Abstract

The number of papers has exploded as more and more people and more types of institutions participating in scientific research. At the same time, institution name disambiguation (IND) is getting more sophisticated, which is critical for research assessment, scholar alignment, etc. Previous knowledge-based and rule-based methods require knowledge and rules prepared in advance, which cannot cope with growing and changing data and learning rules, especially for data with a long period and abundant sources. This paper proposes an automatic learning framework to solve the problem, which is based on entity linking, entity type recognition, candidate generation, and result selection. Experiments show that precision and recall is much higher than the traditional method, ELAD learns more knowledge from the knowledge graph, and it can deal with ever-changing and ever-increasing data. What's more, it solves many problems that cannot be solved by traditional methods: the connection between institution entities, mistakes correction, and the reduction of manual and pre-prepared knowledge. At last, for the case study, we develop two applications based on ELAD which proves its reliability. INDEX TERMS Institution name disambiguation (IND), knowledge graph, entity linking.

Evolutions and trends of artificial intelligence (AI): research, output, influence and competition

“…(2019), Topic Trend [3], Master Reading Tree [4], KnowledgeAtlas [5], etc. Besides, some crucial methods makes it possible to employ SciSci analysis, including institution name disambiguation (Shao et al , 2020), scholar positioning (Tang et al , 2019), and so on. All of these tools can help us with our SciSci analysis, which provides us with a way to analyze the research, output, influence and competition of AI in depth.…”