Entity Linking Korean Text: An Unsupervised Learning Approach using Semantic Relations

Although entity linking is a widely researched topic, the same cannot be said for entity linking geared for languages other than English. Several limitations including syntactic features and the relative lack of resources prevent typical approaches to entity linking to be used as effectively for other languages in general. We describe an entity linking system that leverage semantic relations between entities within an existing knowledge base to learn and perform entity linking using a minimal environment consisting of a part-of-speech tagger. We measure the performance of our system against Korean Wikipedia abstract snippets, using the Korean DBpedia knowledge base for training. Based on these results, we argue both the feasibility of our system and the possibility of extending to other domains and languages in general.