TLAB at the NTCIR-13 AKG Task

In recent years, popular search engines are utilizing the power of Knowledge Graph(KG) to provide specific answers to queries and questions in a direct way. It is expected that search engine result pages (SERPs) will provide facts about the quires satisfying semantic meaning, which encouraging researchers to constructing more powerful Knowledge Graph. One of the major challenges is disambiguating and recognizing entities and their actions stored in KG in a context. To achieve and advance the technologies related to actionable knowledge graph presentation, Action Mining (AM) is an essential step and relatively new research direction to nurture research on generating such KG that is optimized for facilitating entity’s actions e.g. for entity“Donald J. Trump” most potential actions could be “won the US Presidential Election” or “targeting US journalists”. This paper presents the Action Mining (AM) task organized by NTCIR-13. We employ a probabilistic model to address the AM problem.