Entity oriented action recommendations for actionable knowledge graph generation

Popular search engines have recently utilized the power of knowledge graphs (KGs) to provide specific answers to queries in a direct way. Search engine result pages (SERPs) are expected to provide facts in response to queries that satisfy semantic meaning. This encourages researchers to propose more influential knowledge graph generation techniques. To achieve and advance the technologies related to actionable knowledge graph presentation, creating action recommendations (ARs) is an essential step and a relatively new research direction to nurture research on generating KGs that are optimized for facilitating an entity's actions. An action represents the physical or mental activity of an entity. For example, for the entity "Donald J. Trump", typical potential actions could be "won the US presidential election" or "targets US journalists". In this paper, we describe the generation of relevant action recommendations based on entity instance and entity type. We propose two models that employ different approaches. Our first model exploits semisupervised learning and we introduce entity context vector (ECV) as an entity's distinguishing features for capturing the context of entities to reveal the similarity between entities, grounded on the prominent word2vec model. The second model is a probabilistic approach based on the Naive Bayes Theorem. We extensively evaluate our proposed models. Our first model significantly outperforms probabilistic and supervised learning-based models.