Trivia Mining from Knowledge Graphs

Trivia is any fact about an entity which is interesting due to its unusualness, uniqueness or unexpectedness. Trivia could be successfully employed to promote user engagement in various product experiences featuring the given entity. A Knowledge Graph (KG) is a semantic network which encodes various facts about entities and their relationships. We propose a novel approach called DBpedia Trivia Miner (DTM) to automatically mine trivia for entities of a given domain in KGs. The essence of DTM lies in learning an Interestingness Model (IM), for a given domain, from human annotated training data provided in the form of interesting facts from the KG. The IM thus learnt is applied to extract trivia for other entities of the same domain in the KG. We propose two different approaches for learning the IM a) A Convolutional Neural Network (CNN) based approach and b) Fusion Based CNN (F-CNN) approach which combines both hand-crafted and CNN features. Experiments across two different domains Bollywood Actors and Music Artists reveal that CNN automatically learns features which are relevant to the task and shows competitive performance relative to hand-crafted feature based baselines whereas F-CNN significantly improves the performance over the baseline approaches which use hand-crafted features alone. We also study the problem of relevance scoring of triples. We use the triples with “type-like” relations in which we employed a dataset with relevance scores ranging from 0 to 7, with 7 being the “most relevant” and 0 being the “least relevant”. The task focuses on two such relations: profession and nationality. We built a system which could automatically predict the relevance scores for unseen/new triples. Our model is primarily a supervised machine learning based one in which we use well-designed features which are used to a build a Logistic Ordinal Regression based classification model.