Integrating Knowledge from Latent and Explicit Features for Triple Scoring - Team Radicchio's Triple Scorer at WSDM Cup 2017

TL;DR

This paper presents an ensemble model combining latent embeddings and explicit features to accurately predict relevance scores for knowledge-base triples, achieving third place in WSDM Cup 2017.

Contribution

The paper introduces a novel ensemble approach integrating word2vec, GloVe, and Freebase features for triple scoring, improving prediction accuracy.

Findings

Achieved 79.72% accuracy in triple score prediction

Ranked third in WSDM Cup 2017 triple scoring task

Demonstrated effectiveness of combining latent and explicit features

Abstract

The objective of the triple scoring task in WSDM Cup 2017 is to compute relevance scores for knowledge-base triples of type-like relations. For example, consider Julius Caesar who has had various professions, including Politician and Author. For two given triples (Julius Caesar, profession, Politician) and (Julius Caesar, profession, Author), the former triple is likely to have a higher relevance score (also called "triple score") because Julius Caesar was well-known as a politician and not as an author. Accurate prediction of such triple scores greatly benefits real-world applications, such as information retrieval or knowledge base query. In these scenarios, being able to rank all relations (Profession/Nationality) can help improve the user experience. We propose a triple scoring model which integrates knowledge from both latent features and explicit features via an ensemble approach. The latent features consist of representations for a person learned by using a word2vec model and representations for profession/nationality values extracted from a pre-trained GloVe embedding model. In addition, we extract explicit features for person entities from the Freebase knowledge base. Experimental results show that the proposed method performs competitively at WSDM Cup 2017, ranking at the third place with an accuracy of 79.72% for predicting within two places of the ground truth score.