Relevance Score of Triplets Using Knowledge Graph Embedding - The Pigweed Triple Scorer at WSDM Cup 2017
This work addresses entity search ranking for collaborative knowledge bases, but it is incremental as it combines existing methods.
The paper tackled the problem of scoring relevance between entities and their attributes in knowledge graphs by proposing an ensemble of a knowledge graph embedding model and a bag-of-words model, achieving results for the WSDM Cup 2017 challenge.
Collaborative Knowledge Bases such as Freebase and Wikidata mention multiple professions and nationalities for a particular entity. The goal of the WSDM Cup 2017 Triplet Scoring Challenge was to calculate relevance scores between an entity and its professions/nationalities. Such scores are a fundamental ingredient when ranking results in entity search. This paper proposes a novel approach to ensemble an advanced Knowledge Graph Embedding Model with a simple bag-of-words model. The former deals with hidden pragmatics and deep semantics whereas the latter handles text-based retrieval and low-level semantics.