arXiv:1712.08353
Relevance Score of Triplets Using Knowledge Graph Embedding - The Pigweed Triple Scorer at WSDM Cup 2017
원문 초록
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.
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Knowledge Graph Embedding by Translating on Hyperplanes
ETT-CKGE: Efficient Task-driven Tokens for Continual Knowledge Graph Embedding
SMART: Relation-Aware Learning of Geometric Representations for Knowledge Graphs
Automatic Synthesis of High-Quality Triplet Data for Composed Image Retrieval
Contrast & Compress: Learning Lightweight Embeddings for Short Trajectories
ESA: External Space Attention Aggregation for Image-Text Retrieval
토론
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