Billion-scale Pre-trained E-commerce Product Knowledge Graph Model
arXiv:2105.00388
Abstract
In recent years, knowledge graphs have been widely applied to organize data in a uniform way and enhance many tasks that require knowledge, for example, online shopping which has greatly facilitated people's life. As a backbone for online shopping platforms, we built a billion-scale e-commerce product knowledge graph for various item knowledge services such as item recommendation. However, such knowledge services usually include tedious data selection and model design for knowledge infusion, which might bring inappropriate results. Thus, to avoid this problem, we propose a Pre-trained Knowledge Graph Model (PKGM) for our billion-scale e-commerce product knowledge graph, providing item knowledge services in a uniform way for embedding-based models without accessing triple data in the knowledge graph. Notably, PKGM could also complete knowledge graphs during servicing, thereby overcoming the common incompleteness issue in knowledge graphs. We test PKGM in three knowledge-related tasks including item classification, same item identification, and recommendation. Experimental results show PKGM successfully improves the performance of each task.
arXiv commentPaper accepted by ICDE2021
References in corpus (10)
- Language Models are Few-Shot Learners
- A Survey on Knowledge Graphs: Representation, Acquisition and Applications
- XLNet: Generalized Autoregressive Pretraining for Language Understanding
- K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters
- ERNIE: Enhanced Language Representation with Informative Entities
- KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation
- Knowledge-Aware Language Model Pretraining
- Multi-relational Poincaré Graph Embeddings
- Knowledge Enhanced Contextual Word Representations
- Progressively Pretrained Dense Corpus Index for Open-Domain Question Answering