Hongbang Yuan


CogKGE: A Knowledge Graph Embedding Toolkit and Benchmark for Representing Multi-source and Heterogeneous Knowledge
Zhuoran Jin | Tianyi Men | Hongbang Yuan | Zhitao He | Dianbo Sui | Chenhao Wang | Zhipeng Xue | Yubo Chen | Jun Zhao
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

In this paper, we propose CogKGE, a knowledge graph embedding (KGE) toolkit, which aims to represent multi-source and heterogeneous knowledge. For multi-source knowledge, unlike existing methods that mainly focus on entity-centric knowledge, CogKGE also supports the representations of event-centric, commonsense and linguistic knowledge. For heterogeneous knowledge, besides structured triple facts, CogKGE leverages additional unstructured information, such as text descriptions, node types and temporal information, to enhance the meaning of embeddings. Designing CogKGE aims to provide a unified programming framework for KGE tasks and a series of knowledge representations for downstream tasks. As a research framework, CogKGE consists of five parts, including core, data, model, knowledge and adapter module. As a knowledge discovery toolkit, CogKGE provides pre-trained embedders to discover new facts, cluster entities and check facts. Furthermore, we construct two benchmark datasets for further research on multi-source heterogeneous KGE tasks: EventKG240K and CogNet360K. We also release an online system to discover knowledge visually. Source code, datasets and pre-trained embeddings are publicly available at GitHub, with a short instruction video.

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CogKTR: A Knowledge-Enhanced Text Representation Toolkit for Natural Language Understanding
Zhuoran Jin | Tianyi Men | Hongbang Yuan | Yuyang Zhou | Pengfei Cao | Yubo Chen | Zhipeng Xue | Kang Liu | Jun Zhao
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

As the first step of modern natural language processing, text representation encodes discrete texts as continuous embeddings. Pre-trained language models (PLMs) have demonstrated strong ability in text representation and significantly promoted the development of natural language understanding (NLU). However, existing PLMs represent a text solely by its context, which is not enough to support knowledge-intensive NLU tasks. Knowledge is power, and fusing external knowledge explicitly into PLMs can provide knowledgeable text representations. Since previous knowledge-enhanced methods differ in many aspects, making it difficult for us to reproduce previous methods, implement new methods, and transfer between different methods. It is highly desirable to have a unified paradigm to encompass all kinds of methods in one framework. In this paper, we propose CogKTR, a knowledge-enhanced text representation toolkit for natural language understanding. According to our proposed Unified Knowledge-Enhanced Paradigm (UniKEP), CogKTR consists of four key stages, including knowledge acquisition, knowledge representation, knowledge injection, and knowledge application. CogKTR currently supports easy-to-use knowledge acquisition interfaces, multi-source knowledge embeddings, diverse knowledge-enhanced models, and various knowledge-intensive NLU tasks. Our unified, knowledgeable and modular toolkit is publicly available at GitHub, with an online system and a short instruction video.