Knowledge graph embeddingContextualized embeddingsModeling temporal contextKnowledge graph embeddings (KGE) are vector representations that capture the global distributional semantics of each entity instance and
将文本与知识库统一为WK Graph格式,即将文本视为全连接图word graph,将文本中的entity mention检索的子图作为knowledge sub-graph,entity mention作为anchor node结合为WK Graph 使用预训练的knowledge embedding作为初始化,并与word emedding结合进行训练,避免引入额外的模块; 提出三种Masking方法和Learning Loss; 简要信息...
Since many repository nodes in this graph are adjacent, we use graph embedding technique to enhance their representations while making them similar. Also, in order to better improve the recommendation performance, we combine other repository features and learned graph embedding vectors as the input to...
such as graph traversal algorithms [1]. In addition, the RDF data model inherently supports basic inferences. Being modular, it allows fully parallelized data processing and can represent partial information. RDF is one of the primary graph-based data models that are well-utilized...
Source code for paper "CoLAKE: Contextualized Language and Knowledge Embedding". If you have any problem about reproducing the experiments, please feel free to contact us or propose an issue. Prepare your environment We recommend to create a new environment. ...
Notably, other contextualized pretrained embedding frameworks from the NLP domain, such as ULMFiT46 and ELMo26, could also be tested in the EHR domain. However, we choose BERT in this work because it is widely adopted with proven success. To the best of our knowledge, there are only two ...
COLING'2020: CoLAKE: Contextualized Language and Knowledge Embedding aclanthology.org/2020.coling-main.327/ Topics natural-language-processing deep-learning knowledge-graph language-model knowledge-embedding Resources Readme License MIT license Activity Stars 116 stars Watchers 3 watching Forks ...
关键词:预训练模型,knowledge-Enhanced NLP, Knowledge Embedding,GNN 1.背景及问题描述 之前的一些knowledge-Enhanced预训练语言模型,一般都是使用浅层的、静态的并且独立训练的实体embedding,如TransE等,直接融入到预训练模型中,并且实体embedding也不参与训练,他们之间是天然存在gap的。而一些task,比如实体链接、关系抽取...
Specifically, it first models pairwise transition relationships by a global transition graph, upon which global-level POI embedding can be captured by a graph convolution network. We further generate trajectory contextualized POI representation via a trip-level embedding method that compromises transition-...
A common assumption of existing knowledge graph embedding models is that the relation is a translation vector connecting the embedded head entity and tail entity. However, based on this assumption, the same relation connecting multiple entities may form a circle and lead to m...