Multi-hop knowledge graph question answer (KGQA) is a challenging task because it requires reasoning over multiple edges of the knowledge graph (KG) to arrive at the right answer. However, KGs are often incomplete with many missing links, posing additional challenges for multi-hop KGQA. Recent research on multi-hop KGQA attempted to deal with KG sparsity with relevant external texts. In our work, we propose a multi-hop KGQA model based on relation knowledge enhancement (RKE-KGQA), which fuses both label and text relations through global attention for relation knowledge augmentation. It is well known that the relation between entities can be represented by labels in the knowledge graph or texts in the text corpus, and multi-hop KGQA needs to jump across different entities through relations. First, we assign an activation probability to each entity, then calculate a score for the enhancement relation, and then transfer the score through the activated relations and, finally, obtain the answer. We carry out extensive experiments on three datasets and demonstrate that RKE-KGQA achieves the outperformance result.
Chinese Named Entity Recognition is the fundamental technology in the field of the Chinese Natural Language Process. It is extensively adopted into information extraction, intelligent question answering, and knowledge graph. Nevertheless, due to the diversity and complexity of Chinese, most Chinese NER methods fail to sufficiently capture the character granularity semantics, which affects the performance of the Chinese NER. In this work, we propose DSKE-Chinese NER: Chinese Named Entity Recognition based on Dictionary Semantic Knowledge Enhancement. We novelly integrate the semantic information of character granularity into the vector space of characters and acquire the vector representation containing semantic information by the attention mechanism. In addition, we verify the appropriate number of semantic layers through the comparative experiment. Experiments on public Chinese datasets such as Weibo, Resume and MSRA show that the model outperforms characterbased LSTM baselines.
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