计算机科学 ›› 2020, Vol. 47 ›› Issue (11): 237-243.doi: 10.11896/jsjkx.191000015
陈晓军, 向阳
CHEN Xiao-jun, XIANG Yang
摘要： 作为语义网的数据支撑,知识图谱在搜索引擎、智能问答和推荐系统等领域发挥着重要作用,成为了人工智能领域的研究热点。知识图谱因其自身的图展示、图挖掘、图模型等计算优势,可帮助企业或金融从业人员进行业务场景的分析与决策。目前已经有公司将知识图谱应用到金融领域,但是这些知识图谱还存在信息缺失、准确度低等问题,并且现有的金融知识图谱构建方法大都只关注构建过程中的某一环节。针对上述问题,对行业知识图谱构建方法进行系统研究,构建一个企业风险知识图谱,从本体构建、知识抽取、知识融合和知识存储4个方面完整阐述了知识图谱的构建流程。最后,基于企业风险知识图谱,构建了一个智能问答机器人,实现了对知识图谱的检索和利用;为了提高问答系统回答问题的准确性,利用基于字级别的BiLSTM-CRF命名实体识别模型。实验结果表明,在样本数量较少时,基于字级别的模型效果更优。
中图分类号:
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