Department of Economic Management, Dongchang College of Liaocheng University, Liaocheng 252000, Shandong, China.
Dongchang Middle School of Liaocheng Economic and Technological Development Zone, Liaocheng 252000, Shandong, China.
Comput Intell Neurosci. 2022 Jul 19;2022:6781043. doi: 10.1155/2022/6781043. eCollection 2022.
Latent semantic analysis (LSA) is a natural language statistical model, which is considered as a method to acquire, generalize, and represent knowledge. Compared with other retrieval models based on concept dictionaries or concept networks, the retrieval model based on LSA has the advantages of strong computability and less human participation. LSA establishes a latent semantic space through truncated singular value decomposition. Words and documents in the latent semantic space are projected onto the dimension representing the latent concept, and then the semantic relationship between words can be extracted to present the semantic structure in natural language. This paper designs the system architecture of the public prosecutorial knowledge graph. Combining the graph data storage technology and the characteristics of the public domain ontology, a knowledge graph storage method is designed. By building a prototype system, the functions of knowledge management, knowledge query, and knowledge push are realized. A named entity recognition method based on bidirectional long-short-term memory (bi-LSTM) combined with conditional random field (CRF) is proposed. Bi-LSTM-CRF performs named entity recognition based on character-level features. CRF can use the transition matrix to further obtain the relationship between each position label, so that bi-LSTM-CRF not only retains the context information but also considers the influence between the current position and the previous position. The experimental results show that the LSTM-entity-context method proposed in this paper improves the representation ability of text semantics compared with other algorithms. However, this method only introduces relevant entity information to supplement the semantic representation of the text. The order in the case is often ignored, especially when it comes to the time series of the case characteristics, and the "order problem" may eventually affect the final prediction result. The knowledge graph of legal documents of theft cases based on ontology can be updated and maintained in real time. The knowledge graph can conceptualize, share, and perpetuate knowledge related to procuratorial organs and can also reasonably utilize and mine many useful experiences and knowledge to assist in decision-making.
潜在语义分析（LSA）是一种自然语言统计模型，被认为是获取、概括和表示知识的一种方法。与基于概念词典或概念网络的其他检索模型相比，基于 LSA 的检索模型具有较强的计算能力和较少的人工参与。LSA 通过截断奇异值分解建立潜在语义空间。潜在语义空间中的单词和文档被投影到代表潜在概念的维度上，然后可以提取单词之间的语义关系，以呈现自然语言中的语义结构。本文设计了公诉知识图谱的系统架构。结合图数据存储技术和公共领域本体的特点，设计了一种知识图谱存储方法。通过构建原型系统，实现了知识管理、知识查询和知识推送的功能。提出了一种基于双向长短时记忆网络（bi-LSTM）结合条件随机场（CRF）的命名实体识别方法。Bi-LSTM-CRF 基于字符级特征进行命名实体识别。CRF 可以使用转移矩阵进一步获得每个位置标签之间的关系，使得 bi-LSTM-CRF 不仅保留了上下文信息，还考虑了当前位置与前一位置之间的影响。实验结果表明，与其他算法相比，本文提出的 LSTM-entity-context 方法提高了文本语义的表示能力。然而，这种方法仅引入相关实体信息来补充文本的语义表示。案例中的顺序通常会被忽略，尤其是在涉及案例特征的时间序列时，“顺序问题”最终可能会影响最终的预测结果。基于本体的盗窃案法律文献知识图谱可以实时更新和维护。知识图谱可以对与检察机关相关的知识进行概念化、共享和传承，还可以合理利用和挖掘许多有用的经验和知识，辅助决策。
