Mineral question-answering system in Chinese based on multi-hop reasoning in knowledge graphs

doi:10.13745/j.esf.sf.2024.5.11

Abstract

Abstract:

Mineral knowledge is important for geosciences research. Some mineral databases are used for storing and retrieving mineral knowledge, and common search engines can also answer mineral questions. But the mineral databases cannot answer mineral questions in natural language and the answers returned from the common search engines need to be filtered. To solve the above problems knowledge graphs have been used; however, the current mineral question-answering based on knowledge graphs can only answer simple questions involving one triplet, but not complex questions involving multiple triplets and multi-hop reasoning. This paper presents a mineral question-answering system based on multi-hop reasoning in knowledge graphs. The mineral entities, relations and questions are first transformed into vectors of complex domain to obtain their semantic and reasoning relations by using the ComplEx model, and Bert-LSTM-CRF is applied to obtain the head of the question. Candidate entities of the head are then obtained by calculating the edit distance and word segmentation, and a fully connected network is constructed to obtain the most related entity of the head of the question from the candidate entities and the entity is the start of the reasoning. Next, the entity and question vectors are concatenated into an input vector into the fully connected network to get their most related relation; afterward another entity most related to the starting entity/relation can be obtained from the mineral knowledge graph to start the reasoning of the next hop; the question of the next hop is updated by the concatenated vector of this hop to bring the reasoning information of this hop to the next hop. This process continues until the most related relation obtained is the stop sign predefined. The last entity obtained in this process is the answer to the question and the reasoning path is also remembered. This method is implemented using Python under Tensorflow and compared with related methods, which show the effectiveness of the method. Using this method, a question-answering system capable of answering complex mineral questions is developed under the front and back end separation architecture employing RESTful API, React, Ajax, echarts and Flask, which provides a platform for acquiring mineral knowledge and performing geosciences research.

Key words: mineral, question answering, knowledge graph, multi-hop reasoning

CLC Number:

TP391.1
P628

JI Xiaohui, DONG Yuhang, YANG Zhongji, YANG Mei, HE Mingyue, WANG Yuzhu. Mineral question-answering system in Chinese based on multi-hop reasoning in knowledge graphs[J]. Earth Science Frontiers, 2024, 31(4): 37-46.

Figures/Tables 15

Fig.1 Mineral knowledge question-answering system in Chinese based on multi-hop reasoning in knowledge graph

Fig.2 Mineral question vector representation model

Fig.3 Mineral question head recognition model

Fig.4 Mineral question entity link model

Fig.5 Mineral question answering multi-hop reasoning model

Table 1 Mineral question head recognition model parameters

模型参数	值
每批样本数量	32
学习率	0.001
Dropout	0.2
迭代数量	30
优化器	Adam
LSTM神经元数量	512

Table 2 Comparison of mineral question head recognition models

模型	F1评分/%	准确率P/%	召回率R/%
Bert	85.93	79.29	93.77
Bert-BiLSTM	87.51	84.61	94.79
Bert-BiLSTM-CRF(ours)	90.39	86.64	97.36

Fig.6 Learning curves using F1 score for three central word recognition models

Table 3 Mineral question entity link model parameters

模型参数	值
每批样本数量	32
学习率	0.001
Dropout	0.2
迭代数量	50
优化器	Adam
知识图谱向量长度	512

Table 4 Comparison of mineral question entity link models

模型	F1评分/%	准确率P/%	召回率R/%
Bert	91.32	88.43	94.41
知识图谱文本表示(ours)	94.45	96.14	92.81

Fig.7 Learning curves using F1 scores for two entity connection (disambiguation) models

Table 5 Comparison of mineral question answering multi-hop reasoning models

模型	F1评分/%	准确率P/%	召回率R/%
Bert	87.38	89.75	85.14
基于知识图谱文本表示(ours)	91.93	94.59	89.46

Fig.8 Learning curves using F1 score for two multi-hop inference models

Fig.9 Interface of mineral question-answering system based on multi-hop reasoning in knowledge graphs

Fig.10 Answers returned by ChatGPT for test cases

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