The temporal knowledge graph (TKG) query facilitates the retrieval of potential answers by parsing questions that incorporate temporal constraints, regarded as a vital downstream task in the broader spectrum of the TKG applications. Currently, enhancing the accuracy of the queries and the user experience has become a focal point for researchers. Existing query methods of the TKG aim to execute unambiguous standard query statements to return query results while neglecting the potential ambiguity in user input queries. To overcome this problem, in this paper, we propose a semantic query model for temporal knowledge graphs, TKGSQ-PM (Temporal Knowledge Graph Semantic Query based on Pre-trained Model). This model first identifies and extracts entity and temporal information from temporal knowledge graph queries and obtains corresponding temporal knowledge graph embedding information based on embedding methods. Then, it utilizes the pre-trained model DistilBERT to infer the true query intent from user input queries. Finally, it performs comprehensive sorting to return highquality query results. We conduct multiple experiments on three different datasets to demonstrate the efficiency and effectiveness of the proposed methods. Experimental results indicate that the TKGSQ-PM model has an overall advantage over baseline models in terms of query effectiveness and efficiency.

Entity alignment is the most critical technology of knowledge fusion, which aims to identify and align entities that exist in different knowledge graphs (KGs) but represent the same real-world objects. Temporal knowledge graphs (TKGs) extend static triples into quadruples by introducing temporal information, which is more in line with the dynamic changes of the real world and is often used to enhance the performance of various applications. However, the existing entity alignment methods mainly focus on traditional KGs, ignoring the temporal information contained in TKGs may lead to misalignment between some similar entities. The latest method enhances the performance of entity alignment by learning temporal information embedding, but it does not make full use of the advantages of temporal information. In this paper, we propose a new Entity Alignment method for Cross-lingual TKG with BERT (EACTB). EACTB uses the BERT model of multi-language training to learn the semantic relevance of entity description. For temporal information, we propose a new and more efficient method to calculate the similarity of temporal information. EACTB uses graph convolution network (GCN) to embed structural information. In addition, iterative method is used to deal with the problem of insufficient training datasets. Experimental results on three cross-lingual TKGs datasets show that EACTB is significantly superior to existing methods.

Answering complex logical queries on large-scale Knowledge Graphs (KGs) efficiently and accurately has always been crucial for question-answering systems. Recent studies have significantly improved the performance of complex logical queries on massive knowledge graphs by leveraging graph neural networks (GNNs). However, the existing GNN-based methods still have limitations in dealing with long-sequence logical queries. They usually decompose complex queries into multiple independent first-order logical queries, which leads to the inability to optimize globally, and the query accuracy will drop sharply with the increase of query length. In addition, the knowlege in the real world is dynamically changing, but most of the existing methods are more suitable for dealing with static knowledge graphs, and there is still much room for improvement when dealing with logical queries in temporal knowledge graphs. In this paper, we propose a novel Temporal Complex Logical Query (TCLQ) model to achieve temporal logical queries on temporal knowledge graphs. We add time series embedding into GNN, and use multi-layer GRUs to aggregate the node features of previous time and current time, which effectively enhances the time series reasoning ability of the model. In order to solve the problem that the accuracy of logical query model decreases significantly with the increase of query sequence length, we establish a multi-level attention coefficients model to learn and optimize the whole logical queries, thus reducing the error accumulation problem when the queries are decomposed into multiple independent first-order logical queries. We conduct experiments on multiple temporal datasets and demonstrate the effectiveness of TCLQ.