# A Novel Time Constraint-Based Approach for Knowledge Graph Conflict Resolution
## 1 Introduction  
Automatic construction of knowledge graphs is an active research area [1; 2; 3]. Projects such as DBpedia [4], KnowItAll [5], Read The Web [6], and Yet Another Great Ontology (YAGO) [7] have achieved high accuracy and recall rates from structured knowledge representations built from unstructured or semi-structured Web resources. In the process of automatically constructing knowledge graphs, there will be cases of inconsistent knowledge [8; 9; 10], which is called knowledge conflicts in knowledge graphs. The recently popular knowledge graph embedding methods embed the entities and relationships into a continuous vector space, while maintaining the structure of the graph. These methods have shown good results in knowledge graph conflict resolution. However, the existing embedding models ignore the time attributes of facts [11], so the performance is not satisfactorywhen dealing with knowledge conflicts brought about by time attributes. However, in the real world, many facts are not static but change over time. Therefore, time attributes should play an important role in the conflict resolution of knowledge graphs. This article focuses on the knowledge conflicts of time-sensitive knowledge graphs. Most of the facts in the real world change dynamically over time, so these facts are time-sensitive in knowledge graphs. The corresponding entities in the knowledge graphs of persons and their relationships also need to be constantly updated to suit constantly changing real facts. The change in entities and their relationships in knowledge graphs is called the knowledge graph evolution process. In the evolution process of knowledge graphs, problems due to noise, inaccuracy, and even errors are often encountered. These errors will cascade during knowledge reasoning and graph evolution [12], which causes inconsistencies and fact conflicts in knowledge graphs. Therefore, to solve the knowledge conflicts that are inconsistent in the process of knowledge graph evolution, it is necessary to propose new methods to ensure the consistency of entities and their relationships. A major shortcoming of the existing knowledge graphs is the lack of time attributes in presentation models and queries. Therefore, these extraction techniques work well if we evaluate each extracted fact individually, but the time limit plays a major role when we need to reason multiple facts from their temporal context. The main reason is the error detection and time stamping caused by the diversity of timetables in the text as well as the inconsistencies of different data sources. To solve the shortcomings of the existing knowledge graph conflict resolution methods, we propose a new time-constrained knowledge graph conflict resolution method, knowledge graph evolution algorithm based on deep learning (Kgedl), which uses entity and relationship vector embedding and path-based searches to represent entity relationships. Kgedl proposes to use the time conflict restriction method to resolve the time-conflicting entity relationship. A large number of experimental results have shown the effectiveness of Kgedl. In summary, the main contributions of this article are as follows:  
1. We propose a knowledge graph conflict resolution method Kgedl, which is based on entity and relationship vector embedding and path finding. Kgedl takes the time attributes of facts as an important feature in the evolution process of knowledge graphs. As far as we know, this is the first attempt to resolve conflicts in time-sensitive knowledge graphs. To incorporate time attributes, we propose a time-sensitive embedding model that encodes and embeds timing information into the geometry of the embedding space;
2. We propose to use time disjoint, pre-relationship, and mutual exclusion to constrain the facts of time-based conflicts. The method of the recurrent neural network is used to adjust the inconsistencies in conflict detection, thus ensuring knowledge consistency during the evolution of knowledge graphs;
3.