The open domain knowledge base is very important. It is usually extracted
from encyclopedia websites and is widely used in knowledge retrieval systems,
question answering systems, or recommendation systems. In practice, the key
challenge is to maintain an up-to-date knowledge base. Different from Unwieldy
fetching all of the data from the encyclopedia dumps, to enlarge the freshness
of the knowledge base as big as possible while avoiding invalid fetching, the
current knowledge base updating methods usually determine whether entities need
to be updated by building a prediction model. However, these methods can only
be defined in some specific fields and the result turns out to be obvious bias,
due to the problem of data source and data structure. The users' query
intentions are often diverse as to the open domain knowledge, so we construct a
topic-aware graph network for knowledge updating based on the user query log.
Our methods can be summarized as follow: 1. Extract entities through the user's
log and select them as seeds 2. Scrape the attributes of seed entities in the
encyclopedia website, and self-supervised construct the entity attribute graph
for each entity. 3. Use the entity attribute graph to train the GNN entity
update model to determine whether the entity needs to be synchronized. 4.Use
the encyclopedia knowledge to match and update the filtered entity with the
entity in the knowledge base according to the minimum edit times algorithm.

基于用户查询日志，构造主题感知的图网络进行知识更新，通过实体属性图训练 GNN 实体更新模型以确定实体是否需要同步，并使用最小编辑次数算法将过滤的实体与知识库中的实体进行匹配和更新。