  Search engines can quickly response a hyperlink list according to query
keywords. However, when a query is complex, developers need to repeatedly
refine the search keywords and open a large number of web pages to find and
summarize answers. Many research works of question and answering (Q and A)
system attempt to assist search engines by providing simple, accurate and
understandable answers. However, without original semantic contexts, these
answers lack explainability, making them difficult for users to trust and
adopt. In this paper, a brain-inspired search engine assistant named
DeveloperBot based on knowledge graph is proposed, which aligns to the
cognitive process of human and has the capacity to answer complex queries with
explainability. Specifically, DeveloperBot firstly constructs a multi-layer
query graph by splitting a complex multi-constraint query into several ordered
constraints. Then it models the constraint reasoning process as subgraph search
process inspired by the spreading activation model of cognitive science. In the
end, novel features of the subgraph will be extracted for decision-making. The
corresponding reasoning subgraph and answer confidence will be derived as
explanations. The results of the decision-making demonstrate that DeveloperBot
can estimate the answers and answer confidences with high accuracy. We
implement a prototype and conduct a user study to evaluate whether and how the
direct answers and the explanations provided by DeveloperBot can assist
developers' information needs.
