Software Expert Discovery via Knowledge Domain Embeddings in a Collaborative Network

TL;DR

This paper introduces a method for expert discovery in Community Question Answering platforms using knowledge domain embeddings derived from natural language relationships, improving expert recommendation accuracy.

Contribution

It proposes a novel approach combining distributed word representations and semantic domain extraction to identify experts based on their historical performance.

Findings

Effective expert ranking on Stack Overflow

Improved matching of questions to experts

Demonstrated competence through experiments

Abstract

Community Question Answering (CQA) websites can be claimed as the most major venues for knowledge sharing, and the most effective way of exchanging knowledge at present. Considering that massive amount of users are participating online and generating huge amount data, management of knowledge here systematically can be challenging. Expert recommendation is one of the major challenges, as it highlights users in CQA with potential expertise, which may help match unresolved questions with existing high quality answers while at the same time may help external services like human resource systems as another reference to evaluate their candidates. In this paper, we in this work we propose to exploring experts in CQA websites. We take advantage of recent distributed word representation technology to help summarize text chunks, and in a semantic view exploiting the relationships between natural language phrases to extract latent knowledge domains. By domains, the users' expertise is determined on their historical performance, and a rank can be compute to given recommendation accordingly. In particular, Stack Overflow is chosen as our dataset to test and evaluate our work, where inclusive experiment shows our competence.