Finding Topical Experts in Question & Answer Communities

Abstract

Question and Answer (Q&A) communities (such as Stackoverflow) have become important places for information exchange and knowledge creation. Their success relies predominantly on two aspects of the feedback generated by their members: quality and speed. Of these, the former reflects on the reputation of the community, whilst the latter is indicative of the efficiency of the Q&A system to correctly answer a given question. In this paper, we present a three phase study for identifying and recommending topical experts in Q&A communities. The first phase investigates the most relevant criteria for identifying reputable members of the community (often experts in a given field), the second phase introduces an approach based on semantic annotations to ascertain their area of specialism, and the last phase presents a method to recommend experts to answer questions in their areas of expertise. Our evaluation (carried out using real-world data from the Biology Stack Exchange Q&A community) shows that the numbers of answers provided by each member can be used as reliable indicators of expertise, and semantic annotations can be successfully used to identify the topics in which they specialize. Furthermore, on average, 74% of the recommendations suggested by our method were successful.