An Analysis of Knowledge Collected from Volunteer Contributors

Book One

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 20

Knowledge Acquisition and Engineering

A new generation of intelligent applications can be enabled by broad-coverage repositories of knowledge. One emerging approach to constructing such repositories is proactive knowledge collection from large numbers of volunteer contributors. In this paper, we study the coverage and quality of a representative collection of part-of information contributed by volunteers. We analyze growth of coverage over time, redundancy of the collected knowledge, and the effect of the coverage and redundancy on the quality of the collection. We also present initial comparisons with collections created by ontology engineering and text extraction approaches. Our analysis reveals that redundancy of contribution helps identify high quality statements, but that some of the statements also have overly high redundancy, drawing contributor effort away from areas where they are needed more. We suggest possible ways to address these issues in future collection efforts.