- Mina Farid
- Ihab F. Ilyas
- Steven Whang
- Cong Yu
Web search engines often retrieve answers for queries about popular entities from a growing knowledge base that is populated by a continuous information extraction process. However, less popular entities are not frequently mentioned on the web and are generally interesting to fewer users; these entities reside on the long tail of information. Traditional knowledge base construction techniques that rely on the high frequency of entity mentions to extract accurate facts about these mentions have little success with entities that have low textual support.
We present Lonlies, a system for estimating property values of long tail entities by leveraging their relationships to head topics and entities. We demonstrate (1) how Lonlies builds communities of entities that are relevant to a long tail entity utilizing a text corpus and a knowledge base; (2) how Lonlies determines which communities to use in the estimation process; (3) how we aggregate estimates from community entities to produce final estimates, and (4) how users interact with Lonlies to provide feedback to improve the final estimation results.