Ontology assisted crowd mining

Yael Amsterdamer, Susan B. Davidson, Tova Milo, Slava Novgorodov, Amit Somech

Research output: Contribution to journalConference articlepeer-review


We present OASSIS (for Ontology ASSISted crowd mining), a prototype system which allows users to declaratively specify their information needs, and mines the crowd for answers. The answers that the system computes are concise and relevant , and represent frequent, significant data patterns. The system is based on (1) a generic model that captures both ontological knowledge, as well as the individual knowledge of crowd members from which frequent patterns are mined; (2) a query language in which users can specify their information needs and types of data patterns they seek; and (3) an efficient query evaluation algorithm, for mining semantically concise answers while minimizing the number of questions posed to the crowd. We will demonstrate OASSIS using a couple of real-life scenarios, showing how users can formulate and execute queries through the OASSIS UI and how the relevant data is mined from the crowd.

Original languageEnglish
Pages (from-to)1597-1600
Number of pages4
JournalProceedings of the VLDB Endowment
Issue number13
StatePublished - 2014
EventProceedings of the 40th International Conference on Very Large Data Bases, VLDB 2014 - Hangzhou, China
Duration: 1 Sep 20145 Sep 2014


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