Ontology assisted crowd mining

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

doi:10.14778/2733004.2733039

Ontology assisted crowd mining

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

Research output: Contribution to journal › Conference article › peer-review

6 Scopus citations

Abstract

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 language	English
Pages (from-to)	1597-1600
Number of pages	4
Journal	Proceedings of the VLDB Endowment
Volume	7
Issue number	13
DOIs	https://doi.org/10.14778/2733004.2733039
State	Published - 2014
Event	Proceedings of the 40th International Conference on Very Large Data Bases, VLDB 2014 - Hangzhou, China Duration: 1 Sep 2014 → 5 Sep 2014

Funding

Funders	Funder number
National Science Foundation	III-1302212

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10.14778/2733004.2733039

Cite this

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title = "Ontology assisted crowd mining",

abstract = "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.",

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AU - Davidson, Susan B.

AU - Milo, Tova

AU - Novgorodov, Slava

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Ontology assisted crowd mining

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