UMBC ebiquity

Discovering and Querying Hybrid Linked Data

Authors: Zareen Syed, Tim Finin, Muhammad Mahbubur Rahman, James Kukla, and Jeehye Yun

Book Title: Proceedings of the 4th Workshop on Knowledge Discovery and Data Mining Meets Linked Open Data co-located with 12th Extended Semantic Web Conference

Date: May 31, 2015

Abstract: In this paper, we present a unified framework for discovering and querying hybrid linked data. We describe our approach to developing a natural language query interface for a hybrid knowledge base Wikitology, and present that as a case study for accessing hybrid information sources with structured and unstructured data through natural language queries. We evaluate our system on a publicly available dataset and demonstrate improvements over a baseline system. We describe limitations of our approach and also discuss cases where our system can complement other structured data querying systems by retrieving additional answers not available in structured sources.

Type: InProceedings

Publisher: CEUR Workshop Proceedings

Volume: 1365

Google Scholar: search

Number of downloads: 294


Available for download as

size: 438797 bytes

Related Projects:

Past Project