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Authors: Stefan Prisca ; Mihaela Dinsoreanu and Camelia Lemnaru

Affiliation: Technical University of Cluj-Napoca, Romania

Keyword(s): Word Auto-completion, Topic Oriented Data Models, Topic Indexing.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Information Extraction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Pre-Processing and Post-Processing for Data Mining ; Symbolic Systems ; User Profiling and Recommender Systems

Abstract: In this paper we propose an autocompletion approach suitable for mobile devices that aims to reduce the overall data model size and to speed up query processing while not employing any language specific processing. The approach relies on topic information from input documents to split the data models based on topics and index them in a way that allows fast identification through their corresponding topic. Doing so, the size of the data model used for prediction is decreased to almost one fifth of the size of a model that contains all topics, and the query processing becomes two times faster, while maintaining the same precision obtained by employing a model that contains all topics.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Prisca, S.; Dinsoreanu, M. and Lemnaru, C. (2015). Topic Oriented Auto-completion Models - Approaches Towards Fastening Auto-completion Systems. In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2015) - KDIR; ISBN 978-989-758-158-8; ISSN 2184-3228, SciTePress, pages 241-248. DOI: 10.5220/0005597502410248

@conference{kdir15,
author={Stefan Prisca. and Mihaela Dinsoreanu. and Camelia Lemnaru.},
title={Topic Oriented Auto-completion Models - Approaches Towards Fastening Auto-completion Systems},
booktitle={Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2015) - KDIR},
year={2015},
pages={241-248},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005597502410248},
isbn={978-989-758-158-8},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2015) - KDIR
TI - Topic Oriented Auto-completion Models - Approaches Towards Fastening Auto-completion Systems
SN - 978-989-758-158-8
IS - 2184-3228
AU - Prisca, S.
AU - Dinsoreanu, M.
AU - Lemnaru, C.
PY - 2015
SP - 241
EP - 248
DO - 10.5220/0005597502410248
PB - SciTePress

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