research-article

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Mining for Topics to Suggest Knowledge Model Extensions

Authors:

Carlos Lorenzetti,

Filippo Menczer,

Thomas ReichherzerAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 11, Issue 2

Article No.: 23, Pages 1 - 30

https://doi.org/10.1145/2997657

Published: 03 December 2016 Publication History

Abstract

Electronic concept maps, interlinked with other concept maps and multimedia resources, can provide rich knowledge models to capture and share human knowledge. This article presents and evaluates methods to support experts as they extend existing knowledge models, by suggesting new context-relevant topics mined from Web search engines. The task of generating topics to support knowledge model extension raises two research questions: first, how to extract topic descriptors and discriminators from concept maps; and second, how to use these topic descriptors and discriminators to identify candidate topics on the Web with the right balance of novelty and relevance. To address these questions, this article first develops the theoretical framework required for a “topic suggester” to aid information search in the context of a knowledge model under construction. It then presents and evaluates algorithms based on this framework and applied in Extender, an implemented tool for topic suggestion. Extender has been developed and tested within CmapTools, a widely used system for supporting knowledge modeling using concept maps. However, the generality of the algorithms makes them applicable to a broad class of knowledge modeling systems, and to Web search in general.

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Mining for Topics to Suggest Knowledge Model Extensions

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cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 11, Issue 2

May 2017

419 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3017677

Editor:
Philip S. Yu
University of Illinois at Chicago, USA

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Publication History

Published: 03 December 2016

Accepted: 01 September 2016

Revised: 01 May 2016

Received: 01 April 2015

Published in TKDD Volume 11, Issue 2

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https://doi.org/10.7717/peerj-cs.1710
Maisonnave MDelbianco FTohmé FMaguitman A(2021)Assessing the behavior and performance of a supervised term-weighting technique for topic-based retrievalInformation Processing & Management10.1016/j.ipm.2020.10248358:3(102483)Online publication date: May-2021
https://doi.org/10.1016/j.ipm.2020.102483
Khan AGhosh SGhosh S(2020)Measuring Domain Knowledge for Early Prediction of Student Performance: A Semantic Approach2020 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE)10.1109/TALE48869.2020.9368439(444-451)Online publication date: 8-Dec-2020
https://doi.org/10.1109/TALE48869.2020.9368439
Khan AGhosh S(2020)Student performance analysis and prediction in classroom learning: A review of educational data mining studiesEducation and Information Technologies10.1007/s10639-020-10230-3Online publication date: 1-Jul-2020
https://doi.org/10.1007/s10639-020-10230-3
Peng MZhu JWang HLi XZhang YZhang XTian G(2018)Mining Event-Oriented Topics in Microblog Stream with Unsupervised Multi-View Hierarchical EmbeddingACM Transactions on Knowledge Discovery from Data10.1145/317304412:3(1-26)Online publication date: 11-Apr-2018
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Cecchini RLorenzetti CMaguitman APonzoni I(2018)Topic relevance and diversity in information retrieval from large datasets: A multi-objective evolutionary algorithm approachApplied Soft Computing10.1016/j.asoc.2017.11.01669(749-770)Online publication date: Aug-2018
https://doi.org/10.1016/j.asoc.2017.11.016

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