short-paper

Combining Taxonomies using Word2vec

Authors:

Tobias Swoboda,

Matthias Hemmje,

Mihai Dascalu, and

Stefan Trausan-MatuAuthors Info & Claims

DocEng '16: Proceedings of the 2016 ACM Symposium on Document Engineering

September 2016

Pages 131 - 134

https://doi.org/10.1145/2960811.2967151

Published: 13 September 2016 Publication History

Abstract

Taxonomies have gained a broad usage in a variety of fields due to their extensibility, as well as their use for classification and knowledge organization. Of particular interest is the digital document management domain in which their hierarchical structure can be effectively employed in order to organize documents into content-specific categories. Common or standard taxonomies (e.g., the ACM Computing Classification System) contain concepts that are too general for conceptualizing specific knowledge domains. In this paper we introduce a novel automated approach that combines sub-trees from general taxonomies with specialized seed taxonomies by using specific Natural Language Processing techniques. We provide an extensible and generalizable model for combining taxonomies in the practical context of two very large European research projects. Because the manual combination of taxonomies by domain experts is a highly time consuming task, our model measures the semantic relatedness between concept labels in CBOW or skip-gram Word2vec vector spaces. A preliminary quantitative evaluation of the resulting taxonomies is performed after applying a greedy algorithm with incremental thresholds used for matching and combining topic labels.

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Combining Taxonomies using Word2vec

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cover image ACM Conferences

DocEng '16: Proceedings of the 2016 ACM Symposium on Document Engineering

September 2016

222 pages

ISBN:9781450344388

DOI:10.1145/2960811

General Chair:
Robert Sablatnig
TU Wien, Austria
,
Program Chair:
Tamir Hassan
HP Labs, Austria

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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SIGDOC: ACM Special Interest Group on Systems Documentation

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Association for Computing Machinery

New York, NY, United States

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Published: 13 September 2016

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EC H2020 EDISON
EC H2020 RAGE

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DocEng '16

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SIGWEB

DocEng '16: ACM Symposium on Document Engineering 2016

September 13 - 16, 2016

Vienna, Austria

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DocEng '16 Paper Acceptance Rate 11 of 35 submissions, 31%;

Overall Acceptance Rate 178 of 537 submissions, 33%

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Cited By

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https://doi.org/10.3390/app14072833
Zhou EPeng YShao GDeng FMiao YFan W(2024)Password cracking using chunk similarityFuture Generation Computer Systems10.1016/j.future.2023.09.013150(380-394)Online publication date: Jan-2024
https://doi.org/10.1016/j.future.2023.09.013
Diwisch KEngel FWatkins JHemmje M(2023)Verwaltung von Kultur-Artefakten: Herausforderungen bei der Realisierung typischer Einsatzszenarien in Kulturerbe-ArchivenWissensbasierte KI-Anwendungen10.1007/978-3-662-68002-5_15(245-258)Online publication date: 1-Dec-2023
https://doi.org/10.1007/978-3-662-68002-5_15
Pachot AAlbouy-Kissi AAlbouy-Kissi BChausse F(2021)Production2Vec: a hybrid recommender system combining semantic and product complexity approach to improve industrial resiliency2021 2nd International Conference on Artificial Intelligence and Information Systems10.1145/3469213.3469218(1-6)Online publication date: 28-May-2021
https://dl.acm.org/doi/10.1145/3469213.3469218
Deng YTang XZhou ZYang YNiu F(2021)Application of machine learning algorithms in wind power: a reviewEnergy Sources, Part A: Recovery, Utilization, and Environmental Effects10.1080/15567036.2020.1869867(1-22)Online publication date: 8-Feb-2021
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Kok KChua H(2021)Using Word2Vec-LDA-Cosine Similarity for Discovering News Dissemination Pattern to Support Government–Citizen EngagementProceedings of International Conference on Data Science and Applications10.1007/978-981-16-5120-5_53(703-716)Online publication date: 23-Nov-2021
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Al-Zaidy RGiles C(2018)Extracting Semantic Relations for Scholarly Knowledge Base Construction2018 IEEE 12th International Conference on Semantic Computing (ICSC)10.1109/ICSC.2018.00017(56-63)Online publication date: Jan-2018
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Toma IBacioiu FDascalu MTrausan-Matu S(2018)The Edutainment Platform: Interactive Storytelling Relying on Semantic SimilarityChallenges and Solutions in Smart Learning10.1007/978-981-10-8743-1_13(87-96)Online publication date: 10-Mar-2018
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Diwisch KEngel FWatkins JHemmje M(2018)Managing Cultural Assets: Challenges for Implementing Typical Cultural Heritage Archive’s Usage ScenariosSemantic Applications10.1007/978-3-662-55433-3_15(219-230)Online publication date: 14-Apr-2018
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