A Linked Data Approach to Know-How

Paolo Pareti; Benoit Testu; Ryutaro Ichise; Ewan Klein; Adam Barker

doi:10.1007/978-3-319-17966-7_24

A Linked Data Approach to Know-How

Paolo Pareti, Benoit Testu, Ryutaro Ichise, Ewan Klein, Adam Barker

School of Informatics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The Web is one of the major repositories of human generated know-how, such as step-by-step videos and instructions. This knowledge can be potentially reused in a wide variety of applications, but it currently suffers from a lack of structure and isolation from related knowledge. To overcome these challenges we have developed a Linked Data framework which can automate the extraction of know-how from existing Web resources and generate links to related knowledge on the Linked Data Cloud. We have implemented our framework and used it to extract a Linked Data representation of two of the largest know-how repositories on the Web. We demonstrate two possible uses of the resulting dataset of real-world know-how. Firstly, we use this dataset within a Web application to offer an integrated visualization of distributed know-how resources. Lastly, we show the potential of this dataset for inferring common sense knowledge about tasks.

Original language	English
Title of host publication	Knowledge Engineering and Knowledge Management
Subtitle of host publication	EKAW 2014 Satellite Events, VISUAL, EKM1, and ARCOE-Logic, Linköping, Sweden, November 24-28, 2014. Revised Selected Papers.
Editors	Patrick Lambrix, Eero Hyvönen, Eva Blomqvist, Valentina Presutti, Guilin Qi, Uli Sattler, Ying Ding, Chiara Ghidini
Publisher	Springer International Publishing
Pages	168-171
Number of pages	4
ISBN (Electronic)	978-3-319-17966-7
ISBN (Print)	978-3-319-17965-0
DOIs	https://doi.org/10.1007/978-3-319-17966-7_24
Publication status	Published - 2015

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer International Publishing
Volume	8982
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

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Pareti, P., Testu, B., Ichise, R., Klein, E., & Barker, A. (2015). A Linked Data Approach to Know-How. In P. Lambrix, E. Hyvönen, E. Blomqvist, V. Presutti, G. Qi, U. Sattler, Y. Ding, & C. Ghidini (Eds.), Knowledge Engineering and Knowledge Management: EKAW 2014 Satellite Events, VISUAL, EKM1, and ARCOE-Logic, Linköping, Sweden, November 24-28, 2014. Revised Selected Papers. (pp. 168-171). (Lecture Notes in Computer Science; Vol. 8982). Springer International Publishing. https://doi.org/10.1007/978-3-319-17966-7_24

Pareti, Paolo ; Testu, Benoit ; Ichise, Ryutaro et al. / A Linked Data Approach to Know-How. Knowledge Engineering and Knowledge Management: EKAW 2014 Satellite Events, VISUAL, EKM1, and ARCOE-Logic, Linköping, Sweden, November 24-28, 2014. Revised Selected Papers.. editor / Patrick Lambrix ; Eero Hyvönen ; Eva Blomqvist ; Valentina Presutti ; Guilin Qi ; Uli Sattler ; Ying Ding ; Chiara Ghidini. Springer International Publishing, 2015. pp. 168-171 (Lecture Notes in Computer Science).

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title = "A Linked Data Approach to Know-How",

abstract = "The Web is one of the major repositories of human generated know-how, such as step-by-step videos and instructions. This knowledge can be potentially reused in a wide variety of applications, but it currently suffers from a lack of structure and isolation from related knowledge. To overcome these challenges we have developed a Linked Data framework which can automate the extraction of know-how from existing Web resources and generate links to related knowledge on the Linked Data Cloud. We have implemented our framework and used it to extract a Linked Data representation of two of the largest know-how repositories on the Web. We demonstrate two possible uses of the resulting dataset of real-world know-how. Firstly, we use this dataset within a Web application to offer an integrated visualization of distributed know-how resources. Lastly, we show the potential of this dataset for inferring common sense knowledge about tasks.",

author = "Paolo Pareti and Benoit Testu and Ryutaro Ichise and Ewan Klein and Adam Barker",

year = "2015",

doi = "10.1007/978-3-319-17966-7_24",

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pages = "168--171",

editor = "Patrick Lambrix and Eero Hyv{\"o}nen and Eva Blomqvist and Valentina Presutti and Guilin Qi and Uli Sattler and Ying Ding and Chiara Ghidini",

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Pareti, P, Testu, B, Ichise, R, Klein, E & Barker, A 2015, A Linked Data Approach to Know-How. in P Lambrix, E Hyvönen, E Blomqvist, V Presutti, G Qi, U Sattler, Y Ding & C Ghidini (eds), Knowledge Engineering and Knowledge Management: EKAW 2014 Satellite Events, VISUAL, EKM1, and ARCOE-Logic, Linköping, Sweden, November 24-28, 2014. Revised Selected Papers.. Lecture Notes in Computer Science, vol. 8982, Springer International Publishing, pp. 168-171. https://doi.org/10.1007/978-3-319-17966-7_24

A Linked Data Approach to Know-How. / Pareti, Paolo; Testu, Benoit; Ichise, Ryutaro et al.

Knowledge Engineering and Knowledge Management: EKAW 2014 Satellite Events, VISUAL, EKM1, and ARCOE-Logic, Linköping, Sweden, November 24-28, 2014. Revised Selected Papers.. ed. / Patrick Lambrix; Eero Hyvönen; Eva Blomqvist; Valentina Presutti; Guilin Qi; Uli Sattler; Ying Ding; Chiara Ghidini. Springer International Publishing, 2015. p. 168-171 (Lecture Notes in Computer Science; Vol. 8982).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - A Linked Data Approach to Know-How

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N2 - The Web is one of the major repositories of human generated know-how, such as step-by-step videos and instructions. This knowledge can be potentially reused in a wide variety of applications, but it currently suffers from a lack of structure and isolation from related knowledge. To overcome these challenges we have developed a Linked Data framework which can automate the extraction of know-how from existing Web resources and generate links to related knowledge on the Linked Data Cloud. We have implemented our framework and used it to extract a Linked Data representation of two of the largest know-how repositories on the Web. We demonstrate two possible uses of the resulting dataset of real-world know-how. Firstly, we use this dataset within a Web application to offer an integrated visualization of distributed know-how resources. Lastly, we show the potential of this dataset for inferring common sense knowledge about tasks.

AB - The Web is one of the major repositories of human generated know-how, such as step-by-step videos and instructions. This knowledge can be potentially reused in a wide variety of applications, but it currently suffers from a lack of structure and isolation from related knowledge. To overcome these challenges we have developed a Linked Data framework which can automate the extraction of know-how from existing Web resources and generate links to related knowledge on the Linked Data Cloud. We have implemented our framework and used it to extract a Linked Data representation of two of the largest know-how repositories on the Web. We demonstrate two possible uses of the resulting dataset of real-world know-how. Firstly, we use this dataset within a Web application to offer an integrated visualization of distributed know-how resources. Lastly, we show the potential of this dataset for inferring common sense knowledge about tasks.

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T3 - Lecture Notes in Computer Science

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BT - Knowledge Engineering and Knowledge Management

A2 - Lambrix, Patrick

A2 - Hyvönen, Eero

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A2 - Ding, Ying

A2 - Ghidini, Chiara

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Pareti P, Testu B, Ichise R, Klein E, Barker A. A Linked Data Approach to Know-How. In Lambrix P, Hyvönen E, Blomqvist E, Presutti V, Qi G, Sattler U, Ding Y, Ghidini C, editors, Knowledge Engineering and Knowledge Management: EKAW 2014 Satellite Events, VISUAL, EKM1, and ARCOE-Logic, Linköping, Sweden, November 24-28, 2014. Revised Selected Papers.. Springer International Publishing. 2015. p. 168-171. (Lecture Notes in Computer Science). doi: 10.1007/978-3-319-17966-7_24

A Linked Data Approach to Know-How

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