Enriching how-to guides with actionable phrases and linked data

Lagos, N; Gallé, M; Chernov, A; Sándor, Á

Enriching how-to guides with actionable phrases and linked data

Lagos, N Gallé, M Chernov, A Sándor, Á

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Publisher:: IOS Press
Publication Type:: Journal Article
Citation:: Web Intelligence, 2017, 15, (3), pp. 189-203
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Lagos, N
dc.contributor.author	Gallé, M
dc.contributor.author	Chernov, A
dc.contributor.author	Sándor, Á
dc.date.accessioned	2022-10-03T05:40:08Z
dc.date.available	2022-10-03T05:40:08Z
dc.date.issued	2017-01-01
dc.identifier.citation	Web Intelligence, 2017, 15, (3), pp. 189-203
dc.identifier.issn	2405-6456
dc.identifier.issn	2405-6464
dc.identifier.uri	http://hdl.handle.net/10453/162236
dc.description.abstract	The World Wide Web contains a large quantity of community created knowledge of instructional nature. Similarly, in a commercial setting, databases of instructions are used by customer-care providers to guide clients in the resolution of issues. Most of these instructions are expressed in natural language. Knowledge Bases including such information are valuable through the sum of their single entries. However, as each entry is created mostly independently, users (e.g. other community members) cannot take advantage of the accumulated knowledge that can be developed via the aggregation of related entries and of relevant information that can be found in external knowledge bases. In this paper we consider the problem of linking Knowledge Base entries to other relevant parts of the Knowledge Base and to third-party semi-structured knowledge sources. To achieve this, we propose (i) a new method to detect actionable phrases - text fragments that describe how to perform a certain action - and link them to other entries; and (ii) a new method to detect entities and link them to the Linked Open Data cloud. The method that we implemented for extracting actionable phrases achieves an F-score of 67.35%. We show that limiting the linking space to actionable phrases results in better linking quality than using coarser-grained spans of text, as proposed in other approaches to the task. In addition, we propose a new Linked Open Data linking method that uses a global optimization score to filter the set of possible candidates, increasing precision when compared to a standard method implemented in dbpedia-spotlight. Besides the above scientific contributions, we also present a detailed error analysis, and release our annotations to the community to foster future research on the subject.
dc.language	en
dc.publisher	IOS Press
dc.relation.ispartof	Web Intelligence
dc.relation.isbasedon	10.3233/WEB-170364
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0899 Other Information and Computing Sciences, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Enriching how-to guides with actionable phrases and linked data
dc.type	Journal Article
utslib.citation.volume	15
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0899 Other Information and Computing Sciences
utslib.for	1702 Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)/Connected Intelligence Centre
utslib.copyright.status	closed_access	*
dc.date.updated	2022-10-03T05:40:07Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	15
utslib.citation.issue	3

Abstract:

The World Wide Web contains a large quantity of community created knowledge of instructional nature. Similarly, in a commercial setting, databases of instructions are used by customer-care providers to guide clients in the resolution of issues. Most of these instructions are expressed in natural language. Knowledge Bases including such information are valuable through the sum of their single entries. However, as each entry is created mostly independently, users (e.g. other community members) cannot take advantage of the accumulated knowledge that can be developed via the aggregation of related entries and of relevant information that can be found in external knowledge bases. In this paper we consider the problem of linking Knowledge Base entries to other relevant parts of the Knowledge Base and to third-party semi-structured knowledge sources. To achieve this, we propose (i) a new method to detect actionable phrases - text fragments that describe how to perform a certain action - and link them to other entries; and (ii) a new method to detect entities and link them to the Linked Open Data cloud. The method that we implemented for extracting actionable phrases achieves an F-score of 67.35%. We show that limiting the linking space to actionable phrases results in better linking quality than using coarser-grained spans of text, as proposed in other approaches to the task. In addition, we propose a new Linked Open Data linking method that uses a global optimization score to filter the set of possible candidates, increasing precision when compared to a standard method implemented in dbpedia-spotlight. Besides the above scientific contributions, we also present a detailed error analysis, and release our annotations to the community to foster future research on the subject.

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http://hdl.handle.net/10453/162236