Towards the domain agnostic generation of natural language explanations from provenance graphs for casual users
Towards the domain agnostic generation of natural language explanations from provenance graphs for casual users
As more systems become PROV-enabled, there will be a cor- responding increase in the need to communicate provenance data directly to users. Whilst there are a number of existing methods for doing this — formally, diagrammatically, and textually — there are currently no application-generic techniques for generating linguistic explanations of provenance. The principal reason for this is that a certain amount of linguistic information is required to transform a provenance graph — such as in PROV — into a textual explanation, and if this information is not available as an annotation, this transformation is presently not possible. In this paper, we describe how we have adapted the common ‘consensus’ architecture from the field of natural language generation to achieve this graph transformation, resulting in the novel PROVglish architecture. We then present an approach to garnering the necessary linguistic information from a PROV dataset, which involves exploiting the linguistic information informally encoded in the URIs denoting provenance resources. We finish by detailing an evaluation undertaken to assess the effectiveness of this approach to lexicalisation, demonstrating a significant improvement in terms of fluency, comprehensibility, and grammatical correctness.
95-106
Richardson, Darren P.
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Moreau, Luc
033c63dd-3fe9-4040-849f-dfccbe0406f8
Richardson, Darren P.
f55f06e8-4f92-4399-b365-558b4e64d65d
Moreau, Luc
033c63dd-3fe9-4040-849f-dfccbe0406f8
Richardson, Darren P. and Moreau, Luc
(2016)
Towards the domain agnostic generation of natural language explanations from provenance graphs for casual users.
Mattoso, M and Glavic, B
(eds.)
In Provenance and Annotation of Data and Processes. IPAW 2016.
vol. 9672,
Springer.
.
(doi:10.1007/978-3-319-40593-3_8).
Record type:
Conference or Workshop Item
(Paper)
Abstract
As more systems become PROV-enabled, there will be a cor- responding increase in the need to communicate provenance data directly to users. Whilst there are a number of existing methods for doing this — formally, diagrammatically, and textually — there are currently no application-generic techniques for generating linguistic explanations of provenance. The principal reason for this is that a certain amount of linguistic information is required to transform a provenance graph — such as in PROV — into a textual explanation, and if this information is not available as an annotation, this transformation is presently not possible. In this paper, we describe how we have adapted the common ‘consensus’ architecture from the field of natural language generation to achieve this graph transformation, resulting in the novel PROVglish architecture. We then present an approach to garnering the necessary linguistic information from a PROV dataset, which involves exploiting the linguistic information informally encoded in the URIs denoting provenance resources. We finish by detailing an evaluation undertaken to assess the effectiveness of this approach to lexicalisation, demonstrating a significant improvement in terms of fluency, comprehensibility, and grammatical correctness.
Text
dr_ipaw16.pdf
- Accepted Manuscript
More information
Submitted date: 7 March 2016
Accepted/In Press date: 10 April 2016
e-pub ahead of print date: 4 June 2016
Additional Information:
Funded by International Technology Alliance in Network and Information Sciences: International Technology Alliance in Network and Information Sciences Agreement (W911NF-06-3-0001)
Venue - Dates:
6th International Provenance & Annotation Workshop (IPAW'16), McLean, VA, United States, 2016-06-06 - 2016-06-09
Organisations:
Web & Internet Science
Identifiers
Local EPrints ID: 391910
URI: http://eprints.soton.ac.uk/id/eprint/391910
PURE UUID: 42b83dd1-56bd-44f0-8e6c-4e560072c037
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Date deposited: 19 Apr 2016 15:39
Last modified: 15 Mar 2024 18:28
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Contributors
Author:
Darren P. Richardson
Author:
Luc Moreau
Editor:
M Mattoso
Editor:
B Glavic
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