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Erschienen in:
The Universal Ontology: A Vision for Conceptual Modeling and the Semantic Web (Invited Paper) Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Mining Goal Refinement Patterns: Distilling Know-How from Data

verfasst von : Metta Santiputri, Novarun Deb, Muhammad Asjad Khan, Aditya Ghose, Hoa Dam, Nabendu Chaki

Erschienen in: Conceptual Modeling

Verlag: Springer International Publishing

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Abstract

Goal models play an important role by providing a hierarchic representation of stakeholder intent, and by providing a representation of lower-level subgoals that must be achieved to enable the achievement of higher-level goals. A goal model can be viewed as a composition of a number of goal refinement patterns that relate parent goals to subgoals. In this paper, we offer a means for mining these patterns from enterprise event logs and a technique to leverage vector representations of words and phrases to compose these patterns to obtain complete goal models. The resulting machinery can be quiote powerful in its ability to mine know-how or constitutive norms. We offer an empirical evaluation using both real-life and synthetic datasets.

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Vorheriges Kapitel Towards Rearchitecting Meta-Models into Multi-level Models
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Metadaten
Titel
Mining Goal Refinement Patterns: Distilling Know-How from Data
verfasst von
Metta Santiputri
Novarun Deb
Muhammad Asjad Khan
Aditya Ghose
Hoa Dam
Nabendu Chaki
Copyright-Jahr
2017
Buch
Conceptual Modeling

Print ISBN: 978-3-319-69903-5

Electronic ISBN: 978-3-319-69904-2

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-69904-2_6