Data-Driven Annotation of Textual Process Descriptions Based on Formal Meaning Representations

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10441597" target="_blank" >RIV/00216208:11320/21:10441597 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-030-79382-1_5" target="_blank" >https://doi.org/10.1007/978-3-030-79382-1_5</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-79382-1_5" target="_blank" >10.1007/978-3-030-79382-1_5</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Data-Driven Annotation of Textual Process Descriptions Based on Formal Meaning Representations

Popis výsledku v původním jazyce

Business process management encompasses a variety of tasks that can be solved system-aided but usually require formal process representations, i.e. process models. However, it requires a significant effort to learn a formal process modeling language like, for instance, BPMN. Among others, this is one reason why companies often still stick to informal textual process descriptions. However, in contrast to formal models, information from natural language text usually cannot be automatically processed by algorithms. Hence, recent research also focuses on annotated textual process descriptions to make text machine processable. While still human-readable, they additionally contain annotations following a formal scheme. Thus, they also enable automated processing by, for instance, formal reasoning and simulation. State-of-the-art techniques for automatically annotating textual process descriptions are either based on hand-crafted rule sets or artificial neural networks. Maintaining complex rule sets requires a significant manual effort and the approaches using neural networks suffer from rather low result quality. In this paper we present an approach based on Semantic Parsing and Graph Convolutional Networks that avoids manually defined rules and provides significantly better results than existing techniques based on neural networks. A comprehensive evaluation using multiple data sets from both academia and industry shows encouraging results and differentiates between several applied text features.

Název v anglickém jazyce

Data-Driven Annotation of Textual Process Descriptions Based on Formal Meaning Representations

Popis výsledku anglicky

Business process management encompasses a variety of tasks that can be solved system-aided but usually require formal process representations, i.e. process models. However, it requires a significant effort to learn a formal process modeling language like, for instance, BPMN. Among others, this is one reason why companies often still stick to informal textual process descriptions. However, in contrast to formal models, information from natural language text usually cannot be automatically processed by algorithms. Hence, recent research also focuses on annotated textual process descriptions to make text machine processable. While still human-readable, they additionally contain annotations following a formal scheme. Thus, they also enable automated processing by, for instance, formal reasoning and simulation. State-of-the-art techniques for automatically annotating textual process descriptions are either based on hand-crafted rule sets or artificial neural networks. Maintaining complex rule sets requires a significant manual effort and the approaches using neural networks suffer from rather low result quality. In this paper we present an approach based on Semantic Parsing and Graph Convolutional Networks that avoids manually defined rules and provides significantly better results than existing techniques based on neural networks. A comprehensive evaluation using multiple data sets from both academia and industry shows encouraging results and differentiates between several applied text features.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

ADVANCED INFORMATION SYSTEMS ENGINEERING (CAISE 2021)

ISBN

978-3-030-79381-4

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

16

Strana od-do

75-90

Název nakladatele

SPRINGER INTERNATIONAL PUBLISHING AG

Místo vydání

CHAM

Místo konání akce

Melbourne

Datum konání akce

28. 6. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000716947800005