Attempto Controlled English: A Knowledge Representation Language Readable by Humans and Machines

Fuchs, Norbert E.; Höfler, Stefan; Kaljurand, Kaarel; Rinaldi, Fabio; Schneider, Gerold

doi:10.1007/11526988_6

Cited by 20 publications

(11 citation statements)

References 3 publications

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“…Research in this field has attempted to overcome the limitations of traditional logic-based representation languages by proposing different sub-sets of English which are transformable to logics or formal languages to allow automated analysis and reasoning. Some examples of well-known CNLs include ACE (Fuchs et al, 2005), PENG Schwitter (2002), V2E (Pratt-Hartmann, 2003 and SOS (Cregan et al, 2007). ACE, PENG and V2E are transformable to First-order Logic (FOL), represent a great combination of Logics and natural languages and thus are powerful in both expressiveness and user-friendliness.…”

Section: Related Workmentioning

confidence: 99%

KBRE: a framework for knowledge-based requirements engineering

et al. 2013

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Detecting inconsistencies is a critical part of Requirements Engineering (RE) and has been a topic of interest for several decades. Domain knowledge and semantics of requirements not only play important roles in elaborating requirements but are also a crucial way to detect conflicts among them. In this paper we present a novel knowledge-based RE framework (KBRE) in which domain knowledge and semantics of requirements are central to elaboration, structuring, and management of captured requirements. Moreover, we also show how they facilitate the identification of requirements inconsistencies and other related problems. In our KBRE model, Description Logic (DL) is used as the fundamental logical system for requirements analysis and reasoning. In addition, the application of DL in the form of Manchester OWL Syntax brings simplicity to the formalization of requirements while preserving sufficient expressive power. A tool has been developed and applied to an industrial use case to validate our approach.

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Section: Related Workmentioning

confidence: 99%

KBRE: a framework for knowledge-based requirements engineering

et al. 2013

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“…Moreover, MOS offers an easy means to the formalization of requirements (while preserving suf-ficient expressiveness) as it is close to natural language and much simpler than other languages such as LTL in KAOS [7] or Formal Tropos in Tropos [9]. Moreover, in comparison to controlled English languages such as ACE [8] and SOS [6], MOS is at the higher level of maturity and more powerful to capture domain knowledge. In particular, ACE does not provide explicit constructs to express the "AllValueFrom" property as well as enumerations and datatype properties.…”

Section: The Frameworkmentioning

confidence: 99%

REInDetector: a framework for knowledge-based requirements engineering

Nguyen

Bao

Lumpe

et al. 2012

Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering

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Requirements engineering (RE) is a coordinated effort to allow clients, users, and software engineers to jointly formulate assumptions, constraints, and goals about a software solution. However, one of the most challenging aspects of RE is the detection of inconsistencies between requirements. To address this issue, we have developed REInDetector, a knowledge-based requirements engineering tool, supporting automatic detection of a range of inconsistencies. It provides facilities to elicit, structure, and manage requirements with distinguished capabilities for capturing the domain knowledge and the semantics of requirements. This permits an automatic analysis of both consistency and realizability of requirements. REInDetector finds implicit consequences of explicit requirements and offers all stakeholders an additional means to identify problems in a more timely fashion than existing RE tools. In this paper, we describe the Description Logic used to capture requirements, the REInDetector tool, its support for inconsistency detection, and its efficacy as applied to several RE examples. An important feature of REInDetector is also its ability to generate comprehensive explanations to provide more insights into the detected inconsistencies.

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“…and, or, i.e., if, iff ) and natural language quantification (e.g. for all, there is, every, some, no) works as in natural English, only with some syntactical limitations similar to those in Attempto Controlled English [5]. Sentences in a proof can start with words like then, hence, therefore etc.…”

Section: Microstructurementioning

confidence: 99%

The Naproche Project Controlled Natural Language Proof Checking of Mathematical Texts

Cramer

Fisseni

Koepke

et al. 2010

Lecture Notes in Computer Science

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Abstract. This paper discusses the semi-formal language of mathematics and presents the Naproche CNL, a controlled natural language for mathematical authoring. Proof Representation Structures, an adaptation of Discourse Representation Structures, are used to represent the semantics of texts written in the Naproche CNL. We discuss how the Naproche CNL can be used in formal mathematics, and present our prototypical Naproche system, a computer program for parsing texts in the Naproche CNL and checking the proofs in them for logical correctness.

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Attempto Controlled English: A Knowledge Representation Language Readable by Humans and Machines

Cited by 20 publications

References 3 publications

KBRE: a framework for knowledge-based requirements engineering

KBRE: a framework for knowledge-based requirements engineering

REInDetector: a framework for knowledge-based requirements engineering

The Naproche Project Controlled Natural Language Proof Checking of Mathematical Texts

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