Controlled Natural Language Research Articles

CNL2ASP: Converting Controlled Natural Language Sentences into ASP

Abstract Answer set programming (ASP) is a popular declarative programming language for solving hard combinatorial problems. Although ASP has gained widespread acceptance in academic and industrial contexts, there are certain user groups who may find it more advantageous to employ a higher-level language that closely resembles natural language when specifying ASP programs. In this paper, we propose a novel tool, called CNL2ASP, for translating English sentences expressed in a controlled natural language (CNL) form into ASP. In particular, we first provide a definition of the type of sentences allowed by our CNL and their translation as ASP rules and then exemplify the usage of the CNL for the specification of both synthetic and real-world combinatorial problems. Finally, we report the results of an experimental analysis conducted on the real-world problems to compare the performance of automatically generated encodings with the ones written by ASP practitioners, showing that our tool can obtain satisfactory performance on these benchmarks.

Knowledge Authoring for Rules and Actions

AbstractKnowledge representation and reasoning (KRR) systems describe and reason with complex concepts and relations in the form of facts and rules. Unfortunately, wide deployment of KRR systems runs into the problem that domain experts have great difficulty constructing correct logical representations of their domain knowledge. Knowledge engineers can help with this construction process, but there is a deficit of such specialists. The earlier Knowledge Authoring Logic Machine (KALM) based on Controlled Natural Language (CNL) was shown to have very high accuracy for authoring facts and questions. More recently, KALMFL, a successor of KALM, replaced CNL with factual English, which is much less restrictive and requires very little training from users. However, KALMFL has limitations in representing certain types of knowledge, such as authoring rules for multi-step reasoning or understanding actions with timestamps. To address these limitations, we propose KALMRA to enable authoring of rules and actions. Our evaluation using the UTI guidelines benchmark shows that KALMRA achieves a high level of correctness (100%) on rule authoring. When used for authoring and reasoning with actions, KALMRA achieves more than 99.3% correctness on the bAbI benchmark, demonstrating its effectiveness in more sophisticated KRR jobs. Finally, we illustrate the logical reasoning capabilities of KALMRA by drawing attention to the problems faced by the recently made famous AI, ChatGPT.

Pipeline for the Automatic Extraction of Procedural Knowledge from Assembly Instructions into Controlled Natural Language

This paper presents the application of a Natural Language Processing (NLP) pipeline, which automatically extracts procedural knowledge in a standardized way from assembly instructions. The developed pipeline is able to parse and process written German assembly instructions regardless of the language discourse. The pipeline helps resolve ambiguities in assembly instructions by converting them into a Controlled Natural Language (CNL). The pipeline fully automates the translation process from free-text assembly instructions to CNL representations. We investigated and evaluated the efficiency and robustness of the NLP pipeline along multiple dimensions, such as different assembly process designers, language and fuzzy string matching models. To test the developed pipeline we used to automatically extract procedural knowledge in a standardized way for 2,740 assembly instructions obtained from automotive industry. Our investigation shows that the NLP pipeline is able to extract CNL representations with high accuracy (ø 87%). Downstream applications, such as assembly line balancing, can reuse the uniformly extracted procedural knowledge.

A Role and Reference Grammar Account of Adjuncts in the Airbus Corpus: A Quantitative-Based Study

This paper presents the results of a quantitative study of adjuncts in the Airbus corpus carried out within the theoretical framework of Role and Reference Grammar (RRG). We describe the positional behaviour of these peripheral constituents in the Layered Structure of the Clause and postulate scales of positional and peripheral preferences, based on frequency distribution, in the Airbus controlled natural language (CNL). The results obtained were compared with a previous study on adjunct preferences and positions in Natural English to check for changes in these scales due to the nature of the texts written in this CNL. We also aim to contribute to the development of the RRG analysis of adverbials by offering a detailed semantic typology and a description of the syntax of these peripheral constituents grounded in empirical and quantitatively based data that will serve as a basis for the parsing of adverbials in the computational processing of CNLs.

REX: General-Purpose CNL with Code Generation Support

Controlled natural languages (CNLs) have been proposed to address some of the issues of natural language when it is used to express requirements. CNLs, however, are based on formal grammar, which can easily become complex, hard to read, and especially hard to write, and the implementation of support tools can also demand a significant effort. Moreover, unanticipated constructions cannot be handled or have to be handled in unexpected and cumbersome ways. In this article, we present REX, a CNL with a simple grammar that is, thus, easy to understand and easy to support, but still general purpose. To accomplish this, instead of trying to support every conceivable construction and imposing a language on the users, through a small but comprehensive set of rules and through patterns, users specify their own language and how natural it is. Another of the benefits of CNLs is the possibility to automate the transformation of a text or specification into something useful, thereby reducing manual labor and transformation errors. In this article, we also present the support tools that were used to transform a REX text into code and a complete application. It is also shown that this CNL and its support tools can be easily adapted to suit different needs.

ARTEMIS: parsing non-peripheral complex sentences in ASD-STE100

The simulation of natural language understanding is one of the main objectives of natural language processing (NLP). Within the different applications designed for this purpose, the ARTEMIS prototype follows the paradigm of unification grammars, and unlike other trending computational resources, it is theoretically grounded in Role and Reference Grammar (RRG). The syntax-to-semantics linking algorithm proposed in this functional grammar lies at the basis of a parsing process that starts with a natural language sentence, extracts its morphosyntactic features and provides a representation of these in terms of the so-called layered structure of the clause (LSC). The Grammar Development Environment (GDE) in ARTEMIS is a major component where feature- based production rules (syntactic, lexical and constructional) are stored and ready to allow the generation of the enhanced layered structure of the clause of natural language expressions. Syntactic rules that account for phrasal constituents and simple sentences have already been described, but it is now turn to focus on the study of non-peripheral complex sentences. In an attempt to validate these syntactic rules and to avoid some of the common problems that may arise in parsing applications, our research will concentrate on the analysis of RRG’s juncture-nexus combinations as found in a controlled natural language (CNL) such as ASD-STE100 (Aerospace and Defence Simplified Technical English).

How to Write (Science) Better. Simplified English Principles in a Skill-Oriented ESP Course

Abstract Teaching writing to doctoral students or academics at a technical university is a challenging task. Because they need to publish their research findings in English to pursue academic careers, they are usually highly motivated and expect a lot of the class. Their language competences, however, very often lack enough proficiency and may contribute to manuscript rejection. The paper focuses on language issues based on the rules of controlled natural languages (CNLs) and guidelines of Plain English. It shows how employing these issues improves grammatical quality and readability of science-oriented written texts. The paper describes four principles: removing nominalisation and using the so-called strong verbs to make the message simpler and more direct; combining nouns in strings to express complex ideas economically; applying grammatical consistency for coordinate elements in sentences to make them less chaotic; and reducing wordiness to obtain a more precise and comprehensible piece of writing. Sample phrases and sentences from authentic student writing as well as their improved versions are provided to each of the guidelines so that a reader has a deeper insight into how the principles work in a specialist context. Because problems with, for example, research papers, grant proposals or reports are common to various disciplines and at various levels, the Author of the paper draws conclusions that these principles should be implemented not only in a technical but also legal, medical and business writing course offered by English teachers to both young researchers and experienced scientists.

Logical English meets legal English for swaps and derivatives

In this paper, we present an informal introduction to Logical English (LE) and illustrate its use to standardise the legal wording of the Automatic Early Termination (AET) clauses of International Swaps and Derivatives Association (ISDA) Agreements. LE can be viewed both as an alternative to conventional legal English for expressing legal documents, and as an alternative to conventional computer languages for automating legal documents. LE is a controlled natural language (CNL), which is designed both to be computer-executable and to be readable by English speakers without special training. The basic form of LE is syntactic sugar for logic programs, in which all sentences have the same standard form, either as rules of the form conclusion if conditions or as unconditional sentences of the form conclusion. However, LE extends normal logic programming by introducing features that are present in other computer languages and other logics. These features include typed variables signalled by common nouns, and existentially quantified variables in the conclusions of sentences signalled by indefinite articles. Although LE translates naturally into a logic programming language such as Prolog or ASP, it can also serve as a neutral standard, which can be compiled into other lower-level computer languages.

On systematically building a controlled natural language for functional requirements

Natural language (NL) is pervasive in software requirements specifications (SRSs). However, despite its popularity and widespread use, NL is highly prone to quality issues such as vagueness, ambiguity, and incompleteness. Controlled natural languages (CNLs) have been proposed as a way to prevent quality problems in requirements documents, while maintaining the flexibility to write and communicate requirements in an intuitive and universally understood manner. In collaboration with an industrial partner from the financial domain, we systematically develop and evaluate a CNL, named Rimay, intended at helping analysts write functional requirements. We rely on Grounded Theory for building Rimay and follow well-known guidelines for conducting and reporting industrial case study research. Our main contributions are: (1) a qualitative methodology to systematically define a CNL for functional requirements; this methodology is intended to be general for use across information-system domains, (2) a CNL grammar to represent functional requirements; this grammar is derived from our experience in the financial domain, but should be applicable, possibly with adaptations, to other information-system domains, and (3) an empirical evaluation of our CNL (Rimay) through an industrial case study. Our contributions draw on 15 representative SRSs, collectively containing 3215 NL requirements statements from the financial domain. Our evaluation shows that Rimay is expressive enough to capture, on average, 88% (405 out of 460) of the NL requirements statements in four previously unseen SRSs from the financial domain.

An Intelligent Analytics Approach to Minimize Complexity in Ambiguous Software Requirements

An inconsistent and ambiguous Software Requirement Specification (SRS) document results in an erroneous/failed software project. Hence, it is a serious challenge to handle and process complex and ambiguous requirements. Most of the literature work focuses on detection and resolution of ambiguity in software requirements. Also, there is no standardized way to write unambiguous and consistent requirements. The goal of this research was to generate an ambiguity-less SRS document. This paper presents a new approach to write ambiguity-less requirements. Furthermore, we design a framework for Natural Language (NL) to Controlled Natural Language (CNL) (such as Semantic Business Vocabulary and Rules (SBVR)) transition and develop a prototype. The prototype also generates Resource Description Framework (RDF) representation. The SBVR has a shared meaning concept that minimizes ambiguity, and RDF representation is supported by query language such as SPARQL Protocol and RDF Query Language (SPARQL). The proposed approach can help software engineers to translate NL requirements into a format that is understandable by all stakeholders and also is machine processable. The results of our prototype are encouraging, exhibiting the efficient performance of our developed prototype in terms of usability and correctness.

A Socio-Technical Approach for Resilient Connected Transportation Systems in Smart Cities

Connected transportation systems in smart cities can be regarded as Cyber-Physical-Social Systems (CPSSs) or Socio-Technical Systems (STSs) due to the presence of connectivity and complex interaction between human and technological infrastructure. Safety and security are essential requirements for such transportation CPSS. Specifically, resilience against cyber-attacks is crucial to address the vulnerabilities of Information and Communication Technologies (ICT) within the infrastructure. In this work, we explore a cyber-attack detection paradigm that leverages the socio-technical nature of such transportation CPSS. Essentially, we exploit the redundancies between physical signals (received from vehicle and infrastructure sensors) and social signals (received from consumers’ mobile devices and social media) to detect cyber-attack occurrences. The proposed scheme is developed combining system and control theoretic tools and natural language processing techniques. A case study of a vehicular platoon is considered on which extensive simulation studies have been performed. Such simulation results under different types of cyber-attacks illustrate the promising nature of the proposed approach.

The Science Library: Curation and visualization of a science gateway repository

SummaryScientific publications from a group or consortium often form a coherent larger body of work with underlying threads and relationships. Rich social, structural, and topical networks between authors and organizations can be identified, and to convey these we have created the publicly available “Science Library” as a user‐centric, interactive portal. A key consideration in this endeavor is rapid and efficient curation of the corpus of publications, both in terms of assuring quality, as well minimizing the effort required. For this to be sustainable it must offer substantial benefits to the community and avoid excessive operational cost through cumbersome or complex processes. We describe the agility of the Science Library implementation as a controlled natural language (CNL) semantic knowledge graph and describe the different roles within the community to ensure efficient curation, validation, and provenance of the content. By describing the process of curation and validation, alongside the CNL‐based definition of the model we show how relatively non‐technical users are able to interact with, and contribute to the Science Library. This provides an extensible approach, initially based around digital library and virtual community capabilities, that can be applied more broadly to support other desired capabilities of Science Gateways.

Towards an ergonomic linguistics: Application to the design of controlled natural languages

AbstractThis article proposes to define a “new” discipline within applied linguistics: ergonomic linguistics. The article addresses issues concerning language use within organizations, focusing on human‐oriented controlled natural languages (CNLs) whose role is to limit the problems encountered with natural language, in professional contexts in particular. An ergonomic linguistics approach involves first taking into account real linguistic productions and analyzing them with corpus linguistics methods, and then evaluating the efficacy of linguists’ recommendations using cognitive evaluation or acceptance tests. Two projects concerning human‐oriented CNLs that adopt an ergonomic linguistics approach are presented, the first in the space domain and the second in aeronautics.

Automation and consistency analysis of test cases written in natural language: An industrial context

We present here a novel test automation strategy that receives as input a freestyle natural language (NL) test case (consisting of a sequence of test steps) and produces executable test scripts. This strategy relies on a database of previously automated seed test steps, available for reuse. New steps are automated via a capturing process by a tester, without requiring any programming knowledge. Automated tests can be executed by a replay facility. We discuss the reuse improvement, implementation effort, and user feedback regarding the industrial applicability and usability of our capture & replay tool. We then show that restricting the input textual description to obey a proposed Controlled NL (CNL) brings significant advantages: (1) reuse improvement; (2) the possibility of integration with a test generation framework; and (3) definition of consistency notions for test actions and test action sequences, that ensure, respectively, well-formedness of each action and a proper configuration to safely execute a sequence of actions. We formalize these consistency notions in Alloy and use the Alloy Analyzer to carry out the consistency check; the scalability of the analysis is assessed via an evaluation considering a repository with real test cases; the practical context of our work is mobile device testing, involving a partnership with Motorola Mobility, a Lenovo company. • Definition of a CNL to create a standard for test cases that are input to automation. • Formal definition of concepts related to our strategy and test automation in general. • Encoding of these definitions in Alloy, as well as of two consistency notions. • A tool that offers a CNL editor and automatically fixes consistency errors in TCs. • An experiment to measure the scalability of the consistency analysis strategy.

Fluent Editor and Controlled Natural Language in Ontology Development

Ontologies facilitate concise communication across applications and people, by means of precisely defined terms that provide a shared and common understanding. However, typically, the development of an ontology entails the commitment of multiple users and requires significant skills and knowledge in this area. To face this problem, in this paper we discuss the features and capabilities of Fluent Editor and, based on a real-life example, present the properties of Controlled Natural Language (CNL). Firstly, the editor is designed to be user-friendly and is customized with a ribbon and self-extensible windows. Secondly, it actively supports the user when typing sentences by providing built-in features such as syntax highlighting, a hint box, a predicted word list, marking errors, parsing and serializing, and reformatting and validating options. Thirdly, the built-in graphics engine produces high quality images of ontology diagrams, represented on a 2-D view-port, and easy to manipulate and change. Fourthly, CNL allows users with even basic English skills to start typing sentences with little or no preliminary effort in defining terms and the relations between them, and prevents the user from entering any grammatically or morphologically incorrect sentence. The evidenced success of the development of ontologies in many business areas, using CNL aided by Fluent Editor on the one hand, and results from similar studies documented in the literature on the other, seem to indicate that together the language and the tool form a mature approach to developing and maintaining complex information structures. In this case, there is substantial evidence to indicate that the adoption of the above technologies to teach the foundations of knowledge engineering covering artificial intelligence applications, ought to be considered as a good choice also for classroom settings.

EMIL: Extracting Meaning from Inconsistent Language: Towards argumentation using a controlled natural language interface

There are well-developed formal and computational theories of argumentation to reason in the face of inconsistency, some with implementations; there are recent efforts to extract arguments from large textual corpora. Both developments are leading towards automated processing and reasoning with inconsistent, linguistically expressed knowledge in order to provide explanations and justifications in a form accessible to humans. However, there remains a gap between the knowledge-bases of computational theories of argumentation, which are generally coarse-grained and semi-structured (e.g. propositional logic), and inferences from knowledge-bases derived from natural language, which are fine-grained and highly structured (e.g. predicate logic). Arguments that occur in textual corpora are very rich, highly various, and incompletely understood. We identify several subproblems which must be addressed in order to bridge the gap, requiring the development of a computational foundation for argumentation coupled with natural language processing. For the computational foundation, we provide a direct semantics, a formal approach for argumentation, which is implemented and suitable to represent and reason with an associated natural language expression for defeasibility. It has attractive properties with respect to expressivity and complexity; we can reason by cases; we can structure higher level argumentation components such as cases and debates. With the implementation, we output experimental results which emphasise the importance of our efficient approach. To motivate our formal approach, we identify a range of issues found in other approaches. For the natural language processing, we adopt and adapt an existing controlled natural language (CNL) to interface with our computational theory of argumentation; the tool takes natural language input and automatically outputs expressions suitable for automated inference engines. A CNL, as a constrained fragment of natural language, helps to control variables, highlights key problems, and provides a framework to engineer solutions. The key adaptation incorporates the expression ‘it is usual that’, which is a plausibly ‘natural’ linguistic expression of defeasibility. This is an important, albeit incremental, step towards the incorporation of linguistic expressions of defeasibility; yet, by engineering such specific solutions, a range of other, relevant issues arise to be addressed. Overall, we can input arguments expressed in a controlled natural language, translate them to a formal knowledge base, represent the knowledge in a rule language, reason with the rules, generate argument extensions, and finally convert the arguments in the extensions into natural language. Our approach makes for fine-grained, highly structure, accessible, and linguistically represented argumentation evaluation. The overall novel contribution of the paper is an integrated, end-to-end argumentation system which bridges a gap between automated defeasible reasoning and a natural language interface. The component novel contributions are the computational theory of ‘direct semantics’, the motivation for our theory, the results with respect to the direct semantics, the implementation, the experimental results, the tie between the formalisation and the CNL, the adaptation of a CNL defeasibility, and an ‘engineering’ approach to fine-grained argument analysis.

Automatic smart contract generation using controlled natural language and template

Smart contracts, which are widely recognized as key components of blockchain technology, enable automatic execution of agreements. Since each smart contract is a computer program that autonomously runs on a blockchain platform, their development requires much effort and care compared with the development of more common programs. In this paper, we propose a technique to automatically generate a smart contract from a human-understandable contract document that is created using a document template and a controlled natural language (CNL). The automation is based on a mapping from the document template and the CNL to a formal model that can define the terms and conditions in a contract including temporal constraints and procedures. The formal model is then translated into an executable smart contract. We implemented a toolchain that generates smart contracts of Hyperledger Fabric from template-based contract documents via a formal model. We then evaluated the feasibility of our approach through case studies of two types of real-world contracts in different domains.

Test case generation, selection and coverage from natural language

In Model-based Testing (MBT), test cases are automatically generated from a formal model of the system. A disadvantage of MBT is that developers must deal with formal notations. This limitation is addressed in this paper, where use cases are used to model the system. In previous work, we have proposed an automatic strategy for generating test cases from use cases written in a Controlled Natural Language (CNL), which is an English textual notation with a well-defined grammar. Due to its precise syntax, it can be processed and translated into a formal representation for the purpose of automatic test case generation. This paper extends our previous work by proposing a state-based CNL for describing use case control flows enriched with state and data operations. We translate state-based use case descriptions into CSP processes from which test cases can be automatically generated. In addition, we show how a similar notation can be used to specify test selection via the definition of state-based test purposes, which are also translated into CSP processes. Test generation and selection are mechanised by refinement checking using the CSP tool FDR. Despite the fact that we work at a purely process algebraic level to define a test generation strategy, we are able to address model coverage criteria. Particularly, by using FDR, it is possible to have access to the underlying LTS models; we then implemented algorithms for covering events or transitions, possibly combined with selection using test purposes. We also discuss several ways of improving the efficiency of the test generation strategy. As far as we are aware, this integration between an algebraic approach to test case generation with an operational approach for coverage criteria is an original and promising insight. All steps of the strategy are integrated into a tool that provides a GUI for authoring use cases and test purposes described in the proposed CNL, so the formal CSP notation is completely hidden from the test designer. We illustrate our tool and techniques with a running example and a more elaborate case study taken from an industrial setting. • A process algebraic approach to test generation from use cases in natural language. • Integration of coverage criteria into a purely algebraic test generation approach. • Test generation considering coverage of events or transitions of the model. • Complexity analysis and a provably sound optimisation of the test generation approach. • An industrial case study with metrics on the efficiency of the test generation strategy.

Semi-Automatic Translations Of Data Privacy Policies Into Controlled Natural Languages

Natural languages (NLs) are a simple and understandable form for presenting knowledge. However, they are ambiguous and it turns out to be quite complex to process them with machines. Controlled Natural Languages (CNLs) are usually simpler versions of NLs that are obtained by restricting the grammar and vocabulary to reduce, or even eliminate, ambiguity and complexity. CNLs look informal like NLs and are easy to read and understand and can be easily be transformed into machine-readable forms. In this paper, we present NLPT for semi-automatic translation of privacy statements, from NL to a controlled natural one, to improve machine processing. To assess the performance, we experiment on a large set of Twitter data policies. Here, we consider two main aspects i) the translation of social network data privacy policy and ii) the efficiency and efficacy of the proposed system. We also perform an empirical analysis of the results and conclude that our system can be used to translate input policies effectively and efficiently.

Domain and Schema Independent Semantic Model Verbalization: A Conceptual Overview

Semantic Web-based technologies have become extremely popular and its a success that has spread across many domains, additional to the computer science domain. Nevertheless, the reusability aspects associated with the created and available semantic knowledge models are very low. The main bottleneck associated with this issue is, the difficulty associated in understanding the complex schema of a knowledge model created and barriers associated with querying the knowledge models using SPARQL or SQWRL query formulations. This research emphasizes on proposing a verbalizer which can go beyond existing Controlled Natural Language (CNL) type verbalizers and to verbalizer knowledge stored in a knowledge model file written in either RDF or OWL format, despite its domain and schematics.