Rolf Schwitter

Attempto Controlled English - Not Just Another Logic Specification Language

The specification language Attempto Controlled English (ACE) is a controlled natural language, i.e. a subset of standard English with a domain-specific vocabulary and a restricted grammar. The restriction of full natural language to a controlled subset is essential for ACE to be suitable for specification purposes. The main goals of this restriction are to reduce ambiguity and vagueness inherent in full natural language and to make ACE computer processable. ACE specifications can be unambiguously translated into logic specification languages, and can be queried and executed. In brief, ACE allows domain specialists to express specifications in familiar natural language and combine this with the rigour of formal specification languages.

English as a formal specification language

PENG is a computer-processable controlled natural language designed for writing unambiguous and precise specifications. PENG covers a strict subset of standard English and is precisely defined by a controlled grammar and a controlled lexicon. In contrast to other controlled languages, the author does not need to know the grammatical restrictions explicitly. ECOLE, a look-ahead text editor, indicates the restrictions while the specification is written. The controlled lexicon contains domain-specific content words that can be defined by the author on the fly and predefined function words. Specifications written in PENG can be deterministically translated into discourse representations structures to cope with anaphora and presuppositions and also into first-order predicate logic. To test the formal properties of PENG, we reformulated Schuberts steamroller puzzle in PENG, translated the resulting specification via discourse representation structures into first-order predicate logic with equality, and proved the steamrollers conclusion with OTTER, a standard theorem prover.

On controlled natural languages: properties and prospects

This collaborative report highlights the properties and prospects of Controlled Natural Languages (CNLs). The report poses a range of questions concerning the goals of the CNL, the design, the linguistic aspects, the relationships and evaluation of CNLs, and the application tools. In posing the questions, the report attempts to structure the field of CNLs and to encourage further systematic discussion by researchers and developers.

A Novel Architecture for Situation Awareness Systems

Situation Awareness (SA) is the problem of comprehending elements of an environment within a volume of time and space. It is a crucial factor in decision-making in dynamic environments. Current SA systems support the collection, filtering and presentation of data from different sources very well, and typically also some form of low-level data fusion and analysis, e.g., recognizing patterns over time. However, a still open research challenge is to build systems that support higher-level information fusion, viz., to integrate domain specific knowledge and automatically draw conclusions that would otherwise remain hidden or would have to be drawn by a human operator. To address this challenge, we have developed a novel system architecture that emphasizes the role of formal logic and automated theorem provers in its main components. Additionally, it features controlled natural language for operator I/O. It offers three logical languages to adequately model different aspects of the domain. This allows to build SA systems in a more declarative way than is possible with current approaches. From an automated reasoning perspective, the main challenges lay in combining (existing) automated reasoning techniques, from low-level data fusion of time-stamped data to semantic analysis and alert generation that is based on linear temporal logic. The system has been implemented and interfaces with Google-Earth to visualize the dynamics of situations and system output. It has been successfully tested on realistic data, but in this paper we focus on the system architecture and in particular on the interplay of the different reasoning components.

A controlled natural language layer for the semantic web

In this paper, I will show how a controlled natural language can be used to describe knowledge for the Semantic Web and discuss the formal properties of this language. At the first glance, the proposed controlled natural language looks like full English and can therefore be easily written and understood by non-specialists. However, its built-in grammatical and lexical restrictions, which are enforced by an intelligent authoring tool, guarantee that the language can be directly translated into description logic programs, i.e. the intersection of an expressive description logic with function-free logic programs. The controlled natural language can be used to make assertional and terminological statements as well as to specify rules for reasoning with the resulting assertional and terminological knowledge.

ExtrAns: Extracting answers from technical texts

Describes the ExtrAns answer-extraction system which uses logical forms and lexical relations for semantic representation, to delve into and leverage the meaning of sentences, phrases, and words.

Dynamic semantics at work

In this case study we show how an unambiguous semantic representation can be constructed dynamically in left-to-right order while a text is written in PENG, a controlled natural language designed for knowledge representation. PENG can be used in contexts where precise texts (e.g. software specifications, axioms for formal ontologies, legal documents) need to be composed. Texts written in PENG look seemingly informal and are easy to write and to read for humans but have first-order equivalent properties that make these texts computer-processable.

Working for Two: A Bidirectional Grammar for a Controlled Natural Language

This paper introduces the controlled natural language PENG Light together with a language processor that is based on a bidirectional grammar. The language processor has the following interesting properties: (a) it translates declarative sentences written in PENG Light into a first-order logic notation (TPTP); (b) it generates declarative sentences in PENG Light taking syntactically annotated TPTP formulas as input; and (c) it translates questions written in PENG Light into (conjunctive) queries in TPTP notation and uses the TPTP representation of the query as a starting point for generating answers in PENG Light. Moreover, the controlled natural language processor can be interfaced directly with an automated reasoner in order to resolve anaphoric references and to answer questions stated in PENG Light.

Representing Knowledge in Controlled Natural Language: A Case Study

In this case study I argue for the usage of a machine-oriented controlled natural language as interface language to knowledge systems. Instead of using formal languages that are difficult to learn and to remember for non-specialists, authors should be able to write specifications texts in a well-defined subset of English that can be unambiguously processed by a computer. This subset of computer-processable English (PENG) consists of a restricted grammar and lexicon and is used together with an intelligent text editor that guides the writing process. The editor of the PENG system communicates with a language processor that generates logical structures while the author writes a specification text. The language processor is connected via a server with reasoning services that allow for acceptability checking and question answering. Texts written in PENG look seemingly informal and are easy to write and understand for humans but have first-order equivalent properties.

A survey on real-time event detection from the Twitter data stream

The proliferation of social networking services has resulted in a rapid growth of their user base, spanning across the world. The collective information generated from these online platforms is overwhelming, in terms of both the amount of content produced every moment and the diversity of topics discussed. The real-time nature of the information produced by users has prompted researchers to analyse this content, in order to gain timely insight into the current state of affairs. Specifically, the microblogging service Twitter has been a recent focus of researchers to gather information on events occurring in real time. This article presents a survey of a wide variety of event detection methods applied to streaming Twitter data, classifying them according to shared common traits, and then discusses different aspects of the subtasks and challenges involved in event detection. We believe this survey will act as a guide and starting point for aspiring researchers to gain a structured view on state-of-the-art real-time event detection and spur further research in this direction.