Controlled English to facilitate human/machine analytical processing

Braines, Dave; Mott, David; Laws, Simon M.; Mel, Geeth de; Pham, Tien

doi:10.1117/12.2015907

Cited by 13 publications

(7 citation statements)

References 3 publications

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“…Numerous standard languages already exist to capture such domain models -OWL2 5 , Predicate logic 6 , Controlled Natural Languages (CNL) 7 , to name a few. In this work we have used ITA Controlled English (CE) 23 as the medium to specify the domain models. The decision was made due to the following reasons:…”

Section: Methodsmentioning

confidence: 99%

Context-rich semantic framework for effective data-to-decisions in coalition networks

et al. 2013

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In a coalition context, data fusion involves combining of soft (e.g., field reports, intelligence reports) and hard (e.g., acoustic, imagery) sensory data such that the resulting output is better than what it would have been if the data are taken individually. However, due to the lack of explicit semantics attached with such data, it is difficult to automatically disseminate and put the right contextual data in the hands of the decision makers. In order to understand the data, explicit meaning needs to be added by means of categorizing and/or classifying the data in relationship to each other from base reference sources.In this paper, we present a semantic framework that provides automated mechanisms to expose real-time raw data effectively by presenting appropriate information needed for a given situation so that an informed decision could be made effectively. The system utilizes controlled natural language capabilities provided by the ITA (International Technology Alliance) Controlled English (CE) toolkit to provide a human-friendly semantic representation of messages so that the messages can be directly processed in human/machine hybrid environments. The Real-time Semantic Enrichment (RTSE) service adds relevant contextual information to raw data streams from domain knowledge bases using declarative rules. The rules define how the added semantics and context information are derived and stored in a semantic knowledge base. The software framework exposes contextual information from a variety of hard and soft data sources in a fast, reliable manner so that an informed decision can be made using semantic queries in intelligent software systems.

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Section: Methodsmentioning

confidence: 99%

Context-rich semantic framework for effective data-to-decisions in coalition networks

et al. 2013

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“…One of the guiding principles of the CE language is that it should be aimed at the business user and should use the language of their domain of interest rather than forcing them to use ontological terms and set-theoretic expressions [2] . This is motivated from the desire to appeal directly to non-technical users rather than technical specialists and knowledge engineers and in this paper we extend this principle to ontology extension as well, specifically in the assertion that many "simple" ontology extensions could provide significant benefit to a human/machine hybrid system.…”

Section: Extending Ontologies Through Conversationmentioning

confidence: 99%

“…In traditional systems ontologies can be complex to develop and understand and are usually the focus of technical specialists and knowledge engineers rather that business-focused "end users". In our previous work we have experimented with Controlled Natural Languages [1] , specifically ITA Controlled English (CE) [2] in an attempt to present ontological information in a more human-friendly, consumable form, aimed directly at non-technical business users. In our most recent work we have demonstrated a full natural language conversational capability [3] that enables human users to interact with a Controlled Natural Language Knowledge Base in their own natural language (e.g.…”

Section: Introductionmentioning

confidence: 99%

Agile development of ontologies through conversation

et al. 2016

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Ontologies and semantic systems are necessarily complex but offer great potential in terms of their ability to fuse information from multiple sources in support of situation awareness. Current approaches do not place the ontologies directly into the hands of the end user in the field but instead hide them away behind traditional applications. We have been experimenting with human-friendly ontologies and conversational interactions to enable non-technical business users to interact with and extend these dynamically. In this paper we outline our approach via a worked example, covering: OWL ontologies, ITA Controlled English, Sensor/mission matching and conversational interactions between human and machine agents.

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“…The DAIS ITA Science Library is a publicly available website 30 that lists the publications of the DAIS ITA research program (with a second site providing the same experience for the earlier NIS ITA research program 31 ). The data for the science library is held as a semantic graph using "ITA Controlled English" (CE), 32 providing an interesting and novel opportunity in this research since semantic graphs have not been a common focus in existing network motif analysis research. The semantic graph for the science library is comprised of instances (nodes) of given concepts (types or classes), connected together via named relationships (links), and with attribute values (properties).…”

Section: Example Dataset: Science Librarymentioning

confidence: 99%

The role of motifs in understanding behavior in social and engineered networks

Braines

Felmlee

Towsley

et al. 2018

Next-Generation Analyst VI

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Within networks one can identify motifs that are significant recurring patterns of interaction between nodes. Here motifs are sub-graphs that occur more frequently than would be explained by random connections. Graphs can be used to model internal network structures of human groups, or links between groups, with group dynamics being governed by these structures. Graphs can also model behavior in engineered systems, and internal network structures can significantly affect dynamic behavior. A graph may only be partially visible (such as in hostile or coalition environments), however detectable network motifs may in some cases be reflective of the entire graph. We outline a research plan and describe basic network motifs and their properties, along with current analytic techniques for static and dynamic settings. We offer suggestions as to how network motif techniques can be applied to intra-or inter-group behavior, for example to detect whether multiple groups behave as a cooperative alliance, or whether coalition networks inter-operate in positive ways. As an example, we examine a complex time-series graph dataset relevant to coalition focused aspects of the class of networks under study, specifically related to the social network resulting from the authorship of academic papers within a coalition. We provide details of the basic analysis of this network over time and outline how this can be used as one of the datasets for our planned network motif research activities, especially with regards to the temporal and evolutionary aspects.

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Controlled English to facilitate human/machine analytical processing

Cited by 13 publications

References 3 publications

Context-rich semantic framework for effective data-to-decisions in coalition networks

Context-rich semantic framework for effective data-to-decisions in coalition networks

Agile development of ontologies through conversation

The role of motifs in understanding behavior in social and engineered networks

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