scholarly journals A $$\textit{Datalog}{\pm }$$ Domain-Specific Durum Wheat Knowledge Base

2016 ◽  
pp. 132-143
Author(s):  
Abdallah Arioua ◽  
Patrice Buche ◽  
Madalina Croitoru
Keyword(s):  
Knowledge Base ◽  
Durum Wheat ◽  
Domain Specific

scholarly journals RapidRadio: A Domain-Specific Productivity Enhancing Framework

2010 ◽  
Vol 2010 ◽  
pp. 1-15
Author(s):  
Jorge A. Surís ◽  
Adolfo Recio ◽  
Peter Athanas
Keyword(s):  
Knowledge Base ◽  
Signal Classification ◽  
Specific Productivity ◽  
Domain Specific ◽  
Ultimate Objective

The RapidRadio framework for signal classification and receiver deployment is discussed. The framework is a productivity enhancing tool that reduces the required knowledge-base for implementing a receiver on an FPGA-based SDR platform. The ultimate objective of this framework is to identify unknown signals and to build FPGA-based receivers capable of receiving them. The architecture of the receiver deployed by the framework and its implementation are discussed. The framework's capacity to classify a signal and deploy a functional receiver is validated with over-the-air experiments.

Workflows Científicos com Apoio de Bases de Conhecimento em Tempo Real

2020 ◽  
Author(s):  
Victor S. Bursztyn ◽  
Jonas Dias ◽  
Marta Mattoso
Keyword(s):  
Knowledge Base ◽  
Domain Knowledge ◽  
Large Scale ◽  
Workflow Engine ◽  
Workflow Execution ◽  
Human In The Loop ◽  
Domain Specific ◽  
Political Sciences ◽  
Provenance Data ◽  
Domain Specific Knowledge

One major challenge in large-scale experiments is the analytical capacity to contrast ongoing results with domain knowledge. We approach this challenge by constructing a domain-specific knowledge base, which is queried during workflow execution. We introduce K-Chiron, an integrated solution that combines a state-of-the-art automatic knowledge base construction (KBC) system to Chiron, a well-established workflow engine. In this work we experiment in the context of Political Sciences to show how KBC may be used to improve human-in-the-loop (HIL) support in scientific experiments. While HIL in traditional domain expert supervision is done offline, in K-Chiron it is done online, i.e. at runtime. We achieve results in less laborious ways, to the point of enabling a breed of experiments that could be unfeasible with traditional HIL. Finally, we show how provenance data could be leveraged with KBC to enable further experimentation in more dynamic settings.

scholarly journals Diffusion enables integration of heterogeneous data and user-driven learning in a desktop knowledge-base

2021 ◽  
Vol 17 (8) ◽  
pp. e1009283
Author(s):  
Tomasz Konopka ◽  
Sandra Ng ◽  
Damian Smedley
Keyword(s):  
Knowledge Base ◽  
Nearest Neighbor ◽  
Heterogeneous Data ◽  
Data Types ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Heterogeneous Datasets ◽  
Domain Specific Knowledge ◽  
High Throughput Experiments ◽  
Gene Symbols

Integrating reference datasets (e.g. from high-throughput experiments) with unstructured and manually-assembled information (e.g. notes or comments from individual researchers) has the potential to tailor bioinformatic analyses to specific needs and to lead to new insights. However, developing bespoke analysis pipelines from scratch is time-consuming, and general tools for exploring such heterogeneous data are not available. We argue that by treating all data as text, a knowledge-base can accommodate a range of bioinformatic data types and applications. We show that a database coupled to nearest-neighbor algorithms can address common tasks such as gene-set analysis as well as specific tasks such as ontology translation. We further show that a mathematical transformation motivated by diffusion can be effective for exploration across heterogeneous datasets. Diffusion enables the knowledge-base to begin with a sparse query, impute more features, and find matches that would otherwise remain hidden. This can be used, for example, to map multi-modal queries consisting of gene symbols and phenotypes to descriptions of diseases. Diffusion also enables user-driven learning: when the knowledge-base cannot provide satisfactory search results in the first instance, users can improve the results in real-time by adding domain-specific knowledge. User-driven learning has implications for data management, integration, and curation.

scholarly journals End-user development of knowledge bases for semi-automated formation of task cards

2021 ◽  
Author(s):  
N.O. Dorodnykh ◽  
Y.V. Kotlov ◽  
O.A. Nikolaychuk ◽  
V.M. Popov ◽  
A.Y. Yurin
Keyword(s):  
Knowledge Base ◽  
Intelligent Systems ◽  
Knowledge Bases ◽  
Visual Programming ◽  
Event Tree ◽  
End User ◽  
Model Driven ◽  
Domain Specific ◽  
Problem Situations ◽  
Event Trees

The complexity of creating artificial intelligence applications remains high. One of the factors that cause such complexity is the high qualification requirements for developers in the field of programming. Development complexity can be reduced by using methods and tools based on a paradigm known as End-user development. One of the problems that requires the application of the methods of this paradigm is the development of intelligent systems for supporting the search and troubleshooting onboard aircraft. Some tasks connected with this problem are identified, including the task of dynamic formation of task cards for troubleshooting in terms of forming a list of operations. This paper presents a solution to this problem based on some principles of End-user development: model-driven development, visual programming, and wizard form-filling. In particular, an extension of the Prototyping expert systems based on transformations technology, which implements the End-user development, is proposed in the context of the problem to be solved for Sukhoi Superjet aircraft. The main contribution of the work is as follows: expanded the main technology method by supporting event trees formalism (as a popular expert method for formalizing scenarios for the development of problem situations and their localization); created a domain-specific tool (namely, Extended event tree editor) for building standard and extended event trees, including for diagnostic tasks; developed a module for supporting transformations of XML-like event tree representation format for the knowledge base prototyping system – Personal knowledge base designer. A description of the proposed extension and the means of its implementation, as well as an illustrative example, are provided.

Knowledge Extraction from Domain-Specific Documents

2017 ◽  
pp. 106-123
Author(s):  
Ram Kumar ◽  
Shailesh Jaloree ◽  
R. S. Thakur
Keyword(s):  
Knowledge Representation ◽  
Knowledge Base ◽  
Knowledge Bases ◽  
Domain Ontology ◽  
Knowledge Based Systems ◽  
Domain Specific ◽  
Knowledge Based ◽  
Representation Systems ◽  
Terminological Confusion ◽  
Knowledge Representation Systems

Knowledge-based systems have become widespread in modern years. Knowledge-base developers need to be able to share and reuse knowledge bases that they build. As a result, interoperability among different knowledge-representation systems is essential. Domain ontology seeks to reduce conceptual and terminological confusion among users who need to share various kind of information. This paper shows how these structures make it possible to bridge the gap between standard objects and Knowledge-based Systems.

scholarly journals Deep Learning-Based Knowledge Graph Generation for COVID-19

2021 ◽  
Vol 13 (4) ◽  
pp. 2276
Author(s):  
Taejin Kim ◽  
Yeoil Yun ◽  
Namgyu Kim
Keyword(s):  
Knowledge Base ◽  
Language Model ◽  
Fine Tuning ◽  
Knowledge Graph ◽  
Specific Information ◽  
Specific Knowledge ◽  
Text Documents ◽  
Domain Specific ◽  
Open Information Extraction ◽  
Domain Specific Knowledge

Many attempts have been made to construct new domain-specific knowledge graphs using the existing knowledge base of various domains. However, traditional “dictionary-based” or “supervised” knowledge graph building methods rely on predefined human-annotated resources of entities and their relationships. The cost of creating human-annotated resources is high in terms of both time and effort. This means that relying on human-annotated resources will not allow rapid adaptability in describing new knowledge when domain-specific information is added or updated very frequently, such as with the recent coronavirus disease-19 (COVID-19) pandemic situation. Therefore, in this study, we propose an Open Information Extraction (OpenIE) system based on unsupervised learning without a pre-built dataset. The proposed method obtains knowledge from a vast amount of text documents about COVID-19 rather than a general knowledge base and add this to the existing knowledge graph. First, we constructed a COVID-19 entity dictionary, and then we scraped a large text dataset related to COVID-19. Next, we constructed a COVID-19 perspective language model by fine-tuning the bidirectional encoder representations from transformer (BERT) pre-trained language model. Finally, we defined a new COVID-19-specific knowledge base by extracting connecting words between COVID-19 entities using the BERT self-attention weight from COVID-19 sentences. Experimental results demonstrated that the proposed Co-BERT model outperforms the original BERT in terms of mask prediction accuracy and metric for evaluation of translation with explicit ordering (METEOR) score.

scholarly journals Software Process Ontology: A case study of software organisations software process sub domains

2019 ◽  
pp. 122-130
Author(s):  
R. O. Oveh ◽  
O. Efevberha-Ogodo ◽  
F. A. Egbokhare
Keyword(s):  
Knowledge Base ◽  
Software Process ◽  
Formal Representation ◽  
Specific Knowledge ◽  
Process Ontology ◽  
Process Knowledge ◽  
Domain Specific ◽  
Domain Specific Knowledge ◽  
Intellectual Knowledge

In a domain like software process that is intensively knowledge driven, transforming intellectual knowledge by formal representation is an invaluable requirement. An improved use of this knowledge could lead to maximum payoff in software organisations which is key. The purpose of formal representation is to help organisations achieve success by modelling successful organisations. In this paper, Software process knowledge from successful organisations was harvested and formally modeled using ontology. Domain specific knowledge base ontology was produced for core software process subdomain, with its resulting software process ontology produced.

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