Due to the emergence of digital revolution and competitiveness in recent decades, almost all organizations and industries intend to develop solutions to extract information from unstructured documents. These documents comprise of information related to multiple divergent domains, and therefore, there is a need of a multidomain knowledge base. Since recent research works suggest ontology as the predominant model, it is proposed to evolve a unified ontology modeling approach with multiple layers and divergent domains to support information processing from unstructured documents. The model is evolved by integrating relevant domains to facilitate cross domain query. Further as the features of unstructured documents span across multiple domains, domain identification is to be performed prior to any information processing. Hence, an attempt is made to identify the domain using the proposed ontology model. The proposed ontology is developed for the Thermal Power Plant Industry and domain identification is demonstrated with an example. A statistical similarity index is proposed to associate divergent volatile features of unstructured text with ontology knowledge for domain identification. The outcome of the proposal is evaluated using the proposed similarity index. A subsequent study to extract information using classified content with the support of directed acyclic graph relationship is under progress. The merit of the proposal is its ability to extend its usage across multiple stages of information processing with distinctive purpose.

A Multidomain Layered Approach in Development of Industrial Ontology to Support Domain Identification for Unstructured Text

Improving and assessing knowledge on a topic is need of the current generation. In the traditional system, experts create assessments manually by reading articles and generating questions on a topic. However, it is very effort and time consuming to read and create questions for every article. Therefore, learners find it difficult to assess their knowledge on a topic owing to no- or low-availability of assessments. Usually, they rely on books or question banks. With fast updating technology and availability of good web resources, the way assessments are created may be improved. This paper introduces a system for self-assessment using Wikipedia article as a source of data for the assessment. The system generates a set of questions with appropriate choices by searching for an article from the Wikipedia. This work primarily focuses on identifying locations and numbers to generate questions using regular expressions and parts of speech (POS) tagging. The quality of generated questions, in terms of precision, differs from article to article. The precision is very high in case of popular articles whereas, for not so popular articles it is moderate.

Web authoriser tool to build assessments using Wikipedia articles

This is a thought piece on data-intensive science requirements for databases and science centers. It argues that peta-scale datasets will be housed by science centers that provide substantial storage and processing for scientists who access the data via smart notebooks. Next-generation science instruments and simulations will generate these peta-scale datasets. The need to publish and share data and the need for generic analysis and visualization tools will finally create a convergence on common metadata standards. Database systems will be judged by their support of these metadata standards and by their ability to manage and access peta-scale datasets. The procedural stream-of-bytes-file-centric approach to data analysis is both too cumbersome and too serial for such large datasets. Non-procedural query and analysis of schematized self-describing data is both easier to use and allows much more parallelism.

pdf/scientific-data-management-in-the-coming-decade-3v13doph88.pdf

Scientific Data Management in the Coming Decade

The explosion of data over the past twenty years has fostered a huge amount of research in processing semi-structured documents like HTML and XML documents on Web. Nevertheless, the explosion of semi-structured documents that originate from outside the Web domain is more challenging. The data of semi-structured documents are everywhere: in scientific research reports, official journals, electronic health records and any records from other domains. Different from HTML and XML, semi-structured record is usually human-readable and has its own internal schema. On account of the incredible increase of data volumes, the traditional methods of studying on these data cannot meet the needs of high performance and flexible access. Relational database technique is a good alternative technique for managing and organizing data. In this paper, we aim at (1) revealing the semantic structure in human-readable scientific records (HSR), and (2) presenting a transformation system that converts HSR into structured relational database. Our approach identifies the relevant logical sub-structures and objects in HSR according to specific templates. We don't target “the best” methods but provide an available framework for facilitating the semi-structured HSR analysis tasks. The evaluation to the system shows its ability to enable the efficient transformation of HSR and therefore contributes to the increasing corpus of semi-structured HSR documents.

A rule-based information extraction system for human-readable semi-structured scientific documents

The variability and uncertainty of modern industrial control make the software framework of control system pay more attention on the design and re-engineering of control process. Establishing a reasonable control process model is essential to carry out re-engineering of control process components, which can describe control-related information and relationship. In order to ensure that the model can be correctly interpreted and implemented by the control software framework, a novel control process model based on ECA rules and process decomposition is proposed. The model mainly focuses on the execution dependencies between control flow activities and proposes the concepts of sequence dependency, data dependency, trigger dependency and resource dependency. In addition, the proposed process decomposition model can effectively support the hierarchical design and management of the control process. The successful application of this model in large laser facility indicates its reliability and effectiveness.

Control Process Modeling and Execution Based on ECA Rules and Process Decomposition*

There are few data sources for fault diagnosis of stepper motor motion control systems, conventional voltage, current, vibration signals cannot well characterize stepper motor faults, and the fault diagnosis capabilities of stepper motor controllers on the market are limited. In view of the problem that the stepper motor data source is limited, this paper uses the stepper motor load value to provide an effective data source for fault diagnosis and condition monitoring. The time series decomposition of motor load value data is carried out, the time series data is analyzed by GRU recurrent neural network algorithm, fault classification is performed, and a GRU stepper motor fault diagnosis model is constructed to conduct online self-diagnosis of intermittent vibration, instantaneous overload and connection loosening fault, and fault prediction is carried out on motor overload stall fault. 

Modeling of Stepper Motor Fault Diagnosis Based on GRU Time Series Analysis

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A rule-based information extraction system for human-readable semi-structured scientific documents

Control Process Modeling and Execution Based on ECA Rules and Process Decomposition*

Modeling of Stepper Motor Fault Diagnosis Based on GRU Time Series Analysis