Use Of Semantic Networks Research Articles

Connecting Multilayer Semantic Networks to Data Lakes: The Representation of Data Uncertainty and Quality

SummaryDrilling oil and gas wells is a complex process involving many disciplines and stakeholders. This process occurs in a context where some pieces of information are unknown, or are often incomplete, erroneous, or at least uncertain. Yet, during drilling engineering and construction of a well, drilling data quality and uncertainty are barely addressed in an auditable and scientific way. Currently, there are few or no placeholders in engineering and operational databases to document uncertainty and its propagation. The Society of Petroleum Engineers (SPE) has formed a cross-disciplinary technical subcommittee to investigate how to describe and propagate drilling data quality and uncertainty. The subcommittee is a cooperation between the drilling system automation, wellbore positioning, and drilling uncertainty prediction technical sections. As the topic is vast and complex, a systematic method was adopted, where multiple user stories or pain points were generated and ranked with the most compelling user story analyzed in detail. From this approach, a series of multidisciplinary workflows (drilling data generators) can now be captured and described in terms of data quality and propagation of uncertainty. The paper presents details of one user story focused on capturing the description of the quality and uncertainty of depth measurements. Multiple use cases have been extracted from this single user story exemplifying how multiple stakeholders and disciplines manage, communicate, and understand the notion of wellbore depth and its relative uncertainty. Current data stores have the main objective of recording the results of processes but have very limited capabilities to store how the interdisciplinary processes generated and cross-related these results. The study explores the use of semantic networks to capture those multidisciplinary data relationships. A minimum vocabulary has been created using just a few tens of concepts that has sufficient expressiveness to describe all the extracted use cases, showing that the semantic network method has the potential to describe a broad range of complex drilling-related processes. The study also demonstrates that use of a multilayered graph, employing other notions that do not expressly refer to the processes that generated the data, can capture the description of how uncertainty propagates between each of those concepts.

Semantic Networks for Engineering Design: State of the Art and Future Directions

Abstract In the past two decades, there has been increasing use of semantic networks in engineering design for supporting various activities, such as knowledge extraction, prior art search, idea generation and evaluation. Leveraging large-scale pre-trained graph knowledge databases to support engineering design-related natural language processing (NLP) tasks has attracted a growing interest in the engineering design research community. Therefore, this paper aims to provide a survey of the state-of-the-art semantic networks for engineering design and propositions of future research to build and utilize large-scale semantic networks as knowledge bases to support engineering design research and practice. The survey shows that WordNet, ConceptNet and other semantic networks, which contain common-sense knowledge or are trained on non-engineering data sources, are primarily used by engineering design researchers to develop methods and tools. Meanwhile, there are emerging efforts in constructing engineering and technical-contextualized semantic network databases, such as B-Link and TechNet, through retrieving data from technical data sources and employing unsupervised machine learning approaches. On this basis, we recommend six strategic future research directions to advance the development and uses of large-scale semantic networks for artificial intelligence applications in engineering design.

Issues of Upgrading Study Courses and Use of Semantic Networks as a Means for Their Evaluation

In this paper the issues of upgrading study courses at the Faculty of Computer Science and Information Technology at Riga Technical University and means of their description and evaluation are described. In connection with changes of objectives and methods of education, introduction of new requirements necessity of better structuring and classification concepts in the process of design of study courses arises. The aim of this work was to implement in education such teaching technology that would provide informational and methodological support of the qualitative educational process. For several years, the authors have been researching the way changing and supplementing study courses affects academic performance of students and the quality of training. In this paper, the authors attempt to find a solution for the evaluation of study course quality and success of it modification when changing course content and teaching methodology using quantitative metrics of describing study course semantic network graph.

Semantic networks as a component of the unified model of knowledge representation in intellectual systems

The article considering the use of semantic networks in a unified system of knowledge representation. Consideration of the general structure of the information loop, which provides process control. The necessity of unification of representation of knowledge management for building effective intelligent decision support systems in the information technology systems of the control loop.

A novel adaptation approach for electromagnetic device optimization

A novel adaptation approach for electromagnetic device optimizationThe ability of case-based reasoning systems to solve new problems mainly depends on their case adaptation knowledge and adaptation strategies. In order to carry out a successful case adaptation in our case-based reasoning system for a low frequency electromagnetic device design, we make use of semantic networks to organize related domain knowledge, and then construct a rule-based inference system which is based on the network. Furthermore, based on the inference system, a novel adaptation algorithm is proposed to derive a new device case from a real-world induction motor case-base with high dimensionality.

АдаптивнІ семантичнІ моделІ представленнЯ тА контролЮ знанЬ

The paper presented research models of knowledge representation. These models help to organize the industry. They are building related conceptual units (semantic networks). We consider the practical use of semantic networks for learning and knowledge control

Do children "DRM" like adults? False memory production in children.

The Deese/Roediger-McDermott (DRM) paradigm was used to investigate developmental trends in accurate and false memory production. In Experiment 1, DRM lists adjusted to be more consistent with children's vocabulary were used with 2nd graders, 8th graders, and college students. Accurate and false recall and recognition increased with age, but semantic information appeared to be available to all age groups. Experiment 2 created a set of child-generated lists based on the free associations by a group of 3rd graders to critical items. The child-generated associates were different from those generated by adults; long and short versions of the child-generated lists were therefore presented to 2nd, 5th, and 8th graders and college students in Experiment 3. Second graders exhibited few false memories, whereas 5th graders were similar to adults in low-demand conditions and more similar to younger children in high-demand conditions. Findings are discussed in terms of developmental changes in automatic and effortful processing and the use of semantic networks.

Integration of Biological Data with Semantic Networks

In recent years, the broad utilization of high-throughput experimental techniques resulted in a vast amount of expression and interaction data, accompanied by information on metabolic, cell signaling and gene regulatory pathways accumulated in the literature and databases. Thus, one of the major goals of modern bioinformatics is to process and integrate heterogeneous biological data to provide an insight into the inner workings of a cell governed by complex interaction networks. The paper reviews the current development of semantic network (SN) technologies and their applications to the integration of genomic and proteomic data. We also elaborate on our own work that applies a semantic network approach to modeling complex cell signaling pathways and simulating the cause-effect of molecular interactions in human macrophages. The review is concluded with a discussion of the prospective use of semantic networks in bioinformatics practice as an efficient and general language for data integration, knowledge representation and inference. Keywords: Semantic networks, biological data integration, protein interactions, knowledge representation, ontology, semantic web

The use of semantic networks to support concurrent engineering in semiconductor product development

The design of semiconductor devices is an extremely complex and costly process. Numerous design and test iterations are typically necessary to finally complete a successful device. Competition in the industry has forced semiconductor manufacturers to reduce design cycle times and costs. One method now being used to accomplish these objectives is concurrent engineering. This paper will review how concurrent engineering is being integrated into semiconductor device development and how artificial intelligence-based models will support concurrent engineering implementation. Major changes are needed in design simulation, methods of knowledge sharing, and incorporating best practices. A semantic network is proposed that retains the knowledge of a product in a central repository as various engineers contribute to the product's development. The knowledge contained in this central repository can be referenced for applicability by engineers during product design, development, and production.