Leveraging a Technical Domain Taxonomy to Enhance Collaboration, Knowledge Sharing and Operational Support

Perry, Philip Brian; Wise, W.V.; O'Neill, Dennis M.; Jensen, Mary

doi:10.2118/143908-ms

Cited by 6 publications

(4 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another paper discusses the problems of companies located in rural regions but demonstrates how a smart specialisation model ameliorates those issues (Olaniyi and Reidolf, 2015). Using a large technical community as an example, Perry et al (2011) argue for revitalizing the exploration and production taxonomy and demonstrate how problems can be solved with this concept. Lerro and Jacobone (2014) discuss the National Energy Technology District, which has impacted business, geographical and institutional factors.…”

Section: Qualitative Methodsmentioning

confidence: 99%

Evidence revisited: literature on smart specialisation calls for more mixed research designs

Fellnhofer

2017

IJKBD

View full text Add to dashboard Cite

show abstract

Section: Qualitative Methodsmentioning

confidence: 99%

Evidence revisited: literature on smart specialisation calls for more mixed research designs

Fellnhofer

2017

IJKBD

View full text Add to dashboard Cite

show abstract

“…The Integrated Operations in the High North (IOHN) project developed set of ontologies to drive integration effort [4]. Baker Hughes started extending their technical domain taxonomy based knowledge management system to next level with development of Ontology [30]. This ontology is based on send control vocabulary to classify metadata and provide advance search, filtering and navigation capability for unstructured information sources.…”

Section: Related Workmentioning

confidence: 99%

“…The role of domain taxonomies and ontologies are well recognized to enhance collaboration, knowledge sharing and operation support [30]. However, due to the large volumes of sources and inaccuracy of completely automated approaches calls for innovative approach towards this solution.…”

Section: Related Workmentioning

confidence: 99%

Semiautomatic, Semantic Assistance to Manual Curation of Data in Smart Oil Fields

Chelmis

Zhao

Sorathia

et al. 2012

SPE Western Regional Meeting

View full text Add to dashboard Cite

Vast volumes of data are continuously generated in smart oilfields from swarms of sensors. On one hand, increasing amounts of such data are stored in large data repositories and accessed over high-speed networks; On the other hand, captured data is further processed by different users in various analysis, prediction and domain-specific procedures that result in even larger volumes of derived datasets.The decision making process in smart oilfields relies on accurate historical, real-time or predicted datasets. However, the difficulty in searching for the right data mainly lies in the fact that data is stored in large repositories carrying no metadata to describe them. The origin or context in which the data was generated cannot be traced back, thus any meaning associated with the data is lost. Integrated views of data are required to make important decisions efficiently and effectively, but are difficult to produce; since data is being generated and stored in the repository may have different formats and schemata pertaining to different vendor products.In this paper, we present an approach based on Semantic Web Technologies that enables automatic annotation of input data with missing metadata, with terms from a domain ontology, which constantly evolves supervised by domain experts. We provide an intuitive user interface for annotation of datasets originating from the seismic image processing workflow. Our datasets contain models and different versions of images obtained from such models, generated as part of the oil exploration process in the oil industry. Our system is capable of annotating models and images with missing metadata, preparing them for integration by mapping such annotations. Our technique is abstract and may be used to annotate any datasets with missing metadata, derived from original datasets.The broader significance of this work is in the context of knowledge capturing, preservation and management for smart oilfields. Specifically our work focuses on extracting domain knowledge into collaboratively curated ontologies and using this information to assist domain experts in seamless data integration.

show abstract

“…The Integrated Operations in the High North (IOHN) project developed set of ontologies to drive integration effort [4]. Baker Hughes started extending their technical domain taxonomy based knowledge management system to next level with development of Ontology [25]. This ontology is based on send control vocabulary to classify metadata and provide advance search, filtering and navigation capability for unstructured information sources.…”

Section: Introductionmentioning

confidence: 99%

Toward an Automatic Metadata Management Framework for Smart Oil Fields

Chelmis

Zhao

Sorathia

et al. 2013

SPE Economics &Amp; Management

View full text Add to dashboard Cite

Vast volumes of data are continuously generated in smart oilfields from swarms of sensors. On one hand, increasing amounts of such data are stored in large data repositories and accessed over high-speed networks; On the other hand, captured data is further processed by different users in various analysis, prediction and domain-specific procedures that result in even larger volumes of derived datasets. The decision making process in smart oilfields relies on accurate historical, real-time or predicted datasets. However, the difficulty in searching for the right data mainly lies in the fact that data is stored in large repositories carrying no metadata to describe them. The origin or context in which the data was generated cannot be traced back, thus any meaning associated with the data is lost. Integrated views of data are required to make important decisions efficiently and effectively, but are difficult to produce; since data is being generated and stored in the repository may have different formats and schemata pertaining to different vendor products. In this paper, we present an approach based on Semantic Web Technologies that enables automatic annotation of input data with missing metadata, with terms from a domain ontology, which constantly evolves supervised by domain experts. We provide an intuitive user interface for annotation of datasets originating from the seismic image processing workflow. Our datasets contain models and different versions of images obtained from such models, generated as part of the oil exploration process in the oil industry. Our system is capable of annotating models and images with missing metadata, preparing them for integration by mapping such annotations. Our technique is abstract and may be used to annotate any datasets with missing metadata, derived from original datasets. The broader significance of this work is in the context of knowledge capturing, preservation and management for smart oilfields. Specifically our work focuses on extracting domain knowledge into collaboratively curated ontologies and using this information to assist domain experts in seamless data integration.

show abstract

Leveraging a Technical Domain Taxonomy to Enhance Collaboration, Knowledge Sharing and Operational Support

Cited by 6 publications

References 1 publication

Evidence revisited: literature on smart specialisation calls for more mixed research designs

Evidence revisited: literature on smart specialisation calls for more mixed research designs

Semiautomatic, Semantic Assistance to Manual Curation of Data in Smart Oil Fields

Toward an Automatic Metadata Management Framework for Smart Oil Fields

Contact Info

Product

Resources

About