BCLEARer
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bCLEARer is a model-driven approach to mining the ontology/semantics from existing systems.
History[edit]
BORO Solutions has been developing an approach to semantic interoperability and legacy modernisation since the late 80s. bCLEARer is the latest incarnation of this approach, which has been in development since 2012. It can be used to support ontology mining. It has been extensively used to support modernization of legacy data using BORO as the top level ontology..[1][2] Publicly available examples of its use in mining ontologies include:
1) Energy Sector - Master Data Foundation - where it was named CLEAN and was used for data migration to new systems and well as improving data quality within a system.[3]
2) Manufacturing - Contact Lenses - used to recover the core ontology for the manufacturing process.[4]
3) Coordinate Systems and Spatial Data - used for developing a common foundation for spatial/temporal data.[5]
4) Construction - Uniclass - where it was used to refactor Uniclass the 2015 classification system used for construction industries..[6] Some of the code for this is available for open source use.[1].
5) Addresses - where it was used for extracting all the names from UK Ordnance Survey Open Names. The code for this was open sourced.[2]
Description[edit]
The full bCLEARer process mines the underlying ontology from multiple data sources (typically business applications), providing a basis for semantic interoperability and legacy modernisation.
The full bCLEARer has five stages:
- Collect - select the data; establish the broad scope
- Load - structure the data
- Evolve - (foundationally) ontologise the data
- Assimilate - integrate into the global repository
- Reuse - publish data in reuse format
The bCLEARer approach can be seen as a way to increase the semantic maturity of the data. It is meant to expose the meta-ontological choices made implicitly in the data.[7] It is iterative, where the results of each iteration can also be fed back into the source systems to update the data and issues identified during the process, and this can continue until the data is sufficiently mature. The global repository where the evolved ontologised data is stored can contain a foundational ontology that is used to enable semantic integration between the different datasets assimilated in each cycle and from different sources.
References[edit]
- ↑ Partridge, Chris (2016). "BORO Foundational Ontology's Meta-ontological Choices". Onto.Com 2016, Co-located with FOIS 2016.
- ↑ Partridge, Chris; Mitchell, Andrew; de Cesare, Sergio (2019). "Grounding for an Enterprise Computing Nomenclature Ontology". International Conference on Conceptual Modeling. Lecture Notes in Computer Science. 11788: 457–465. doi:10.1007/978-3-030-33223-5_38. ISBN 978-3-030-33222-8. Unknown parameter
|s2cid=ignored (help) - ↑ "Tullow's Master Data". Oil Information Technology Journal. 18 (197). December 2013.
- ↑ Atkinson, Hayden (September 2018). The Manufacturing Domain Ontology for simplifying interoperability of systems for Contact Lens Manufacture (PDF) (Thesis). Brunel University London.
- ↑ Partridge, Chris (April 2021). A 4-Dimensionalist Top Level Ontology (TLO): Mereotopology and Space-Time. 4-Dimensionalism in Large Scale Data Sharing and Integration Newton Gateway to Mathematics.
- ↑ Partridge, Chris; Mitchell, Andrew; da Silva, Marco; Xiberta-Soto, Oscar; West, Matthew; Khan, Mesbah A.; de Cesare, Sergio (2020). "Implicit Requirements for Ontological Multi-Level Types in the UNICLASS Classification". 7th International Workshop on Multi-Level Modelling.
- ↑ Partridge, Chris (June 2016). BORO Foundational Ontology's Meta-ontological Choices. Onto.Com 2016 (PDF).
External links[edit]
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