Java-Based Regulation Broker

Liang, Vei-Chung; Garrett, James H.

doi:10.1061/(asce)0887-3801(2000)14:2(100)

Cited by 5 publications

(2 citation statements)

References 3 publications

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“…To overcome this situation, the level of formal definitions and foundations, what can be referenced as commitment in IFC will need to be raised and we recommend a formal definition for IFC extensions, by identifying the underlying concepts used to define the meaning of IFC entities, relations, attributes and rules, as these are the basis for the IFC structure. Garrett et al [23][24][25] utilizes text analysis algorithms to solve classification issues, whereas semi-automated approaches for the integration of project documents to model objects are also available [26]. The need for advancing formal methods for data collection, data mining, data analysis, and project planning and control are well documented [27].…”

Section: Discussionmentioning

confidence: 99%

An ontology-based analysis of the industry foundation class schema for building information model exchanges

Venugopal

Eastman

Teizer³

2015

Advanced Engineering Informatics

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Robust knowledge sharing frameworks between different stakeholders in a building project is of high priority. Industry Foundation Classes (IFC) provides a rich schema for interoperability through objectbased transactions. However, IFC lacks semantic clarity in mapping entities and relationships, resulting in multiple definitions to map the same information between different federated models. The objective of this research is to examine IFC from a perspective of an ontological framework, which can make the IFC definitions more formal, consistent and unambiguous. Different methods of ontological approaches to engineering knowledge are reviewed. Various issues such as the need for a logical framework, the current semantic approaches in the AEC/FM industry, and advantages of building an ontology structure are addressed. A comparative study of the ontology and segments of the existing IFC schema definition are performed. This exercise reveals the ambiguous nature of current IFC definitions and proposes reforms such that data exchanges would be more semantically robust. An ontology would structure the overall interoperability of BIM tools by providing a formal and consistent taxonomy and classification structure for extending IFC and for defining subsets as model view definitions (MVD).

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Section: Discussionmentioning

confidence: 99%

An ontology-based analysis of the industry foundation class schema for building information model exchanges

Venugopal

Eastman

Teizer³

2015

Advanced Engineering Informatics

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“…The recent increase in network capacity has given rise to the proposal of a web-based broker for regulations [17]. Data mining techniques, in particularly text mining algorithms, are sought to perform classification and clustering on legal documents [27].…”

Section: Related Workmentioning

confidence: 99%

Similarity analysis on government regulations

Lau

Law

Wiederhold

2003

Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Government regulations are semi-structured text documents that are often voluminous, heavily cross-referenced between provisions and even ambiguous. Multiple sources of regulations, like those from federal, state, and local offices, lead to difficulties in both understanding and complying with all applicable codes. In this work, we propose a framework for regulation management and similarity analysis. An online repository for legal documents is created with the help of text mining tool, and users can access regulatory documents either through the natural hierarchy of provisions or from a taxonomy based on concepts generated by knowledge engineers. Our similarity analysis core identifies relevant provisions and brings them to the user's attention, and this is performed by utilizing both the structure and referencing of regulations to provide a better comparison between provisions. Preliminary results show that our system reveals hidden similarities between provisions that are not identified using traditional comparison techniques.

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