Building Model Object Classification for Semantic Enrichment Using Geometric Features and Pairwise Spatial Relations

Semantic enrichment is a process of supplementing/correcting information in a poorly prepared BIM model. Object classifications are essential information, but are commonly missing or incorrectly represented when transferring a BIM model or creating a model using tools customized for other domains in design. Automated compilation of 'as-is' BIM models from point cloud data also requires object classification, as well as 3D reconstruction. We present a systematic approach to classifying objects in a BIM model, for use in future semantic enrichment systems. Previous work on object classification in BIM model enrichment was restricted by its limited ability to accurately interpret geometric and spatial features and by the constraints of Boolean logic rules and the rule compilation process. To address these issues, we propose a procedure for establishing a knowledge base that associates objects with their features and relationships, and a matching algorithm based on a similarity measurement between the knowledge base and facts. An implementation on a synthetic bridge model shows that whereas some objects can be classified by shape features alone, most objects require the use of spatial relations for unique classification. Spatial context is more likely to uniquely identify an object than shape features are

Building Model Object Classification for Semantic Enrichment Using Geometric Features and Pairwise Spatial Relationships

Semantic enrichment is a process of supplementing/correcting information in a poorly prepared BIM model. Object classifications are essential information, but are commonly missing or incorrectly represented when transferring a BIM model or creating a model using tools customized for other domains in design. Automated compilation of 'as-is' BIM models from point cloud data also requires object classification, as well as 3D reconstruction. We present a systematic approach to classifying objects in a BIM model, for use in future semantic enrichment systems. Previous work on object classification in BIM model enrichment was restricted by its limited ability to accurately interpret geometric and spatial features and by the constraints of Boolean logic rules and the rule compilation process. To address these issues, we propose a procedure for establishing a knowledge base that associates objects with their features and relationships, and a matching algorithm based on a similarity measurement between the knowledge base and facts. An implementation on a synthetic bridge model shows that whereas some objects can be classified by shape features alone, most objects require the use of spatial relations for unique classification. Spatial context is more likely uniquely identify an object than shape features are

Trustworthy Refactoring via Decomposition and Schemes: A Complex Case Study

Widely used complex code refactoring tools lack a solid reasoning about the correctness of the transformations they implement, whilst interest in proven correct refactoring is ever increasing as only formal verification can provide true confidence in applying tool-automated refactoring to industrial-scale code. By using our strategic rewriting based refactoring specification language, we present the decomposition of a complex transformation into smaller steps that can be expressed as instances of refactoring schemes, then we demonstrate the semi-automatic formal verification of the components based on a theoretical understanding of the semantics of the programming language. The extensible and verifiable refactoring definitions can be executed in our interpreter built on top of a static analyser framework.Comment: In Proceedings VPT 2017, arXiv:1708.0688

Enterprise application reuse: Semantic discovery of business grid services

Web services have emerged as a prominent paradigm for the development of distributed software systems as they provide the potential for software to be modularized in a way that functionality can be described, discovered and deployed in a platform independent manner over a network (e.g., intranets, extranets and the Internet). This paper examines an extension of this paradigm to encompass ‘Grid Services’, which enables software capabilities to be recast with an operational focus and support a heterogeneous mix of business software and data, termed a Business Grid - "the grid of semantic services". The current industrial representation of services is predominantly syntactic however, lacking the fundamental semantic underpinnings required to fulfill the goals of any semantically-oriented Grid. Consequently, the use of semantic technology in support of business software heterogeneity is investigated as a likely tool to support a diverse and distributed software inventory and user. Service discovery architecture is therefore developed that is (a) distributed in form, (2) supports distributed service knowledge and (3) automatically extends service knowledge (as greater descriptive precision is inferred from the operating application system). This discovery engine is used to execute several real-word scenarios in order to develop and test a framework for engineering such grid service knowledge. The examples presented comprise software components taken from a group of Investment Banking systems. Resulting from the research is a framework for engineering servic

Information Extraction, Data Integration, and Uncertain Data Management: The State of The Art

Information Extraction, data Integration, and uncertain data management are different areas of research that got vast focus in the last two decades. Many researches tackled those areas of research individually. However, information extraction systems should have integrated with data integration methods to make use of the extracted information. Handling uncertainty in extraction and integration process is an important issue to enhance the quality of the data in such integrated systems. This article presents the state of the art of the mentioned areas of research and shows the common grounds and how to integrate information extraction and data integration under uncertainty management cover

Using Natural Language as Knowledge Representation in an Intelligent Tutoring System

Knowledge used in an intelligent tutoring system to teach students is usually acquired from authors who are experts in the domain. A problem is that they cannot directly add and update knowledge if they don’t learn formal language used in the system. Using natural language to represent knowledge can allow authors to update knowledge easily. This thesis presents a new approach to use unconstrained natural language as knowledge representation for a physics tutoring system so that non-programmers can add knowledge without learning a new knowledge representation. This approach allows domain experts to add not only problem statements, but also background knowledge such as commonsense and domain knowledge including principles in natural language. Rather than translating into a formal language, natural language representation is directly used in inference so that domain experts can understand the internal process, detect knowledge bugs, and revise the knowledgebase easily. In authoring task studies with the new system based on this approach, it was shown that the size of added knowledge was small enough for a domain expert to add, and converged to near zero as more problems were added in one mental model test. After entering the no-new-knowledge state in the test, 5 out of 13 problems (38 percent) were automatically solved by the system without adding new knowledge