181,369 research outputs found
Bim-Based Risk Identification System in tunnel construction
This paper presents an innovative approach of integrating Building Information Modeling (BIM) and expert systems to address deficiencies in traditional safety risk identification process in tunnel construction. A BIM-based Risk Identification Expert System (B-RIES) composed of three main built-in subsystems: BIM extraction, knowledge base management, and risk identification subsystems, is proposed. The engineering parameter information related to risk facÂtors is first extracted from BIM of a specific project where the Industry Foundation Classes (IFC) standard plays a bridge role between the BIM data and tunnel construction safety risks. An integrated knowledge base, consisting of fact base, rule base and case base, is then established to systematize the fragmented explicit and tacit knowledge. Finally, a hybrid inference approach, with case-based reasoning and rule-based reasoning combined, is developed to improve the flexibilÂity and comprehensiveness of the system reasoning capacity. B-RIES is used to overcome low-efficiency in traditional information extraction, reduce the dependence on domain experts, and facilitate knowledge sharing and communication among dispersed clients and domain experts. The identification of a safety hazard regarding the water gushing in one metro station of China is presented in a case study. The results demonstrate the feasibility of B-RIES and its application effectiveness
Application of case-based reasoning techniques to the automation of single-family residential property appraisals
Case-based reasoning has emerged as an alternative to rule-based reasoning techniques for the design of expert systems. This paper concentrates on the issues involved in the application of the case-based reasoning techniques to a specific domain, property appraisal. Case-based reasoning has been recently favored because it seems to resemble more closely to the psychological process human follows when trying to apply their knowledge to the solution of problems: People adapt solutions of similar problems they handled in past experiences to address present situations.
Property appraisal or valuation is a domain characterized by having a single parameter in its solution, that is, the value of the property being appraised. This makes it differ from most of the domains in which case-based reasoning have been attempted. Those other domains require the satisfaction of multiple goals, which are related to one another in some type of explanation or plan. Because of the fact that property appraisal has a single goal, it is particularly important to find the best possible answer for that solution. In addition to this, the achievement of consistency is also essential in this domain in which different experts may reach different answers, even having the same data at their disposition.
By modelling the market data approach of the appraisal, using adaptations of case-based reasoning techniques, such as the similarity links and the critics, and integrating other techniques, such as the use of comfort factors, a case-based reasoned for property appraisal is implemented addressing the issues just mentioned above
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Towards a Semantic Knowledge Management Framework for Laminated Composites
The engineering of laminated composite structures is a complex task for design engineers and manufacturers, requiring significant management of manufacturing process and materials information. Ontologies are becoming increasingly commonplace for semantically representing knowledge in a formal manner that facilitates sharing of rich information between people and applications. Moreover, ontologies can support first-order logic and reasoning by rule engines that enhance automation. To support the engineering of laminated composite structures, this work developed a novel Semantic LAminated Composites Knowledge management System (SLACKS) that is based on a suite of ontologies for laminated composites materials and design for manufacturing (DFM) and their integration into a previously developed engineering design framework. By leveraging information from CAD/FEA tools and materials data from online public databases, SLACKS uniquely enables software tools and people to interoperate, to improve communication and automate reasoning during the design process. With SLACKS, this research shows the power of integrating relevant domains of the product lifecycle, such as design, analysis, manufacturing and materials selection through the engineering case study of a wind turbine blade. The integration reveals a usable product lifecycle knowledge tool that can facilitate efficient knowledge creation, retrieval and reuse, from design inception to manufacturing of the product
The VEX-93 environment as a hybrid tool for developing knowledge systems with different problem solving techniques
The paper describes VEX-93 as a hybrid environment for developing
knowledge-based and problem solver systems. It integrates methods and
techniques from artificial intelligence, image and signal processing and
data analysis, which can be mixed. Two hierarchical levels of reasoning
contains an intelligent toolbox with one upper strategic inference engine
and four lower ones containing specific reasoning models: truth-functional
(rule-based), probabilistic (causal networks), fuzzy (rule-based) and
case-based (frames). There are image/signal processing-analysis capabilities
in the form of programming languages with more than one hundred primitive
functions.
User-made programs are embeddable within knowledge basis, allowing the
combination of perception and reasoning. The data analyzer toolbox contains
a collection of numerical classification, pattern recognition and ordination
methods, with neural network tools and a data base query language at
inference engines's disposal.
VEX-93 is an open system able to communicate with external computer programs
relevant to a particular application. Metaknowledge can be used for
elaborate conclusions, and man-machine interaction includes, besides windows
and graphical interfaces, acceptance of voice commands and production of
speech output.
The system was conceived for real-world applications in general domains, but
an example of a concrete medical diagnostic support system at present under
completion as a cuban-spanish project is mentioned.
Present version of VEX-93 is a huge system composed by about one and half
millions of lines of C code and runs in microcomputers under Windows 3.1.Postprint (published version
A Neural-CBR System for Real Property Valuation
In recent times, the application of artificial intelligence (AI) techniques for real property valuation has been on the
increase. Some expert systems that leveraged on machine intelligence concepts include rule-based reasoning, case-based
reasoning and artificial neural networks. These approaches have proved reliable thus far and in certain cases outperformed
the use of statistical predictive models such as hedonic regression, logistic regression, and discriminant analysis. However,
individual artificial intelligence approaches have their inherent limitations. These limitations hamper the quality of
decision support they proffer when used alone for real property valuation. In this paper, we present a Neural-CBR system
for real property valuation, which is based on a hybrid architecture that combines Artificial Neural Networks and Case-
Based Reasoning techniques. An evaluation of the system was conducted and the experimental results revealed that the
system has higher satisfactory level of performance when compared with individual Artificial Neural Network and Case-
Based Reasoning systems
Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study
Recommender systems engage user profiles and appropriate filtering techniques
to assist users in finding more relevant information over the large volume of
information. User profiles play an important role in the success of
recommendation process since they model and represent the actual user needs.
However, a comprehensive literature review of recommender systems has
demonstrated no concrete study on the role and impact of knowledge in user
profiling and filtering approache. In this paper, we review the most prominent
recommender systems in the literature and examine the impression of knowledge
extracted from different sources. We then come up with this finding that
semantic information from the user context has substantial impact on the
performance of knowledge based recommender systems. Finally, some new clues for
improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science &
Engineering Survey (IJCSES) Vol.2, No.3, August 201
Reasoning with Forest Logic Programs and f-hybrid Knowledge Bases
Open Answer Set Programming (OASP) is an undecidable framework for
integrating ontologies and rules. Although several decidable fragments of OASP
have been identified, few reasoning procedures exist. In this article, we
provide a sound, complete, and terminating algorithm for satisfiability
checking w.r.t. Forest Logic Programs (FoLPs), a fragment of OASP where rules
have a tree shape and allow for inequality atoms and constants. The algorithm
establishes a decidability result for FoLPs. Although believed to be decidable,
so far only the decidability for two small subsets of FoLPs, local FoLPs and
acyclic FoLPs, has been shown. We further introduce f-hybrid knowledge bases, a
hybrid framework where \SHOQ{} knowledge bases and forest logic programs
co-exist, and we show that reasoning with such knowledge bases can be reduced
to reasoning with forest logic programs only. We note that f-hybrid knowledge
bases do not require the usual (weakly) DL-safety of the rule component,
providing thus a genuine alternative approach to current integration approaches
of ontologies and rules
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