Experience modeling with graphs encoded knowledge for construction industry

Kamsu-Foguem, Bernard; Abanda, Fonbeyin Henry

doi:10.1016/j.compind.2015.02.004

Cited by 24 publications

(15 citation statements)

References 32 publications

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“…Foguem et al used conceptual graphs for knowledge formalization in experience feedback processes and converted derived information from experiences to explicit knowledge. Kamsu‐Foguem and Abanda also used knowledge bases for collecting experiences and conceptual graph for representing ontology in the industry. In another paper, data mining and conceptual graphs have been used for reusing experiences in the industry .…”

Section: Modeling Experiencesmentioning

confidence: 99%

Spatial experience based route finding using ontologies

2020

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Spatial experiences in route finding, such as the ability of finding low‐traffic routes, exert a significant influence on travel time in big cities; therefore, the spatial experiences of seasoned individuals such as taxi drivers in route finding can be useful for improving route‐finding algorithms and preventing using routes having considerable traffic. In this regard, a spatial experience‐based route‐finding algorithm is introduced through ontology in this paper. To this end, different methods of modeling experiences are investigated. Then, a modeling method is chosen for modeling the experiences of drivers for route finding depending on the advantages of ontology, and an ontology based on the taxi drivers’ experiences is proposed. This ontology is employed to create an ontology‐based route‐finding algorithm. The results are compared with those of Google maps in terms of route length and travel time at peak traffic time. According to the results, although the route lengths of route‐finding method based on the ontology of drivers’ experiences in three cases (from nine cases) are greater than that based on Google maps, the travel times are shorter in most cases, and in some routes, the difference in travel time reaches only 10 minutes.

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Section: Modeling Experiencesmentioning

confidence: 99%

Spatial experience based route finding using ontologies

2020

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“…In another studies such as [19][20][21], the authors have also used ontology, knowledge base and web software for exchanging experiences. Moreover, a knowledge base for collecting experiences and graphs for displaying ontology in industry have been used in [22]. Ontology is found to be an efficient method for formulating knowledge.…”

Section: Introductionmentioning

confidence: 99%

A Context-Aware Route Finding Algorithm for Self-Driving Tourists Using Ontology

et al. 2019

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This study proposed a context-aware ontology-based route finding algorithm for self-driving tourists. In this algorithm, two ontologies—namely drivers’ experiences and required tourist services—were used according to tourist requirements. Trips were classified into business and touristic. The algorithm was then compared with Google Maps in terms of travel time and travel length for evaluation. The results showed that the proposed algorithm performed similarly to Google Maps in some cases of business trips and better in other cases, with a maximum 10-min travel time difference. In touristic trips, the capabilities of the proposed algorithm were far better than those of Google Maps.

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“…Reyes et al [16] used case-based reasoning and the knowledge base to store and reuse experiences in industry. Kamsu-Foguem and Abanda [17] used the knowledge base to gather experiences and graphic concepts to represent ontology in industry. In another study, Kamsu-Foguem et al [18] used conceptual graphs and ontology for checking Building Research Establishment Environmental Assessment Methodology (BREEAM) sustainability standard.…”

Section: Introductionmentioning

confidence: 99%

“…Maleki et al [23] proposed ontology as a framework for supporting smart services in product-service system of industries. According to studies on the storage of experiences, different methods including ontology [6,24], conceptual graph [25], rules [17], and logic [16] have been used for modeling experiences. However, conceptual graphs and rules are primarily used for knowledge representation, and their applications are limited.…”

Section: Introductionmentioning

confidence: 99%

An Improved Route‐Finding Algorithm Using Ubiquitous Ontology‐Based Experiences Modeling

et al. 2019

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Every day, people are hired in different organizations and old and retiring employees are eliminated from enterprise systems. Eliminating these individuals from organizations leads to the loss of their spatial experiences. In addition, since new employees lack relevant experience, they need a long time to develop the correct skills for the company and may even cause damage to the organization during this learning process. Therefore, storing the spatial experience of individuals is a critical issue. Due to the intelligence of ubiquitous Geospatial Information System (GIS), any experience from any user can be received and stored. In the future, based on these experiences, an appropriate service to each user may be provided as needed. This paper aims to propose an ontology-based model to store spatial experiences in the field of ubiquitous GIS route finding. For this purpose, first ontology is designed for route finding, and then according to this ontology, an ontology-based route-finding algorithm is developed for ubiquitous GIS. Finally, this algorithm is implemented for Tehran, Iran, and its results are compared with the shortest path algorithm (Dijkstra’s algorithm) in terms of the route length and travel time for peak traffic time. The results show that while the route length obtained from the ontology-based algorithm is more than Dijkstra’s algorithm, the travel time is lower, and on some routes the difference in travel time saved reaches 35 minutes.

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Experience modeling with graphs encoded knowledge for construction industry

Cited by 24 publications

References 32 publications

Spatial experience based route finding using ontologies

Spatial experience based route finding using ontologies

A Context-Aware Route Finding Algorithm for Self-Driving Tourists Using Ontology

An Improved Route‐Finding Algorithm Using Ubiquitous Ontology‐Based Experiences Modeling

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