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Open Access
Article
Publication date: 31 July 2021

Taro Aso, Toshiyuki Amagasa and Hiroyuki Kitagawa

The purpose of this paper is to propose a scheme that allows users to interactively explore relations between entities in knowledge bases (KBs). KBs store a wide range of…

Abstract

Purpose

The purpose of this paper is to propose a scheme that allows users to interactively explore relations between entities in knowledge bases (KBs). KBs store a wide range of knowledge about real-world entities in a structured form as (subject, predicate, object). Although it is possible to query entities and relations among entities by specifying appropriate query expressions of SPARQL or keyword queries, the structure and the vocabulary are complicated, and it is hard for non-expert users to get the desired information. For this reason, many researchers have proposed faceted search interfaces for KBs. Nevertheless, existing ones are designed for finding entities and are insufficient for finding relations.

Design/methodology/approach

To this problem, the authors propose a novel “relation facet” to find relations between entities. To generate it, they applied clustering on predicates for grouping those predicates that are connected to common objects. Having generated clusters of predicates, the authors generated a facet according to the result. Specifically, they proposed to use a couple of clustering algorithms, namely, agglomerative hierarchical clustering (AHC) and CANDECOMP/PARAFAC (CP) tensor decomposition which is one of the tensor decomposition methods.

Findings

The authors experimentally show test the performance of clustering methods and found that AHC performs better than tensor decomposition. Besides, the authors conducted a user study and show that their proposed scheme performs better than existing ones in the task of searching relations.

Originality/value

The authors propose a relation-oriented faceted search method for KBs that allows users to explore relations between entities. As far as the authors know, this is the first method to focus on the exploration of relations between entities.

Details

International Journal of Web Information Systems, vol. 17 no. 6
Type: Research Article
ISSN: 1744-0084

Keywords

Open Access
Article
Publication date: 8 November 2022

Yilong Ren and Jianbin Wang

The missing travel time data for roads is a common problem encountered by traffic management departments. Tensor decomposition, as one of the most widely used method for…

Abstract

Purpose

The missing travel time data for roads is a common problem encountered by traffic management departments. Tensor decomposition, as one of the most widely used method for completing missing traffic data, plays a significant role in the intelligent transportation system (ITS). However, existing methods of tensor decomposition focus on the global data structure, resulting in relatively low accuracy in fibrosis missing scenarios. Therefore, this paper aims to propose a novel tensor decomposition model which further considers the local spatiotemporal similarity for fibrosis missing to improve travel time completion accuracy.

Design/methodology/approach

The proposed model can aggregate road sections with similar physical attributes by spatial clustering, and then it calculates the temporal association of road sections by the dynamic longest common subsequence. A similarity relationship matrix in the temporal dimension is constructed and incorporated into the tensor completion model, which can enhance the local spatiotemporal relationship of the missing parts of the fibrosis type.

Findings

The experiment shows that this method is superior and robust. Compared with other baseline models, this method has the smallest error and maintains good completion results despite high missing rates.

Originality/value

This model has higher accuracy for the fibrosis missing and performs good convergence effects in the case of the high missing rate.

Details

Smart and Resilient Transportation, vol. 4 no. 3
Type: Research Article
ISSN: 2632-0487

Keywords

Open Access
Article
Publication date: 19 December 2018

Lei Zhu, Shuguang Li, Yaohua Li, Min Wang, Yanyu Li and Jin Yao

Cooperative driving refers to a notion that intelligent system sharing controlling with human driver and completing driving task together. One of the key technologies is that the…

Abstract

Purpose

Cooperative driving refers to a notion that intelligent system sharing controlling with human driver and completing driving task together. One of the key technologies is that the intelligent system can identify the driver’s driving intention in real time to implement consistent driving decisions. The purpose of this study is to establish a driver intention prediction model.

Design/methodology/approach

The authors used the NIRx device to measure the cerebral cortex activities for identifying the driver’s braking intention. The experiment was carried out in a virtual reality environment. During the experiment, the driving simulator recorded the driving data and the functional near-infrared spectroscopy (fNIRS) device recorded the changes in hemoglobin concentration in the cerebral cortex. After the experiment, the driver’s braking intention identification model was established through the principal component analysis and back propagation neural network.

Findings

The research results showed that the accuracy of the model established in this paper was 80.39 per cent. And, the model could identify the driver’s braking intent prior to his braking operation.

Research limitations/implications

The limitation of this study was that the experimental environment was ideal and did not consider the surrounding traffic. At the same time, other actions of the driver were not taken into account when establishing the braking intention recognition model. Besides, the verification results obtained in this paper could only reflect the results of a few drivers’ identification of braking intention.

Practical implications

This study can be used as a reference for future research on driving intention through fNIRS, and it also has a positive effect on the research of brain-controlled driving. At the same time, it has developed new frontiers for intention recognition of cooperative driving.

Social implications

This study explores new directions for future brain-controlled driving and wheelchairs.

Originality/value

The driver’s driving intention was predicted through the fNIRS device for the first time.

Details

Journal of Intelligent and Connected Vehicles, vol. 1 no. 3
Type: Research Article
ISSN: 2399-9802

Keywords

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