A new truth discovery method for resolving object conflicts over Linked Data with scale-free property

Liu, Wenqiang; Li, Jun; Wei, Bifan; Duan, Haimeng; Hu, Wei

doi:10.1007/s10115-018-1192-z

Cited by 7 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We selected three popular RDF data fusion systems for comparative experiments. They are ObResolution [2], Truth Discovery [11], MINTE [12]. Our method is called RDFSim+KNN.…”

Section: Resultsmentioning

confidence: 99%

Linked Data Fusion Based on Similarity Calculation and K-Nearest Neighbor

Chen

2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

The development of semantic web technology supports the continuous development of linked data and its applications. In order to make effective use of the growing linked data on the web, multi-source data should be fused, which is a key step in multi-source large-scale data analysis and management. Currently, merging these data can be difficult, because various sources usually provide multiple conflict descriptions for entities in the same real world. To complete the fusion, we need to solve the problem of object conflict. This paper proposes a linked data fusion method based on similarity calculation and k-nearest neighbor. This method has two contributions. Firstly, a similarity calculation method of linked data is proposed, which can effectively integrate URI nodes and blank nodes in linked data; Secondly, a literal type node fusion strategy based on k-nearest neighbor classification method is proposed, which realizes the automation of fusion and has the independence of data source. The results show that compared with other methods, this method can improve the conciseness and consistency and precision by up to 12.9%, 30.6% and 12%.

show abstract

“…We selected three popular RDF data fusion systems for comparative experiments. They are ObResolution [2], Truth Discovery [11], MINTE [12]. Our method is called RDFSim+KNN.…”

Section: Resultsmentioning

confidence: 99%

Linked Data Fusion Based on Similarity Calculation and K-Nearest Neighbor

Chen

2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…What is more, a novel distributed truth discovery frame-work is proposed in [21], which can effectively and efficiently aggregate conflicting data stored across distributed servers, with the differences among the objects as well as the importance level of each server being considered. Because confidence interval contains richer information, Liu et al [22] propose a novel approach called TruthDiscover to determine the most trustworthy object in Linked Data with a scale-free property. More specifically, TruthDiscover consists of two core components: Priori Belief Estimation for smoothing the trustworthiness of sources by leveraging the topological properties of the Source Belief Graph, and Truth Computation for inferencing the trust-worthiness of source and trust value of an object.…”

Section: Related Workmentioning

confidence: 99%

Estimate Information Fusion Weight of WSNs Nodes Based on Truth Discovery Optimization Method Among Conflicting Sources of Data

2019

View full text Add to dashboard Cite

In practical wireless sensor networks (WSNs)-based applications, often only a few nodes are active while most of the others are activated occasionally due to limited resource of the WSNs. Thus most sensors only provide a few observations and only a few sensors make many observations, which often cause long-tail issue to undermine information fusion performance. So we present a confidence-aware information fusion scheme named CAIF to solve such a problem. In particular, we first make a quantitative study on the long-tail data phenomenon and the relationship between node-target distance and node sensing capability, which can provide a guideline to improve node weight estimation error caused by long-tail data. Then, we propose a truth discovery-based method in WSNs via incorporating node-target distance into the truth discovery optimization solution framework to infer the sensor node's fusion weight. In order to adapt to the distribution characteristic of the WSNs, we propose a distributed implementation to estimate the sensor nodes' weights via confidence level and node-target distance to further improve the fusion performance. Besides, the iterative process shown in CAIF converges to a stationary point of the optimization problem and its time complexity is linear with respect to the total number of observations. Finally, we conduct extensive experiments on real data to validate and evaluate CAIF. The experimental results demonstrate the superior performance of our method over existing solutions in terms of root-mean-square error and accuracy.

show abstract