Automated Fine-Grained Trust Assessment in Federated Knowledge Bases

Nölle, Andreas; Chekol, Melisachew Wudage; Meilicke, Christian; Nemirovski, German; Stuckenschmidt, Heiner

doi:10.1007/978-3-319-68288-4_29

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…First, we order the links of each node by descending weight on line 4 of Algorithm 1. Then, we initialize the head links of the node by accumulating all the links having the two heaviest weights (lines [5][6][7][8][9][10][11][12][13][14][15]. During the head link weight accumulation, the rankedWeight function (line 9) adopts an increasing ratio rw i ∈ (0, 1] starting from the second heaviest link (i = 1):…”

Section: A Weight-preserving Reduction Of the Similarity Matrixmentioning

confidence: 99%

“…This category is extended with mixen, representing the union of the samples of the category datasets belonging to the English DBpedia. The second category are biomedical datasets 5 while the third category are open government datasets 6 . Some statistics about each dataset are listed in Table I.…”

Section: B Datasetsmentioning

confidence: 99%

“…In particular, type information is often missing [3], which is particularly problematic as types are crucial for correctly handling many integration and postprocessing tasks such as semantic search [4], federated query This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement 683253/GraphInt) and in part by the Swiss National Science Foundation under grant number CRSII2 147609. 1 http://lod-cloud.net/ processing [5], linked data integration [6], or knowledge graph partitioning [7]. In this paper, we propose a novel method, called StaTIX 2 (for Statistical Type Inference), for automatically inferring instance types from Linked Data.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, Linked Data often does not have a single maintainer or a strict unified schema for structuring the instances and as such typically includes noisy and/or incomplete data [2]. In particular, type information is often missing [3], which is particularly problematic as types are crucial for correctly handling many integration and postprocessing tasks such as semantic search [4], federated query processing [5], linked data integration [6], or knowledge graph partitioning [7].…”

Section: Introductionmentioning

confidence: 99%

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StaTIX — Statistical Type Inference on Linked Data

Lutov

Roshankish

Khayati

et al. 2018

2018 IEEE International Conference on Big Data (Big Data)

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Large knowledge bases typically contain data adhering to various schemas with incomplete and/or noisy type information. This seriously complicates further integration and post-processing efforts, as type information is crucial in correctly handling the data. In this paper, we introduce a novel statistical type inference method, called StaTIX, to effectively infer instance types in Linked Data sets in a fully unsupervised manner. Our inference technique leverages a new hierarchical clustering algorithm that is robust, highly effective, and scalable. We introduce a novel approach to reduce the processing complexity of the similarity matrix specifying the relations between various instances in the knowledge base. This approach speeds up the inference process while also improving the correctness of the inferred types due to the noise attenuation in the input data. We further optimize the clustering process by introducing a dedicated hash function that speeds up the inference process by orders of magnitude without negatively affecting its accuracy. Finally, we describe a new technique to identify representative clusters from the multi-scale output of our clustering algorithm to further improve the accuracy of the inferred types. We empirically evaluate our approach on several real-world datasets and compare it to the state of the art. Our results show that StaTIX is more efficient than existing methods (both in terms of speed and memory consumption) as well as more effective. StaTIX reduces the F1-score error of the predicted types by about 40% on average compared to the state of the art and improves the execution time by orders of magnitude.

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