Knowledge Graph (KG) errors introduce non-negligible noise, severely affecting KG-related downstream tasks. Detecting errors in KGs is challenging since the patterns of errors are unknown and diverse, while ground-truth labels are rare or even unavailable. A traditional solution is to construct logical rules to verify triples, but it is not generalizable since different KGs have distinct rules with domain knowledge involved. Recent studies focus on designing tailored detectors or ranking triples based on KG embedding loss. However, they all rely on negative samples for training, which are generated by randomly replacing the head or tail entity of existing triples. Such a negative sampling strategy is not enough for prototyping practical KG errors, e.g., (Bruce_Lee, place_of_birth, China), in which the three elements are often relevant, although mismatched. We desire a more effective unsupervised learning mechanism tailored for KG error detection. To this end, we propose a novel framework -ContrAstive knowledge Graph Error Detection (CAGED). It introduces contrastive learning into KG learning and provides a novel way of modeling KG. Instead of following the traditional setting, i.e., considering entities as nodes and relations as semantic edges, CAGED augments a KG into different hyper-views, by regarding each relational triple as a node. After joint training with KG embedding and contrastive learning loss, CAGED assesses the trustworthiness of each triple based on two learning signals, i.e., the consistency of triple representations across multi-views and the self-consistency within the triple. Extensive experiments on three real-world KGs show that CAGED outperforms state-ofthe-art methods in KG error detection. Our codes and datasets are available at https://github.com/Qing145/CAGED.git.
CCS CONCEPTS• Computing methodologies → Anomaly detection.
Financial block chain technology is a frontier technology of financial science and technology, which plays an important role in the business processing and expansion of commercial banks under the background of Internet. This paper introduces the applications of block chain including the application of the three types of block chain, namely, digital currency block chain 1.0, intelligent contract block chain of 2.0, and block chain 3.0. In addition to the financial sector, the relationship between block chain and Internet finance is also expounded. Then, mainly through the case of bitcoin, the application of financial block chain has been or will be legislative and regulatory, which can lead to legal issues like lack of legislation, specific regulatory issues, privacy security issues, monopoly and technical problems. Finally, this paper analyzes the legal problems and puts forward some suggestions to relieve the legal problems caused by the application of financial block chain.
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