Named entity disambiguation in short texts over knowledge graphs

Bouarroudj, Wissem; Boufaïda, Zizette; Bellatreche, Ladjel

doi:10.1007/s10115-021-01642-9

Cited by 14 publications

(6 citation statements)

References 51 publications

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“…In the second part, the extracted words were combined to generate a set of entity pairs, using all the possible concatenations of the pairs. In edge computing security texts, NER is achieved through the pretrained BERT model [42,43]. The process began by analyzing edge computing security texts to generate a vocabulary.…”

Section: Automated Generation and Expansion Of Bert-based Edge Comput...mentioning

confidence: 99%

Recommendations for Responding to System Security Incidents Using Knowledge Graph Embedding

Kim,

Choi

2023

Electronics

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Recently, security attacks occurring in edge computing environments have emerged as an important research topic in the field of cybersecurity. Edge computing is a distributed computing technology that expands the existing cloud computing architecture to introduce a new layer, the edge layer, between the cloud layer and the user terminal layer. Edge computing has the advantage of greatly improving the data processing speed and efficiency but, at the same time, is complex, and various new attacks occur frequently. Therefore, for improving the security of edge computing, effective and intelligent security strategies and policies must be established in consideration of a wide range of vulnerabilities. Intelligent security systems, which have recently been studied, provide a way to detect and respond to security threats by integrating the latest technologies, such as machine learning and big data analysis. Intelligent security technology can quickly recognize attack patterns or abnormal behaviors within a large amount of data and continuously respond to new threats through learning. In particular, knowledge-based technologies using ontology or knowledge graph technology play an important role in more deeply understanding the meaning and relationships between of security data and more effectively detecting and responding to complex threats. This study proposed a method for recommending strategies to respond to edge computing security incidents based on the automatic generation and embedding of security knowledge graphs. An EdgeSecurity–BERT model, utilizing the latest security vulnerability data from edge computing, was designed to extract entities and their relational information. Also, a security vulnerability assessment method was proposed to recommend strategies to respond to edge computing security incidents through knowledge graph embedding. In the experiment, the classification accuracy of security news data for common vulnerability and exposure data was approximately 86% on average. In addition, the EdgeSecurityKG applying the security vulnerability similarity improved the Hits@10 performance to identify the correct link, but the MR performance was degraded owing to the increased complexity. In complex areas, such as security, careful evaluation of the model’s performance and data selection are important. The EdgeSecurityKG applying the security vulnerability similarity provides an important advantage in understanding complex security vulnerability relationships.

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Section: Automated Generation and Expansion Of Bert-based Edge Comput...mentioning

confidence: 99%

Recommendations for Responding to System Security Incidents Using Knowledge Graph Embedding

Kim,

Choi

2023

Electronics

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“…This results in the fusion problem between heterogeneous data, such as different names referring to the same part problem, e.g., the part location in the mainline of physical structure may be the fault location in the fault maintenance mainline and the monitoring location in the condition monitoring mainline. This is addressed by utilizing the entity disambiguation and the co-referring disambiguation duplication or redundancy, as well as by more accurately establishing entity links [42,43].…”

Section: ) Mainline Of Condition Monitoringmentioning

confidence: 99%

Dynamic Optimization Method of Knowledge Graph Entity Relations for Smart Maintenance of Cantilever Roadheaders

Wang,

Liu,

Ding

et al. 2023

Mathematics

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The fault maintenance scenario in coal-mine equipment intelligence is composed of videos, images, signals, and repair process records. Text data are not the primary data that reflect the fault phenomenon, but rather the secondary processing based on operation experience. Focusing on the difficulty of extracting fault knowledge from the limited textual maintenance process records, a forward static full-connected topology network modeling method based on domain knowledge from four dimensions of physical structure, internal association, condition monitoring, and fault maintenance, is proposed to increase the efficiency of constructing a fault-maintenance knowledge graph. Accurately identifying the intrinsic correlation between the equipment anomalies and the faults’ causes through only domain knowledge and loosely coupled data is difficult. Based on the static full-connected knowledge graph of the cantilever roadheader, the information entropy and density-based DBSCAN clustering algorithm is used to process and analyze many condition-monitoring historical datasets to optimize the entity relationships between the fault phenomena and causes. The improved DBSCAN algorithm consists of three stages: firstly, extracting entity data related to fault information from the static fully connected graph; secondly, calculating the information entropy based on the real dataset describing the fault information and the historical operating condition, respectively; and thirdly, comparing the entropy values of the entities and analyzing the intrinsic relationship between the fault phenomenon, the operating condition data, and the fault causes. Based on the static full-connected topology storage in the Neo4j database, the information entropy and density-based DBSCAN algorithm is computed by using Python to identify the relationship weights and dynamically display optimized knowledge graph topology. Finally, an example of EBZ200-type cantilever roadheader for smart maintenance is studied to analyze and evaluate the forward and four-mainlines knowledge graph modeling method and the dynamic entity relations optimization method for static full-connected knowledge graph.

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“…• Case No. 3 4 : the same place can be designated by several names or several appellations (e.g., United Kingdom and Great Britain, Paris and Panama).…”

Section: Motivation and Significancementioning

confidence: 99%