Knowledge Graphs Representation for Event-Related E-News Articles

Lakshika, M. V. P. T.; Caldera, H. A.

doi:10.3390/make3040040

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Before the application of the sentences to a language model, such as BERT, the sentences were tokenized, and special tokens, such as [SEP], were used to distinguish them. The BERT model provides an output hidden-state value for each token, which is processed using the average pooling and max pooling methods [44][45][46]. In addition, as the performance of the relation extraction is closely related to the types of entities, the types of the Entity_Head and Entity_Tail were represented by embedding them into 64-dimensional vectors.…”

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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“…Typical techniques required for knowledge graph generation are NER and relation extraction between entities [20]. Named entity recognition refers to the task of recognizing an entity that has a unique name in a text [21].…”

Section: Deep Learning-based Pre-trained Language Modelmentioning

confidence: 99%

Graph Embedding-Based Domain-Specific Knowledge Graph Expansion Using Research Literature Summary

Choi¹

2022

Sustainability

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Knowledge bases built in the knowledge processing field have a problem in that experts have to add rules or update them through modifications. To solve this problem, research has been conducted on knowledge graph expansion methods using deep learning technology, and in recent years, many studies have been conducted on methods of generating knowledge bases by embedding the knowledge graph’s triple information in a continuous vector space. In this paper, using a research literature summary, we propose a domain-specific knowledge graph expansion method based on graph embedding. To this end, we perform pre-processing and process and text summarization with the collected research literature data. Furthermore, we propose a method of generating a knowledge graph by extracting the entity and relation information and a method of expanding the knowledge graph using web data. To this end, we summarize research literature using the Bidirectional Encoder Representations from Transformers for Summarization (BERTSUM) model based on domain-specific research literature data and design a Research-BERT (RE-BERT) model that extracts entities and relation information, which are components of the knowledge graph, from the summarized research literature. Moreover, we proposed a method of expanding related entities based on Google news after extracting related entities through the web for the entities in the generated knowledge graph. In the experiment, we measured the performance of summarizing research literature using the BERTSUM model and the accuracy of the knowledge graph relation extraction model. In the experiment of removing unnecessary sentences from the research literature text and summarizing them in key sentences, the result shows that the BERTSUM Classifier model’s ROUGE-1 precision is 57.86%. The knowledge graph extraction performance was measured using the mean reciprocal rank (MRR), mean rank (MR), and HIT@N rank-based evaluation metric. The knowledge graph extraction method using summarized text showed superior performance in terms of speed and knowledge graph quality.

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Method and Models of Extraction of Knowledge from Medical Documents

Zulkarneev¹,

Yusupova²,

Сметанина³

et al. 2022

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The paper analyzes the problem of extracting knowledge from clinical recommendations presented in the form of semi-structured corpora of text documents in natural language, taking into account their periodic updating. The considered methods of intellectual analysis of the accumulated arrays of medical data make it possible to automate a number of tasks aimed at improving the quality of medical care due to significant decision support in the treatment process. A brief review of well-known publications has been made, highlighting approaches to automating the construction of ontologies and knowledge graphs in the problems of semantic modeling of a problem-oriented text corpus. The structural and functional organization of the system of knowledge extraction and automatic construction of an ontology and a knowledge graph of a problem-oriented corpus for a specific subject area is presented. The main stages of knowledge extraction and dynamic updating of the knowledge graph are considered: named entity extraction, semantic annotation, term and keyword extraction, topic modeling, topic identification, and relationship extraction. The formalized representation of texts was obtained using a pre-trained BERT transformer model. The automatic selection of triplets "object" - "action" - "subject" based on part-of-speech markup of the text corpus was used to construct fragments of the knowledge graph. An experiment was carried out on a corpus of medical texts on a given topic (162 documents of depersonalized case histories of patients of a pediatric center) without preliminary markup in order to test the proposed solution for extracting triplets and constructing a knowledge graph based on them. An analysis of the experimental results confirms the need for a deeper markup of the corpus of text documents to take into account the specifics of medical text documents. For an unmarked corpus of texts, the proposed solution demonstrates satisfactory performance in view of the selection of atomic fragments included in the automatically generated ontology.

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Knowledge Graphs Representation for Event-Related E-News Articles

Cited by 3 publications

References 14 publications

Recommendations for Responding to System Security Incidents Using Knowledge Graph Embedding

Recommendations for Responding to System Security Incidents Using Knowledge Graph Embedding

Graph Embedding-Based Domain-Specific Knowledge Graph Expansion Using Research Literature Summary

Method and Models of Extraction of Knowledge from Medical Documents

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