Research on Knowledge Graph Model for Cybersecurity Logs Based on Ontology and Classified Protection

Knowledge graph can effectively analyze and construct the essential characteristics of data. At present, scholars have proposed many knowledge graph models from different perspectives, especially in the medical field, but there are still relatively few studies on stroke diseases using medical knowledge graphs. Therefore, this paper will build a medical knowledge graph model for stroke. Firstly, a stroke disease dictionary and an ontology database are built through the international standard medical term sets and semiautomatic extraction-based crowdsourcing website data. Secondly, the external data are linked to the nodes of the existing knowledge graph via the entity similarity measures and the knowledge representation is performed by the knowledge graph embedded model. Thirdly, the structure of the established knowledge graph is modified continuously through iterative updating. Finally, in the experimental part, the proposed stroke medical knowledge graph is applied to the real stroke data and the performance of the proposed knowledge graph approach on the series of Trans ∗ models is compared.

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Graph and Neural Network-Based Intelligent Conversation System

Nature-Inspired Algorithms for Big Data Frameworks - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-5852-1.ch014 ◽

2019 ◽

pp. 339-357 ◽

Cited By ~ 1

Author(s):

Anuja Arora ◽

Aman Srivastava ◽

Shivam Bansal

Keyword(s):

Deep Learning ◽

Natural Language ◽

System Approach ◽

Domain Knowledge ◽

Research Work ◽

Graph Model ◽

Machine Intelligence ◽

Knowledge Graph ◽

Feature Engineering ◽

Graph Based Model

The conventional approach to build a chatbot system uses the sequence of complex algorithms and productivity of these systems depends on order and coherence of algorithms. This research work introduces and showcases a deep learning-based conversation system approach. The proposed approach is an intelligent conversation model approach which conceptually uses graph model and neural conversational model. The proposed deep learning-based conversation system uses neural conversational model over knowledge graph model in a hybrid manner. Graph-based model answers questions written in natural language using its intent in the knowledge graph and neural conversational model converses answer based on conversation content and conversation sequence order. NLP is used in graph model and neural conversational model uses natural language understanding and machine intelligence. The neural conversational model uses seq2seq framework as it requires less feature engineering and lacks domain knowledge. The results achieved through the authors' approach are competitive with solely used graph model results.

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Knowledge Graph Model of Power Grid for Human-machine Mutual Understanding

2020 Chinese Automation Congress (CAC) ◽

10.1109/cac51589.2020.9327043 ◽

2020 ◽

Author(s):

Tao Yin ◽

Na Lu

Keyword(s):

Power Grid ◽

Graph Model ◽

Knowledge Graph ◽

Mutual Understanding

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Using the W3C Generating RDF from Tabular Data on the Web recommendation to manage small Wikidata datasets

Semantic Web ◽

10.3233/sw-210443 ◽

2021 ◽

pp. 1-23

Author(s):

Steven J. Baskauf ◽

Jessica K. Baskauf

Keyword(s):

Simple Form ◽

Data Model ◽

Graph Model ◽

Value Added ◽

Knowledge Graph ◽

Tabular Data ◽

Rdf Graph ◽

Current State ◽

Over Time ◽

The Web

The W3C Generating RDF from Tabular Data on the Web Recommendation provides a mechanism for mapping CSV-formatted data to any RDF graph model. Since the Wikibase data model used by Wikidata can be expressed as RDF, this Recommendation can be used to document tabular snapshots of parts of the Wikidata knowledge graph in a simple form that is easy for humans and applications to read. Those snapshots can be used to document how subgraphs of Wikidata have changed over time and can be compared with the current state of Wikidata using its Query Service to detect vandalism and value added through community contributions.

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