What Are Knowledge Graphs Used for?
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Knowledge graphs are utilized in various domains such as telecommunications management, cybersecurity, and question answering systems. In telecommunications, knowledge graphs aid in data integration and complex knowledge representation, particularly in resource/service discovery and monitoring. Cybersecurity knowledge graphs help process vast amounts of complex data, enabling cyber-threat intelligence and visualization of networks and attack paths. Additionally, in question answering systems, knowledge graphs play a crucial role by connecting entities and relations to provide accurate responses, with the system's performance directly linked to the strength of the knowledge graph. Furthermore, knowledge graphs are essential for querying large-scale real-world facts, supporting web search, question answering, semantic search, and recommendation systems.
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| Papers (5) | Insight |
|---|---|
| Knowledge graphs are used for querying in web search, question answering, semantic search, personal assistants, fact checking, and recommendation systems, facilitating diverse applications in information retrieval and natural language processing. | |
| Knowledge graphs are used in question answering systems to extract entities and relations, enabling accurate responses to user queries based on the information stored within the graph. | |
| Knowledge graphs are used for querying in web search, question answering, semantic search, personal assistants, fact checking, and recommendation systems, storing real-world facts as triples and graph structures. | |
| Knowledge graphs are used in cybersecurity for processing complex data, aiding threat intelligence, enhancing situational awareness, visualizing networks, and enabling machine learning and automated reasoning over cyber-knowledge. | |
| Knowledge graphs are utilized in telecommunications systems management for data integration, complex knowledge representation, resource/service discovery, and monitoring, particularly in software-defined networks due to their extensibility and interoperability. |
Related Questions
How do knowledge graphs influence recommendation systems in various applications?5 answersKnowledge graphs play a crucial role in enhancing recommendation systems across various applications by providing rich and structured information for more accurate and personalized recommendations. Incorporating knowledge graphs into recommendation systems allows for a more fine-grained modeling of data, enabling better understanding of user preferences. Techniques like message passing and preference propagation on knowledge graphs help address sparsity and cold-start issues, improving recommendation performance. Additionally, deep learning models, such as RKGCN, leverage knowledge graphs to offer more personalized recommendations by dynamically analyzing user preferences. By utilizing the diverse relationships and information within knowledge graphs, recommendation systems can overcome challenges like interpretability and cold start, leading to superior recommendation results in real-world scenarios.
How AI can be used to investigate with knowledge graph?5 answersAI can be used to investigate with knowledge graphs by integrating heterogeneous information sources and representing knowledge elements in a machine-readable form. Knowledge graphs can be leveraged in eXplainable AI (XAI) models to enhance transparency and trust in healthcare decision-making processes. They can be used for explainability in detecting healthcare misinformation, adverse drug reactions, and drug-drug interactions, as well as reducing the knowledge gap between healthcare experts and AI-based models. Knowledge graphs are also utilized in neuro-symbolic AI systems to enhance the performance and explainability of predictive models by augmenting the contextual meaning of entities in a knowledge graph. Additionally, knowledge-augmented graph machine learning (KaGML) integrates external biomedical knowledge into the GML pipeline for more precise and interpretable drug discovery.
What is a knowledge graph?4 answersA knowledge graph is a structured representation of knowledge that uses entities and relations to capture information about a domain. It is a form of graph data structure that can store and reveal interlinked relationships between different objects within a system. Knowledge graphs are being used in various fields, such as drug discovery, access control, product design, and soil ecology, to integrate and interpret large amounts of heterogeneous data. They provide a way to organize and represent complex information, making it easier for humans and computers to understand and analyze the data. Knowledge graphs can be constructed using machine learning techniques, such as deep learning, to learn graph embeddings, make predictions, and support reasoning. They offer opportunities for AI systems and have potential applications in different domains.
How to construct education knowledge graph?5 answersTo construct an education knowledge graph, several steps can be followed. First, a corpus of knowledge graphs specific to the education domain needs to be constructed. This involves the automatic recognition of named entities and the parallel mining of entity relations. Deep learning algorithms, particularly those based on artificial intelligence, can be used for this purpose. Additionally, the fusion of disciplinary knowledge graphs can be achieved using these algorithms. To enhance the graph's comprehensiveness, the inclusion of multiple modalities such as teacher speeches can be considered. Techniques like fine-tuned Bidirectional Encoder Representation from Transformers (BERT) models and Bidirectional Long Short-Term Memory-Conditional Random Field (BiLSTM-CRF) can be employed for effective identification of educational entities and extraction of educational relationships. By following these steps, a comprehensive and informative education knowledge graph can be constructed, facilitating better education services and promoting research in the field.
How to represent knowledge graphs for machine learning?5 answersKnowledge graphs (KGs) can be represented for machine learning using various methods. One approach is to use knowledge representation learning methods that embed entities and relations of the KG into a low-dimensional vector space. Another method is to use graph neural networks over knowledge graphs to learn meaningful representations of medical concepts. Additionally, a meta-strategy for embedding knowledge graphs can be used, which involves coarsening the graph before embedding it using an arbitrary method. Furthermore, knowledge graphs can be encoded into numerical representations and used with relational graph convolutional networks (RGCN) to infer missing connections and improve data completion. Finally, knowledge graph embeddings (KGE) can be learned through neural network architectures, but it is important to consider their vulnerability to data poisoning attacks.
Do you know any ai knowledge graphs?5 answersKnowledge graphs play an important role in AI applications, such as personal assistants, question answering, and semantic search. They are widely used in industry and research, with public knowledge bases like Wikidata being commonly used. However, AI techniques like machine learning and natural language processing often struggle to use knowledge graphs in their full size and complexity. Custom preprocessing code is commonly used to simplify and sample the data. A more principled approach using declarative methods from knowledge representation and reasoning is suggested, with rule-based systems being a promising platform for computing customized views on knowledge graphs. This integrated approach can help in integrating the results of other AI methods back into the overall knowledge model.
In addition to the Google Knowledge Graph, which has had a significant impact on web search and advertising, knowledge graphs have been less prominent in other industries. The commercial domain, particularly product-related e-commerce sectors like Amazon Product Graph, has seen more focus. However, there is an opportunity to exploit knowledge graphs in service-related sectors like tourism. This book chapter discusses the fundamentals of knowledge graphs, how they can be built and used in the tourism domain, and provides practical guidelines for implementation.
Knowledge graphs are proposed as the reference technology for enterprise AI, providing a common backbone for all AI-driven applications in a business domain. They help govern the proliferation of smart AI-driven applications and coordinate them within coherent knowledge workflows. The Vadalog system, a successful knowledge graph middleware, is highlighted as a tool for designing software architectures for AI-driven applications. Use cases from the financial domain demonstrate knowledge graphs in action.
Sowa's conceptual graphs have received attention as a knowledge representation language in the AI literature. They are considered a good intermediate stage towards a relational database schema. However, the claim that they can be used as an intermediate stage towards a relational database schema is examined.
The author expresses skepticism about proposals for a trouble-free marriage of AI knowledge bases and databases. They argue against a shotgun wedding, suggesting caution and careful consideration in integrating AI knowledge bases and databases.