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  2. Graph neural network - Wikipedia

    en.wikipedia.org/wiki/Graph_neural_network

    The graph attention network (GAT) was introduced by Petar Veličković et al. in 2018. [11] Graph attention network is a combination of a GNN and an attention layer. The implementation of attention layer in graphical neural networks helps provide attention or focus to the important information from the data instead of focusing on the whole data.

  3. Knowledge graph - Wikipedia

    en.wikipedia.org/wiki/Knowledge_graph

    There is no single commonly accepted definition of a knowledge graph. Most definitions view the topic through a Semantic Web lens and include these features: [14] Flexible relations among knowledge in topical domains: A knowledge graph (i) defines abstract classes and relations of entities in a schema, (ii) mainly describes real world entities and their interrelations, organized in a graph ...

  4. Knowledge graph embedding - Wikipedia

    en.wikipedia.org/wiki/Knowledge_graph_embedding

    The machine learning task for knowledge graph embedding that is more often used to evaluate the embedding accuracy of the models is the link prediction. [ 1 ] [ 3 ] [ 5 ] [ 6 ] [ 7 ] [ 18 ] Rossi et al. [ 5 ] produced an extensive benchmark of the models, but also other surveys produces similar results.

  5. Machine learning - Wikipedia

    en.wikipedia.org/wiki/Machine_learning

    Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]

  6. Graphical model - Wikipedia

    en.wikipedia.org/wiki/Graphical_model

    A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. Graphical models are commonly used in probability theory, statistics—particularly Bayesian statistics—and machine learning.

  7. Data and information visualization - Wikipedia

    en.wikipedia.org/wiki/Data_and_information...

    Data presentation architecture (DPA) is a skill-set that seeks to identify, locate, manipulate, format and present data in such a way as to optimally communicate meaning and proper knowledge. Historically, the term data presentation architecture is attributed to Kelly Lautt: [ a ] "Data Presentation Architecture (DPA) is a rarely applied skill ...

  8. Retrieval-augmented generation - Wikipedia

    en.wikipedia.org/wiki/Retrieval-augmented_generation

    Retrieval-augmented generation (RAG) is a technique that grants generative artificial intelligence models information retrieval capabilities. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to augment information drawn from its own vast, static training data.

  9. Machine-readable document - Wikipedia

    en.wikipedia.org/wiki/Machine-readable_document

    The Portable Document Format (PDF) is a file format used to present documents in a manner independent of application software, hardware, and operating systems. Each PDF file encapsulates a complete description of the presentation of the document, including the text, fonts, graphics, and other information needed to display it.