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  2. Knowledge graph embedding - Wikipedia

    en.wikipedia.org/wiki/Knowledge_graph_embedding

    These models have the generality to distinguish the type of entity and relation, temporal information, path information, underlay structured information, [18] and resolve the limitations of distance-based and semantic-matching-based models in representing all the features of a knowledge graph. [1] The use of deep learning for knowledge graph ...

  3. Vadalog - Wikipedia

    en.wikipedia.org/wiki/Vadalog

    A cyclical dependency graph. A rule is an expression of the form n :− a 1, ..., a n where: . a 1, ..., a n are the atoms of the body,; n is the atom of the head.; A rule allows to infer new knowledge starting from the variables that are in the body: when all the variables in the body of a rule are successfully assigned, the rule is activated and it results in the derivation of the head ...

  4. llama.cpp - Wikipedia

    en.wikipedia.org/wiki/Llama.cpp

    llama.cpp began development in March 2023 by Georgi Gerganov as an implementation of the Llama inference code in pure C/C++ with no dependencies. This improved performance on computers without GPU or other dedicated hardware, which was a goal of the project.

  5. Knowledge graph - Wikipedia

    en.wikipedia.org/wiki/Knowledge_graph

    In knowledge representation and reasoning, a knowledge graph is a knowledge base that uses a graph-structured data model or topology to represent and operate on data. Knowledge graphs are often used to store interlinked descriptions of entities – objects, events, situations or abstract concepts – while also encoding the free-form semantics ...

  6. Library of Efficient Data types and Algorithms - Wikipedia

    en.wikipedia.org/wiki/Library_of_Efficient_Data...

    The Library of Efficient Data types and Algorithms (LEDA) is a proprietarily-licensed software library providing C++ implementations of a broad variety of algorithms for graph theory and computational geometry. [1] It was originally developed by the Max Planck Institute for Informatics Saarbrücken. [2]

  7. 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.

  8. Conceptual graph - Wikipedia

    en.wikipedia.org/wiki/Conceptual_graph

    In this approach, a formula in first-order logic (predicate calculus) is represented by a labeled graph. A linear notation, called the Conceptual Graph Interchange Format (CGIF), has been standardized in the ISO standard for common logic. The diagram above is an example of the display form for a conceptual graph.

  9. Neural scaling law - Wikipedia

    en.wikipedia.org/wiki/Neural_scaling_law

    Since there are 4 variables related by 2 equations, imposing 1 additional constraint and 1 additional optimization objective allows us to solve for all four variables. In particular, for any fixed C {\displaystyle C} , we can uniquely solve for all 4 variables that minimizes L {\displaystyle L} .