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In representation learning, knowledge graph embedding (KGE), also referred to as knowledge representation learning (KRL), or multi-relation learning, [1] is a machine learning task of learning a low-dimensional representation of a knowledge graph's entities and relations while preserving their semantic meaning.
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 ...
A knowledge graph is a knowledge base that uses a graph-structured data model. Common applications are for gathering lightly-structured associations between topic-specific knowledge in a range of disciplines, which each have their own more detailed data shapes and schemas .
In natural language processing (NLP), a text graph is a graph representation of a text item (document, passage or sentence). It is typically created as a preprocessing step to support NLP tasks such as text condensation [ 1 ] term disambiguation [ 2 ] (topic-based) text summarization , [ 3 ] relation extraction [ 4 ] and textual entailment .
Knowledge representation goes hand in hand with automated reasoning because one of the main purposes of explicitly representing knowledge is to be able to reason about that knowledge, to make inferences, assert new knowledge, etc. Virtually all knowledge representation languages have a reasoning or inference engine as part of the system.
GraphRAG with a knowledge graph combining access patterns for unstructured, structured, and mixed data GraphRAG [ 40 ] (coined by Microsoft Research ) is a technique that extends RAG with the use of a knowledge graph (usually, LLM-generated) to allow the model to connect disparate pieces of information, synthesize insights, and holistically ...
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 ...
Many models of communication include the idea that a sender encodes a message and uses a channel to transmit it to a receiver. Noise may distort the message along the way. The receiver then decodes the message and gives some form of feedback. [1] Models of communication simplify or represent the process of communication.