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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 ...
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.
Once the knowledge graph is created, subgraphs can be vectorized, stored in a vector database, and used for retrieval as in plain RAG. The advantage here is that graphs has more recognizable structure than strings of text and this structure can help retrieve more relevant facts for generation. Sometimes this approach is called GraphRAG.
Key features of GBKR, the graph-based knowledge representation and reasoning model developed by Chein and Mugnier and the Montpellier group, can be summarized as follows: [3] All kinds of knowledge (ontology, rules, constraints and facts) are labeled graphs, which provide an intuitive and easily understandable means to represent knowledge.
Many of the early approaches to knowledge represention in Artificial Intelligence (AI) used graph representations and semantic networks, similar to knowledge graphs today. In such approaches, problem solving was a form of graph traversal [2] or path-finding, as in the A* search algorithm. Typical applications included robot plan-formation and ...
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 .
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 .
The Knowledge Graph proposed by Google in 2012 is actually an application of semantic network in search engine. Modeling multi-relational data like semantic networks in low-dimensional spaces through forms of embedding has benefits in expressing entity relationships as well as extracting relations from mediums like text.