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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 ...
An embedded graph uniquely defines cyclic orders of edges incident to the same vertex. The set of all these cyclic orders is called a rotation system.Embeddings with the same rotation system are considered to be equivalent and the corresponding equivalence class of embeddings is called combinatorial embedding (as opposed to the term topological embedding, which refers to the previous ...
According to the Technology Review, Teevan "is a leader in using data about people's knowledge, preferences, and habits to help them manage information." [ 4 ] Teevan also works on 'microproductivity,' breaking down complex tasks into a series of microtasks that can be completed more easily and efficiently. [ 18 ]
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 .
SharePoint is a collection of enterprise content management and knowledge management tools developed by Microsoft.Launched in 2001, [8] it was initially bundled with Windows Server as Windows SharePoint Server, then renamed to Microsoft Office SharePoint Server, and then finally renamed to SharePoint.
Graph databases are commonly referred to as a NoSQL database. Graph databases are similar to 1970s network model databases in that both represent general graphs, but network-model databases operate at a lower level of abstraction [3] and lack easy traversal over a chain of edges. [4] The underlying storage mechanism of graph databases can vary.
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 .