Search results
Results From The WOW.Com Content Network
Graph-based entity linking uses features of the graph topology or multi-hop connections between entities, which are hidden to simple text analysis. Han et al. propose the creation of a disambiguation graph (a subgraph of the knowledge base which contains candidate entities). [ 2 ]
A knowledge graph = {,,} is a collection of entities , relations , and facts . [5] A fact is a triple (,,) that denotes a link between the head and the tail of the triple. . Another notation that is often used in the literature to represent a triple (or fact) is <,, >
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 ...
Babelfy is a software algorithm for the disambiguation of text written in any language.. Specifically, Babelfy performs the tasks of multilingual Word Sense Disambiguation (i.e., the disambiguation of common nouns, verbs, adjectives and adverbs) and Entity Linking (i.e. the disambiguation of mentions to encyclopedic entities like people, companies, places, etc.).
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.
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 ...
Entity linking; At the terminology extraction level, lexical terms from the text are extracted. For this purpose a tokenizer determines at first the word boundaries and solves abbreviations. Afterwards terms from the text, which correspond to a concept, are extracted with the help of a domain-specific lexicon to link these at entity linking.
More complex graph-based approaches have been shown to perform almost as well as supervised methods [21] or even outperforming them on specific domains. [3] [22] Recently, it has been reported that simple graph connectivity measures, such as degree, perform state-of-the-art WSD in the presence of a sufficiently rich lexical knowledge base. [23]