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We propose ways to use multilingual knowledge graphs to reduce gaps in coverage between languages. We explore the current state of language distribution in knowledge graphs by developing a framework – based on existing standards, frameworks, and guidelines – to measure label and language distribution in knowledge graphs.
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 ...
Knowledge panel data about Thomas Jefferson displayed on Google Search, as of January 2015. The Google Knowledge Graph is a knowledge base from which Google serves relevant information in an infobox beside its search results. This allows the user to see the answer in a glance, as an instant answer. The data is generated automatically from a ...
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.
A knowledge as a service provider responds to knowledge requests from users through a centralised knowledge server, and provides an interface between users and data owners. [2] [3] KaaS is one of several cloud computing-dependent business models in which computer resources are sold on an on-demand and pay-as-you-use basis. [4]
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.
In artificial intelligence, a sentential decision diagram (SDD) is a type of knowledge representation used in knowledge compilation to represent Boolean functions. SDDs can be viewed as a generalization of the influential ordered binary decision diagram (OBDD) representation, by allowing decisions on multiple variables at once. Like OBDDs, SDDs ...
Several branches were manually improved by domain experts. The current version (CSO 3.2 [4]) includes about 14K research topics and 160K semantic relationships. [5] CSO is available in OWL, Turtle, and N-Triples. It is aligned with several other knowledge graphs, including DBpedia, Wikidata, YAGO, Freebase, and Cyc. New versions of CSO are ...