Difference between revisions of "Semantic query"

Revision as of 10:45, 5 December 2016

Semantic queries allow for queries and analytics of associative and contextual nature. Semantic queries enable the retrieval of both explicitly and implicitly derived information based on syntactic, semantic and structural information contained in data. They are designed to deliver precise results (possibly the distinctive selection of one single piece of information) or to answer more fuzzy and wide open questions through pattern matching and digital reasoning.

Description

Semantic queries work on named graphs, linked-data or triples. This enables the query to process the actual relationships between information and infer the answers from the network of data. This is in contrast to semantic search, which uses semantics (the science of meaning) in unstructured text to produce a better search result (see natural language processing).

From a technical point of view semantic queries are precise relational-type operations much like a database query. They work on structured data and therefore have the possibility to utilize comprehensive features like operators (e.g. >, < and =), namespaces, pattern matching, subclassing, transitive relations, semantic rules and contextual full text search.

The semantic web technology stack of the W3C is offering SPARQL to formulate semantic queries in a syntax similar to SQL. Semantic queries are used in triplestores, graph databases, semantic wikis, natural language and artificial intelligence systems.

See also

• Dataspaces
• Knowledge Representation
• Linked Data
• Ontology alignment
• Semantic Integration
• Semantic publishing
• Semantics of Business Vocabulary and Business Rules
• SPARQL

External links

• Semantic query @ Wikipedia