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Another important aspect of semantic queries is that the type of the relationship can be used to incorporate intelligence into the system. The relationship between a customer and a product has a fundamentally different nature than the relationship between a neighbourhood and its city. The latter
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is created (customers with their geo-spatial information like city, state and country; products with their categories within sub- and super-categories). Now the system can automatically answer more complex queries and analytics that look for the connection of a particular location with a product
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manner. In the above example, no query code needs to be written. The correct product for each customer can be fetched automatically. Whereas this simple example is trivial, the real power of linked-data comes into play when a
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whereas other relationships might have more complicated patterns and "contextual analytics". This process is called inference or reasoning and is the ability of the software to derive new information based on given facts.
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manner only. For example, the relationships between customers and products (stored in two content-tables and connected with an additional link-table) only come into existence in a query statement (
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contained in data. They are designed to deliver precise results (possibly the distinctive selection of one single piece of information) or to answer more
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100:. They work on structured data and therefore have the possibility to utilize comprehensive features like operators (e.g. >, < and =), namespaces,
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Zhifeng, Xiao (2009). "Spatial information semantic query based on SPARQL". In Liu, Yaolin; Tang, Xinming (eds.).
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in the case of relational databases) written by a developer. Writing the query demands exact knowledge of the
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nature. Semantic queries enable the retrieval of both explicitly and implicitly derived information based on
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category. The development effort for this query is omitted. Executing a semantic query is conducted by
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From a technical point of view, semantic queries are precise relational-type operations much like a
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278:"How Siri Works: iPhone's 'Brain' Comes from Natural Language Processing"
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488:"Karma: A System for Mapping Structured Sources into the Semantic Web"
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505:"A Scalable Approach to Learn Semantic Models of Structured Sources"
510:. 8th IEEE International Conference on Semantic Computing. 2014.
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330:"Using the SPARQL Package in R to handle Spatial Linked Data"
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the network of information and finding matches (also called
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318:"Querying Heterogeneous Datasets on the Linked Data Web"
527:. AAAI Fall Symposium on Semantics for Big Data. 2013.
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Semantic queries in databases: problems and challenges
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to formulate semantic queries in a syntax similar to
224:"Semantics Help Wall Street Cope With Data Overload"
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Semantics of
Business Vocabulary and Business Rules
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living in
Manhattan is also living in New York City
22:allow for queries and analytics of associative and
522:"Semantics for Big Data Integration and Analysis"
450:. ACM Digital Library. 2009. pp. 1505–1508.
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266:"Watson, more than a Semantic Web search engine"
418:"Introducing SPARQL: Querying the Semantic Web"
181:represent all relationships between data in an
163:represent all relationships between data in an
65:. This enables the query to process the actual
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235:. Vol. 7492. SPIE. pp. 74921P.
302:. markorodriguez.com on Graph Computing.
291:"The semantic Web gets down to business"
89:to produce a better search result. (See
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42:and wide open questions through
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432:"SPARQL Query Language for RDF"
136:. Semantic queries are used in
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282:International Business Times
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289:Horwitt, Elisabeth (2011).
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91:natural language processing
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323:. IEEE Internet Computing.
309:"SPARQL Nuts & Bolts"
298:Rodriguez, Marko (2011).
77:. This is in contrast to
53:Semantic queries work on
363:Knowledge Representation
337:Lorentz, Alissa (2013).
328:Kauppinen, Tomi (2012).
226:. wallstreetandtech.com.
124:technology stack of the
69:between information and
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316:Freitas, Andre (2012).
276:Dworetzky, Tom (2011).
271:. Semantic Web Journal.
264:Aquin, Mathieu (2010).
150:artificial intelligence
148:, natural language and
311:. Cambridge Semantics.
307:Sequeda, Juan (2011).
188:network of information
36:structural information
222:Velez, Golda (2008).
73:the answers from the
383:Semantic Integration
332:. linkedscience.org.
293:. computerworld.com.
197:Data Graph Traversal
161:Relational databases
110:transitive relations
388:Semantic publishing
241:2009SPIE.7492E..60X
373:Ontology alignment
249:10.1117/12.838556
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378:Philosophy
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156:Background
24:contextual
353:Attention
152:systems.
83:semantics
28:syntactic
347:See also
341:. Wired.
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237:Bibcode
193:walking
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120:. The
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