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primary topic: CWPK #25: Querying KBpedia with SPARQL
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CWPK #25: Querying KBpedia with SPARQL
type: Post
has container: weblog
has creator: Mike Bergman
maker: Mike Bergman
created: 2020-08-28T15:36:20Z
content: Now, We Open Up the Power In our recent installments we have been looking at how to search -- ultimately, of course, related to how to extract -- information from our knowledge graph, KBpedia, and the various large-scale knowledge bases to which it maps, such as Wikipedia, DBpedia, and Wikidata. We've seen that owlready2 offers us some native search capabilities, and that we can extend that by indexing additional attributes. What is powerful about knowledge graphs, however, is that all nodes and all edges are structural from the get-go, and we can easily add meaningful structure to our searches by how we represent the pieces (nodes) and by how we relate, or connect, them using the edges. Today's knowledge graphs are explicit in organizing information by structure. The exact scope of this structure varies across representations, and certainly one challenge in getting information to work together from multiple locations and provenances is the diversity of these representations. Those are the questions of semantics, and, fortunately, semantic technologies and parsers give us rich ways to retrieve and relate that structure. So, great, we now have structure galore! What are we going to do with it? Well, this structured information exists, literally, everywhere. We have huge online structured datestores, trillions of semi-structured Web pages and records, and meaningful information and analysis across a rich pastiche of hierarchies and relationships. What is clear in any attempt to solve a meaningful problem is that we need much external information as well as much grounding in our internal circumstances. Problem solving can not be separated from obtaining and integrating meaningful information. Thus, it is essential that we be able to query external information stores on an equivalent basis to our local ones. This equivalence requires both internal and external sources be structured and queriable on an equivalent basis, which is where the W3C-enabled standards and SPARQL…
encoded: Now, We Open Up the Power In our recent installments we have been looking at how to search — ultimately, of course, related to how to extract — information from our knowledge graph, KBpedia, and the various large-scale knowledge bases to which it maps, such as Wikipedia, DBpedia, and Wikidata. We’ve seen that owlready2 offers us some native search capabilities, and that we can extend that by indexing additional attributes. What is powerful about knowledge graphs, however, is that all nodes and all edges are structural from the get-go, and we can easily add meaningful structure to our searches by how we represent the pieces (nodes) and by how we relate, or connect, them using the edges. Today’s knowledge graphs are explicit in organizing information by structure. The exact scope of this structure varies across representations, and certainly one challenge in getting information to work together from multiple locations and provenances is the diversity of these representations. Those are the questions of semantics, and, fortunately, semantic technologies and parsers give us rich ways to retrieve and relate that structure. So, great, we now have structure galore! What are we going to do with it? Well, this structured information exists, literally, everywhere. We have huge online structured datestores, trillions of semi-structured Web pages and records, and meaningful information and analysis across a rich pastiche of hierarchies and relationships. What is clear in any attempt to solve a meaningful problem is that we need much external information as well as much grounding in our internal circumstances. Problem solving can not be separated from obtaining and integrating meaningful information. Thus, it is essential that we be able to query external information stores on an equivalent basis to our local ones. This equivalence requires both internal and external sources be structured and queriable on an equivalent basis, which is where the W3C-enabled standards and SPARQL c…
topic: CWPK
topic: KBpedia
topic: Semantic Web Tools
links to: KBpedia
links to: Wikipedia
links to: DBpedia
links to: Wikidata
links to: owlready2
links to: W3C
links to: SPARQL
links to: RDF
links to: OWL
links to: SQL
links to: cb1-1
links to: cb1-2
links to: cb1-3
links to: cb1-4
links to: cb1-5
links to: cb1-6
links to: cb1-7
links to: cb1-8
links to: cb1-9
links to: cb1-10
links to: cb1-11
links to: cb1-12
links to: cb1-13
links to: cb1-14
links to: cb1-15
links to: cb1-16
links to: cb1-17
links to: cb1-18
links to: cb1-19
links to: cb1-20
links to: cb2-1
links to: cb2-2
links to: cb2-3
links to: cb2-4
links to: cb2-5
links to: cb2-6
links to: cb2-7
links to: cb2-8
links to: cb2-9
links to: cb2-10
links to: cb2-11
links to: cb2-12
links to: cb5-1
links to: cb5-2
links to: cb5-3
links to: cb5-4
links to: cb5-5
links to: cb5-6
links to: cb5-7
links to: cb5-8
links to: cb5-9
links to: cb5-10
links to: cb5-11
links to: cb5-12
links to: cb5-13
links to: sparql
links to: cb7-1
links to: cb7-2
links to: cb7-3
links to: cb7-4
links to: cb7-5
links to: cb7-6
links to: cb7-7
links to: cb7-8
links to: cb7-9
links to: cb7-10
links to: cb7-11
links to: cb7-12
links to: cb7-13

Mike Bergman
type: User

Mike Bergman
type: Person

SPARQL by Example: The Cheat Sheet

SPARQL in 11 minutes

Learning SPARQL

sparqlwarpper documentation

Wikidata SPARQL query examples

Cooking with Python and KBpedia
label: CWPK listing

KBpedia

direct download

notify me