SIOC Post profile for "AI3:::Adaptive Information"
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primary topic: CWPK #30: Extracting Annotations
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CWPK #30: Extracting Annotations
type: Post
has container: weblog
has creator: Mike Bergman
maker: Mike Bergman
created: 2020-09-04T15:35:29Z
content: Everything Can Be Annotated in a Knowledge Graph We've seen in the previous two installments of this Cooking with Python and KBpedia series various ways to specify a subset population for driving an iterative process for extracting structure from KBpedia. We're going to retain that iterative approach, only change it now to extract annotations. Classes, properties, and instances (individuals) may all be annotated in OWL. We thus need to derive generalized approaches that can apply to any entity in a knowledge graph. Annotations are information applied to a given entity in order to point to it, describe it, or identify it. As a best practices matter, there are certain fields we recommend be universally applied to annotate any given entity: A preferred label (prefLabel) that is the standard name or title for a thing A multiple of alternative labels (altLabel) that capture any of the ways a given thing may be referred to, including synonyms, acronyms, jargon, etc. A definition of the thing (definition) All labels should be tagged with a language tag in order to more readily support translation and use in multiple languages. We may also find comments or notes associated with particular items. Further, in the case of object or data properties, we may have additional characterizations such as domain or range or functionality assigned to the item. We could have retrieved these characterizations as part of our structural extractions, but decided to include them rather in an annotation extraction pass (even though those characterizations are not annotative). Items to be Extracted During Annotation Pass We can thus assemble up a list of items that may be extracted during an annotation extraction pass. We could do these extractions in parts, since that is often the better approach during the inverse process of building our knowledge graph. However, given the number of annotation and related items that may be extracted, and the number of combinations of same, we decide as a matt…
encoded: Everything Can Be Annotated in a Knowledge Graph We’ve seen in the previous two installments of this Cooking with Python and KBpedia series various ways to specify a subset population for driving an iterative process for extracting structure from KBpedia. We’re going to retain that iterative approach, only change it now to extract annotations. Classes, properties, and instances (individuals) may all be annotated in OWL. We thus need to derive generalized approaches that can apply to any entity in a knowledge graph. Annotations are information applied to a given entity in order to point to it, describe it, or identify it. As a best practices matter, there are certain fields we recommend be universally applied to annotate any given entity: A preferred label (prefLabel) that is the standard name or title for a thing A multiple of alternative labels (altLabel) that capture any of the ways a given thing may be referred to, including synonyms, acronyms, jargon, etc. A definition of the thing (definition) All labels should be tagged with a language tag in order to more readily support translation and use in multiple languages. We may also find comments or notes associated with particular items. Further, in the case of object or data properties, we may have additional characterizations such as domain or range or functionality assigned to the item. We could have retrieved these characterizations as part of our structural extractions, but decided to include them rather in an annotation extraction pass (even though those characterizations are not annotative). Items to be Extracted During Annotation Pass We can thus assemble up a list of items that may be extracted during an annotation extraction pass. We could do these extractions in parts, since that is often the better approach during the inverse process of building our knowledge graph. However, given the number of annotation and related items that may be extracted, and the number of combinations of same, we decide as a matt…
topic: CWPK
topic: KBpedia
topic: Semantic Web Tools
links to: Cooking with Python and KBpedia
links to: KBpedia
links to: OWL
links to: roundtripping
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
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links to: cb1-10
links to: cb1-11
links to: cb1-12
links to: cb1-13
links to: cb1-14
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links to: cb1-19
links to: cb2-1
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links to: cb2-6
links to: cb2-7
links to: cb2-8
links to: cb2-9
links to: cb4-1
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links to: cb4-6
links to: cb4-7
links to: cb4-8
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links to: cb4-10
links to: cb4-11
links to: owlready2
links to: cb6-1
links to: cb6-2
links to: cb6-3
links to: cb6-4
links to: CWPK #28
links to: CWPK #29
links to: cb8-1
links to: cb8-2
links to: cb8-3
links to: cb8-4
links to: cb8-5
links to: cb8-6
links to: cb8-7
links to: cb8-8
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links to: cb8-12
links to: cb9-1
links to: cb9-2
links to: cb9-3
links to: cb9-4
links to: cb9-5
links to: cb9-6
links to: cb9-7

Mike Bergman
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Mike Bergman
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Cooking with Python and KBpedia
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