SIOC Post profile for "AI3:::Adaptive Information"
type: Document
description: A SIOC profile describes the structure and contents of a weblog in a machine readable form. For more information please refer to http://sioc-project.org/.
primary topic: Uses and Control of Inferencing in Knowledge Graphs
generator agent: http://rdfs.org/sioc/wp-sioc.php

Uses and Control of Inferencing in Knowledge Graphs
type: Post
has container: weblog
has creator: Mike Bergman
maker: Mike Bergman
created: 2017-03-15T17:10:41Z
content: Dialing In Queries from the General to the Specific Inferencing is a common term heard in association with semantic technologies, but one that is rarely defined and still less frequently described as to value and rationale. I try to redress this gap in part with this article. Inferencing is the drawing of new facts, probabilities or conclusions based on reasoning over existing evidence. Charles Sanders Peirce classed inferencing into three modes: deductive reasoning, inductive reasoning and abductive reasoning. Deductive reasoning extends from premises known to be true and clear to infer new facts. Inductive reasoning looks at the preponderance of evidence to infer what is probably true. And abductive reasoning poses possible explanations or hypotheses based on available evidence, often winnowing through the possibilities based on the total weight of evidence at hand or what is the simplest explanation. Though all three reasoning modes may be applied to knowledge graphs, the standard and most used form is deductive reasoning. An inference engine may be applied to a knowledge graph and its knowledge bases in order to deduce new knowledge. Inference engines apply either backward- or forward-chaining deductive reasoning. In backward chaining, the reasoning tests are conducted "backwards" from a current consequent or "fact" to determine what antecedents can support that conclusion, based on the rules used to construct the graph. ("What reasons bring us to this fact?") In forward chaining the opposite occurs; namely, a goal or series of goals are stated and then existing facts (as rules) are checked to see which ones can lead to the goal. (" A goal X may be possible because of?") The process is iterated until the goal is reached or not; if reached, new knowledge in terms of heretofore unstated connections may be added to the knowledge base. Inference engines can be applied at the time of graph building or extension to test the consistency and logic of the new additions. …
encoded: Dialing In Queries from the General to the Specific Inferencing is a common term heard in association with semantic technologies, but one that is rarely defined and still less frequently described as to value and rationale. I try to redress this gap in part with this article. Inferencing is the drawing of new facts, probabilities or conclusions based on reasoning over existing evidence. Charles Sanders Peirce classed inferencing into three modes: deductive reasoning, inductive reasoning and abductive reasoning. Deductive reasoning extends from premises known to be true and clear to infer new facts. Inductive reasoning looks at the preponderance of evidence to infer what is probably true. And abductive reasoning poses possible explanations or hypotheses based on available evidence, often winnowing through the possibilities based on the total weight of evidence at hand or what is the simplest explanation. Though all three reasoning modes may be applied to knowledge graphs, the standard and most used form is deductive reasoning. An inference engine may be applied to a knowledge graph and its knowledge bases in order to deduce new knowledge. Inference engines apply either backward- or forward-chaining deductive reasoning. In backward chaining, the reasoning tests are conducted “backwards” from a current consequent or “fact” to determine what antecedents can support that conclusion, based on the rules used to construct the graph. (“What reasons bring us to this fact?”) In forward chaining the opposite occurs; namely, a goal or series of goals are stated and then existing facts (as rules) are checked to see which ones can lead to the goal. (” A goal X may be possible because of?”) The process is iterated until the goal is reached or not; if reached, new knowledge in terms of heretofore unstated connections may be added to the knowledge base. Inference engines can be applied at the time of graph building or extension to test the consistency and logic of the new additions. …
topic: Cognonto
topic: KBpedia
topic: Searching
topic: Semantic Web
links to: cognonto.com
links to: Inferencing
links to: Charles Sanders Peirce
links to: Deductive reasoning
links to: Inductive reasoning
links to: abductive reasoning
links to: knowledge graphs
links to: Inference engines
links to: backward chaining
links to: forward chaining
links to: semantic reasoners
links to: Cognonto
links to: KBpedia knowledge structure
links to: OWL 2
links to: FOL
links to: description logics
links to: [1]
links to: SPARQL
links to: SWRL
links to: OWL API
links to: [2]
links to: [3]
links to: previous use case
links to: knowledge graph
links to: [4]
links to: <http://kbpedia.org/1.40/>
links to: <http://www.w3.org/2004/02/skos/core#altLabel>
links to: <http://kbpedia.org/kko/rc/OntologyInformationScience>
links to: type
links to: Class
links to: isDefinedBy
links to: rc
links to: <http://www.w3.org/2000/01/rdf-schema#subClassOf>
links to: KnowledgeRepresentation-CW
links to: Ontology
links to: <http://wikipedia.org/wiki/Ontology_%28information_science%29>
links to: prefLabel
links to: definition
links to: superClassOf
links to: PropositionalConceptualWork
links to: Knowledge representation
links to: Mx4r4e 7xpGBQdmREI4QPyn0Gw
links to: StructuredInformationSource
links to: ClassificationSystem
links to: Mx4rv7D EBSHQdiLMuoH7dC2KQ
links to: Technology-Artifact
links to: Q324254
links to: [5]
links to: Montague grammars
links to: Use Cases
links to: A Description Logic Primer
links to: Description Logics
links to: 'Natural Classes' in the Knowledge Web
links to: Rationales for Typology Designs in Knowledge Bases
links to: SPARQL 1.1 Query Language
links to: property paths
links to: Owl 2 and SWRL Tutorial

Mike Bergman
type: User

Mike Bergman
type: Person

KBpedia knowledge structure
label: KBpedia knowledge graph

<http://wikipedia.org/wiki/Ontology_%28information_science%29>
label: http://wikipedia.org/wiki/Ontology_(information_science)