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: Creating a Platform for Knowledge-based Machine Intelligence
generator agent: http://rdfs.org/sioc/wp-sioc.php

Creating a Platform for Knowledge-based Machine Intelligence
type: Post
has container: weblog
has creator: Mike Bergman
maker: Mike Bergman
created: 2015-09-21T16:41:45Z
content: Practical and Reusable Designs to Make Knowledge Bases Computable Wikipedia is a common denominator in question answering and commercial natural language applications that leverage artificial intelligence. Witness Siri, Watson, Cortana and Google Now, among others. DBpedia is a structured data representation of Wikipedia that makes much of this content machine readable. Wikidata is a multilingual repository of 18 million structured entities now feeding the Wikipedia ecosystem. The availability of these sources is remaking and accelerating the role of knowledge bases in powering the next generation of artificial intelligence applications. But much, much more is possible. All of these noted knowledge bases lack a comprehensive and coherent knowledge structure. They are not computable, nor able to be reasoned over or inferenced. While they are valuable resources for structured data and content, the vast potential in these storehouses remains locked up. Yet the relevance of these sources to drive an artificial intelligence platform geared to data and content is profound. And what makes this potential profound? Well, properly structured, knowledge bases can provide the features and generation of positive and negative training sets useful to machine learning. Coherent organization of the knowledge graph within the KB's domain enables various forms of reasoning and inference, further useful to making fine-grained recognizers, extractors and classifiers applicable to external knowledge. As I have pointed out before with regard to knowledge-based artificial intelligence (or KBAI) [1], these capabilities can work to extract still more accurate structure and knowledge from the knowledge base, creating a virtuous circle of still further improvements. In all fairness, the Wikipedia ecosystem was not designed to be a computable one. But the free and open access to content in the Wikipedia ecosystem has sparked an explosion of academic and commercial interest in using this knowled…
encoded: Practical and Reusable Designs to Make Knowledge Bases Computable Wikipedia is a common denominator in question answering and commercial natural language applications that leverage artificial intelligence. Witness Siri, Watson, Cortana and Google Now, among others. DBpedia is a structured data representation of Wikipedia that makes much of this content machine readable. Wikidata is a multilingual repository of 18 million structured entities now feeding the Wikipedia ecosystem. The availability of these sources is remaking and accelerating the role of knowledge bases in powering the next generation of artificial intelligence applications. But much, much more is possible. All of these noted knowledge bases lack a comprehensive and coherent knowledge structure. They are not computable, nor able to be reasoned over or inferenced. While they are valuable resources for structured data and content, the vast potential in these storehouses remains locked up. Yet the relevance of these sources to drive an artificial intelligence platform geared to data and content is profound. And what makes this potential profound? Well, properly structured, knowledge bases can provide the features and generation of positive and negative training sets useful to machine learning. Coherent organization of the knowledge graph within the KB’s domain enables various forms of reasoning and inference, further useful to making fine-grained recognizers, extractors and classifiers applicable to external knowledge. As I have pointed out before with regard to knowledge-based artificial intelligence (or KBAI) [1], these capabilities can work to extract still more accurate structure and knowledge from the knowledge base, creating a virtuous circle of still further improvements. In all fairness, the Wikipedia ecosystem was not designed to be a computable one. But the free and open access to content in the Wikipedia ecosystem has sparked an explosion of academic and commercial interest in using this knowled…
topic: Adaptive Innovation
topic: Artificial Intelligence
topic: Description Logics
topic: Enterprise-scale Semantic Systems
topic: Knowledge-based Artificial Intelligence
topic: Ontologies
topic: Semantic Web
topic: UMBEL
has reply: comment-506830
links to: Wikipedia
links to: Siri
links to: Watson
links to: Cortana
links to: Google Now
links to: others
links to: DBpedia
links to: Wikidata
links to: [1]
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links to: Kermit the Frog
links to: The Rock
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links to: glossary
links to: SUMO
links to: [13]
links to: OWL
links to: RDF
links to: Message Understanding Conferences
links to: [14]
links to: BBN
links to: [15]
links to: rigid designators
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links to: people
links to: astronauts
links to: explorers
links to: products
links to: manufactured goods
links to: cameras
links to: SLR cameras
links to: digital SLR cameras
links to: [25]
links to: definitive description of “toucan”
links to: keel-billed toucan
links to: full list of toucan species
links to: essences
links to: [26]
links to: [27]
links to: [28]
links to: [29]

Mike Bergman
type: User

Mike Bergman
type: Person

glossary
label: Glossary

SLR cameras
label: Canon EOS 7D Mark II

Structured Dynamics

Knowledge-based Artificial Intelligence

Knowledge Bases in the Age of Big Data Analytics,

SWEETpedia

Wikipedia in Academic Studies

Open Biological and Biomedical Ontologies

Announcing UMBEL: A Lightweight Subject Structure for the Web

Basing UMBEL's Backbone on OpenCyc

Shaping Wikipedia into a Computable Knowledge Base

‘SuperTypes’ and Logical Segmentation of Instances

Metamodeling in Domain Ontologies

Big Structure and Data Interoperability

Making Linked Data Reasonable using Description Logics, Part 1

Extended Entity Types

Overview of MUC-7

"Annotation Guidelines for Answer Types"

DBpedia-A Crystallization Point for the Web of Data

Github

Inside YAGO2: A Transparent Information Extraction Architecture

HYENA: Hierarchical Type Classification for Entity Names

Fine-grained Named Entity Recognition using Conditional Random Fields for Question Answering

Fine-Grained Entity Recognition

Context-Dependent Fine-Grained Entity Type Tagging

Semi-supervised Named Entity Recognition: Learning to Recognize 100 Entity Types with Little Supervision

Give Me a Sign: What Do Things Mean on the Semantic Web?

archaelogy description on Wikipedia

'Natural' Classes in the Knowledge Web

Conceptual and Practical Distinctions in the Attributes Ontology