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: Big Structure and Data Interoperability
generator agent: http://rdfs.org/sioc/wp-sioc.php

Big Structure and Data Interoperability
type: Post
has container: weblog
has creator: Mike Bergman
maker: Mike Bergman
created: 2014-08-18T14:51:15Z
content: A Critical Fit with the Semantic Web and AI Download PDF In the first parts of this series we introduced the idea of Big Structure, and the fact that it resides at the nexus of the semantic Web, artificial intelligence, natural language processing, knowledge bases, and Big Data. In this article, we look specifically at the work that Big Structure promotes in data interoperability as a way to clarify what the roles these various aspects play. By its nature, data integration (the first step in data interoperability) means that data is being combined across two or more datasets. Such integration surfaces all of the myriad aspects of semantic heterogeneities, exactly the kinds of issues that the semantic Web and semantic technologies were designed to address. But resolving semantic differences can not be fulfilled by semantic technologies alone. While semantics can address the basis of differences in meaning and context, resolution of those differences or deciding between differing interpretations (that is, ambiguity) also requires many of the tools of artificial intelligence or natural language processing (NLP). By decomposing this space into its various sources of semantic heterogeneities -- as well as the work required in order to provide for such functions as search, disambiguation, mapping and transformations -- we can begin to understand how all of these components can work together in order to help achieve data interoperability. This understanding, in turn, is essential to understand the stack and software architecture -- and its accompanying information architecture -- in order to best achieve these interoperability objectives. So, this current article lays out this conceptual framework of components and roles. Later articles in this series will address the specific questions of software and information architectural design. Data Interoperability in Relation to Semantics Semantic technologies give us the basis for understanding differences in meaning across sour…
encoded: A Critical Fit with the Semantic Web and AI Download PDF In the first parts of this series we introduced the idea of Big Structure, and the fact that it resides at the nexus of the semantic Web, artificial intelligence, natural language processing, knowledge bases, and Big Data. In this article, we look specifically at the work that Big Structure promotes in data interoperability as a way to clarify what the roles these various aspects play. By its nature, data integration (the first step in data interoperability) means that data is being combined across two or more datasets. Such integration surfaces all of the myriad aspects of semantic heterogeneities, exactly the kinds of issues that the semantic Web and semantic technologies were designed to address. But resolving semantic differences can not be fulfilled by semantic technologies alone. While semantics can address the basis of differences in meaning and context, resolution of those differences or deciding between differing interpretations (that is, ambiguity) also requires many of the tools of artificial intelligence or natural language processing (NLP). By decomposing this space into its various sources of semantic heterogeneities — as well as the work required in order to provide for such functions as search, disambiguation, mapping and transformations — we can begin to understand how all of these components can work together in order to help achieve data interoperability. This understanding, in turn, is essential to understand the stack and software architecture — and its accompanying information architecture — in order to best achieve these interoperability objectives. So, this current article lays out this conceptual framework of components and roles. Later articles in this series will address the specific questions of software and information architectural design. Data Interoperability in Relation to Semantics Semantic technologies give us the basis for understanding differences in meaning across sources,…
topic: Adaptive Information
topic: Big Structure
topic: Description Logics
topic: Knowledge-based Artificial Intelligence
topic: Linked Data
topic: Ontologies
topic: Semantic Web
topic: UMBEL
links to: Download PDF
links to: first
links to: parts
links to: series
links to: Big Structure
links to: semantic Web
links to: artificial intelligence
links to: natural language processing
links to: knowledge bases
links to: Big Data
links to: data interoperability
links to: data integration
links to: [1]
links to: [2]
links to: [4]
links to: [3]
links to: Lola
links to: Ray Davies
links to: Kinks
links to: Berlin
links to: John Kennedy
links to: open world ones
links to: [5]
links to: [6]
links to: lingua franca
links to: description logics
links to: TBox
links to: ABox
links to: [7]
links to: W3C
links to: linked data,
links to: [8]
links to: [9]
links to: Solr
links to: [10]
links to: machine learning
links to: [11]
links to: functional programming languages
links to: machine-learning algorithms.
links to: MDM
links to: REP-2000-396.pdf
links to: sources-and-classification-of-semantic-heterogeneities
links to: InternationalizationTutorial060323.pdf
links to: when-linked-data-rules-fail
links to: climbing-the-data-federation-pyramid
links to: thinking-inside-the-box-with-description-logics
links to: 0129.html
links to: UMBEL
links to: SUMO
links to: DOLCE
links to: PROTON
links to: Cyc
links to: BFO
links to: Roget’s Thesaurus
links to: upper ontologies
links to: spring-dawns-on-artificial-intelligence
links to: ontology-driven-apps-using-generic-applications

Mike Bergman
type: User

Mike Bergman
type: Person

[3]
label: 3]