About: My Response to 'Messy Data' post by Curt Monash

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type	Comment
label	My Response to 'Messy Data' post by Curt Monash
seeAlso	http://www.dbms2.com/2015/08/03/data-messes/#comment-2236561
References	Conceptual Data Virtualization across Disparate SQL Databases Data & Semantics Demonstrating Reasoning & Inference using SPARQL http://www.dbms2.com/2015/08/03/data-messes/
described by	http://kingsley.idehen.net/DAV/home/kidehen/Public/Linked%20Data%20Documents/Nanotations/random-messy-data-notes.ttl
comment	There isn't a silver bullet for solving data integration issues. Data is always subjectively messy. Thus, you need "context lenses" through which relevant data is viewed en route to final processing by data consuming applications. The following principles provide an effective basis for addressing these challenges: 1. Use HTTP URIs as Entity Names (Identifiers) -- these implicitly resolve to entity description documents 2. Use an abstract daa representation language (e.g., RDF) to describe entities using subject, predicate, object based sentences -- in a notation of your choice (i.e., it doesn't have to be JSON, JSON-LD, Turtle, RDF/XML etc..). 3. Ensure the nature of Entity Types and Relationship Types are also described using same abstract data representation language -- this relates to Classes, Sub Classes, Transitivity, Symmetry, Equivalence etc.. The links that follow include live examples of this approach to data integration across disparate data sources: Links: [1] http://kidehen.blogspot.com/2015/07/conceptual-data-virtualization-across.html -- deals with integrating data across disparate RDBMS systems [2] http://kidehen.blogspot.com/2015/07/situation-analysis-never-day-goes-by.html -- show how controlled natural language can be used to harmonize disparate data [3] http://kidehen.blogspot.com/2014/01/demonstrating-reasoning-via-sparql.html -- demonstrating reasoning and inference based on entity relationship type semantics, using SPARQL .

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