Using Artificial Intelligence to get from Open Data to Linked Open Data

Situation Analysis

AI (Artificial Intelligence) is fundamentally about Software Agents (and the machines they drive) being able to perform Reasoning & Inference against data from a variety of sources in varying circumstances.

The city of Boston has recently published a vast collection of Open Data. One of these datasets is about the locations of electric car charging stations.

Challenge

One significant challenge is that the fields (a/k/a attributes, relationship types, or relations) used to construct longitude and latitude data have identifiers that are too tightly scoped to the CSV document in which they were originally published.

Solution

Produce a Linked Open Data rendition of the data without making any changes to the source data and without losing sensitivity to changes to the source over time.

Linked Open Data implications

Every entity (including entity relationship types) is identified using a HTTP URI (hyperlink)
Entity descriptions take the form of a collection of RDF Language sentences/statements

How?

By producing a set of built-in and/or custom inference rules for an RDF-aware Software Agent (e.g., Virtuoso) that enables it to generate the change-sensitive Linked Open Data rendition.

Note: in this post, built-in inference rules are the key to the solution; custom inference rules will be addressed in part 2, demonstrating an alternative approach.

Steps using Built-In Inference Rules

Create a rule using rdfs:subProperty relations that map the local field names (relations, attributes, properties, predicates) for longitude and latitude to geo:lat and geo:long terms from the Geo Ontology.
Ingest the electric car charging station data using a live instance of the Virtuoso Sponger (Linked Open Data Middleware service).
Produce Linked Open Data using a SPARQL Query (leveraging the Inference Rule pragma) against the electric car charging station dataset.

SPARQL Query with Built-In Inference Rule Enabled

## Context Rule for Built-In Reasoning & Inference 
## See: http://kingsley.idehen.net/DAV/home/kidehen/Public/SPASQL/built-in-inference-rules/geo-data.sql
DEFINE input:inference "urn:geospatial:cleanup:inference:rules"
DEFINE get:soft "soft"
PREFIX  geo:  <http://www.w3.org/2003/01/geo/wgs84_pos#>
SELECT DISTINCT 
   ?s1 AS ?webid
   ?s3 AS ?latitude 
   ?s2 AS ?longitude 
   ?s4 AS ?name 
   ?s8 AS ?address
   ?s6 AS ?fuelType
   ?s5 AS ?city 
FROM <http://bostonopendata-boston.opendata.arcgis.com/datasets/465e00f9632145a1ad645a27d27069b4_2.csv>
WHERE {
 ?s1 a <http://bostonopendata-boston.opendata.arcgis.com/datasets/465e00f9632145a1ad645a27d27069b4_2.csv#class> .
 ?s1 <http://bostonopendata-boston.opendata.arcgis.com/datasets/465e00f9632145a1ad645a27d27069b4_2.csv#Longitude> ?s2 .
 ?s1 <http://bostonopendata-boston.opendata.arcgis.com/datasets/465e00f9632145a1ad645a27d27069b4_2.csv#Latitude> ?s3 .
 ?s1 <http://bostonopendata-boston.opendata.arcgis.com/datasets/465e00f9632145a1ad645a27d27069b4_2.csv#Station_Name> ?s4 .
 ?s1 <http://bostonopendata-boston.opendata.arcgis.com/datasets/465e00f9632145a1ad645a27d27069b4_2.csv#City> ?s5 .
 ?s1 <http://bostonopendata-boston.opendata.arcgis.com/datasets/465e00f9632145a1ad645a27d27069b4_2.csv#Fuel_Type_Code> ?s6 .
 ?s1 <http://bostonopendata-boston.opendata.arcgis.com/datasets/465e00f9632145a1ad645a27d27069b4_2.csv#Status_Code> ?s7 .
 ?s1 <http://bostonopendata-boston.opendata.arcgis.com/datasets/465e00f9632145a1ad645a27d27069b4_2.csv#Street_Address> ?s8 .
 ?s1 geo:lat ?lat1 ; 
     geo:long ?lng1 .
}