Difference between revisions of "Truly Tabular RDF"

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= Querying tabular data from Wikidata =
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A starting point for analyzing the data in a triplestore such as
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wikidata might be a single item of interest such as the "Game of
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Thrones" character [https://www.wikidata.org/wiki/Q3183235 Jon Snow]
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[https://www.wikidata.org/wiki/Property:P31 instance of]
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<graphviz>
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JS [ label="Jon Snow" ]
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io [ label="instance of"]
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GOTC [ label="Game of Thrones character" ]
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JS->io->GOTC
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subject -> predicate -> object
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JS -> subject
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io -> predicate
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GOTC -> object
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</graphviz>
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= Naive SPARQL Query =
 
= Naive SPARQL Query =
 
# Start with a wikidata item your are intested in e.g. [https://www.wikidata.org/wiki/Q109296593 International Semantic Web Conference ISWC 2022]
 
# Start with a wikidata item your are intested in e.g. [https://www.wikidata.org/wiki/Q109296593 International Semantic Web Conference ISWC 2022]

Revision as of 14:53, 31 July 2022

Querying tabular data from Wikidata

A starting point for analyzing the data in a triplestore such as wikidata might be a single item of interest such as the "Game of Thrones" character Jon Snow instance of

Naive SPARQL Query

  1. Start with a wikidata item your are intested in e.g. International Semantic Web Conference ISWC 2022
  2. use the instance of property to find similar items of the same class academic conference
  3. straight-forward select further properties by adding statements similar to 
    OPTIONAL { ?conference wdt:P1813 ?short_name }
    to the WHERE clause.
    1. P1813 short name
    2. P17 country
    3. P1476 title

This naive approach will lead to more results for Step 3 (e.g. 7730) than for step 2 (e.g. 7695) which is a surprise for most novices since this effect would not happen with a similar SQL query 

SELECT short_name,country,title from academic_conference

Result of Step #2

# Academic conference wikidata query
# WF 2021-01-30
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?conference ?conferenceLabel 
WHERE
{
  #  academic conference series (Q2020153)
  ?conference wdt:P31 wd:Q2020153.
  # label
  ?conference rdfs:label ?conferenceLabel filter (lang(?conferenceLabel) = "en").
}

try it!

conference conferenceLabel
http://www.wikidata.org/entity/Q75698988 The 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
http://www.wikidata.org/entity/Q75707991 Digital Humanities 2020
http://www.wikidata.org/entity/Q75709854 Digital Humanities 2018
...

Result of Step 3

# Academic conference wikidata query
# WF 2021-01-30
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT 
  ?conference ?conferenceLabel
  ?short_name
  ?country
  ?title
WHERE
{
  #  academic conference series (Q2020153)
  ?conference wdt:P31 wd:Q2020153.
  # label
  ?conference rdfs:label ?conferenceLabel filter (lang(?conferenceLabel) = "en").
  # short name
  OPTIONAL { ?conference wdt:P1813 ?short_name }
  # country
  OPTIONAL { ?conference wdt:P17 ?country }
  # title
  OPTIONAL { ?conference wdt:P1476 ?title }
}

try it!

More elaborate example: novel series

  1. start with Lord of the Rings
  2. find similar Novel Series

Naive SPARQL Query

# truly tabular query for 
# Q1667921:novel series
# generated by trulytabular.py on 2022-07-27T17:33:43.681991
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?novel_series ?novel_seriesLabel
  ?instance_of
  ?language_of_work_or_name
  ?genre
  ?author
  ?country_of_origin
  ?has_part_s_
  ?publication_date
  ?Freebase_ID
  ?ISFDB_series_ID
  ?title
  ?Google_Knowledge_Graph_ID
WHERE {
  # instanceof Q1667921:novel series
  ?novel_series wdt:P31 wd:Q1667921.
  # label
  ?novel_series rdfs:label ?novel_seriesLabel  
  FILTER (LANG(?novel_seriesLabel) = "en").
  # instance of (P31)
  OPTIONAL { ?novel_series wdt:P31 ?instance_of. }
  # language of work or name (P407)
  OPTIONAL { ?novel_series wdt:P407 ?language_of_work_or_name. }
  # genre (P136)
  OPTIONAL { ?novel_series wdt:P136 ?genre. }
  # author (P50)
  OPTIONAL { ?novel_series wdt:P50 ?author. }
  # country of origin (P495)
  OPTIONAL { ?novel_series wdt:P495 ?country_of_origin. }
  # has part(s) (P527)
  OPTIONAL { ?novel_series wdt:P527 ?has_part_s_. }
  # publication date (P577)
  OPTIONAL { ?novel_series wdt:P577 ?publication_date. }
  # Freebase ID (P646)
  OPTIONAL { ?novel_series wdt:P646 ?Freebase_ID. }
  # ISFDB series ID (P1235)
  OPTIONAL { ?novel_series wdt:P1235 ?ISFDB_series_ID. }
  # title (P1476)
  OPTIONAL { ?novel_series wdt:P1476 ?title. }
  # Google Knowledge Graph ID (P2671)
  OPTIONAL { ?novel_series wdt:P2671 ?Google_Knowledge_Graph_ID. }
}

try it!

Aggregate SPARQL Query with SAMPLE

# truly tabular query for 
# Q1667921:novel series
# generated by trulytabular.py on 2022-07-27T17:33:43.681991
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT DISTINCT ?novel_series ?novel_seriesLabel
  (SAMPLE (?instance_of) AS ?instance_of  )
  (SAMPLE (?language_of_work_or_name) AS ?language_of_work_or_name)
  (SAMPLE (?genre) AS ?genre)
  (SAMPLE (?author) AS ?author)
  (SAMPLE (?country_of_origin) AS ?country_of_origin)
  (SAMPLE (?has_part_s_) AS ?has_part_s_)
  (SAMPLE (?publication_date) AS ?publication_date)
  (SAMPLE (?Freebase_ID) AS ?Freebase_ID)
  (SAMPLE (?ISFDB_series_ID) AS ?ISFDB_series_ID)
  (SAMPLE (?title) AS ?title )
  (SAMPLE (?Google_Knowledge_Graph_ID) AS ?Google_Knowledge_Graph_ID)
WHERE {
  # instanceof Q1667921:novel series
  ?novel_series wdt:P31 wd:Q1667921.
  # label
  ?novel_series rdfs:label ?novel_seriesLabel  
  FILTER (LANG(?novel_seriesLabel) = "en").
  # instance of (P31)
  OPTIONAL { ?novel_series wdt:P31 ?instance_of. }
  # language of work or name (P407)
  OPTIONAL { ?novel_series wdt:P407 ?language_of_work_or_name. }
  # genre (P136)
  OPTIONAL { ?novel_series wdt:P136 ?genre. }
  # author (P50)
  OPTIONAL { ?novel_series wdt:P50 ?author. }
  # country of origin (P495)
  OPTIONAL { ?novel_series wdt:P495 ?country_of_origin. }
  # has part(s) (P527)
  OPTIONAL { ?novel_series wdt:P527 ?has_part_s_. }
  # publication date (P577)
  OPTIONAL { ?novel_series wdt:P577 ?publication_date. }
  # Freebase ID (P646)
  OPTIONAL { ?novel_series wdt:P646 ?Freebase_ID. }
  # ISFDB series ID (P1235)
  OPTIONAL { ?novel_series wdt:P1235 ?ISFDB_series_ID. }
  # title (P1476)
  OPTIONAL { ?novel_series wdt:P1476 ?title. }
  # Google Knowledge Graph ID (P2671)
  OPTIONAL { ?novel_series wdt:P2671 ?Google_Knowledge_Graph_ID. }
} GROUP BY ?novel_series ?novel_seriesLabe

How tabular are the Academic Conference entries in wikidata?

Result as of 2022-03

property total f1 total% non tabular non tabular% f2 f3 f14 f4 f7 f5 f9
∑ 7518
short name 6750 6741 89.8 9 0.1 9
country 7077 7077 94.1 0 0
title 6718 6700 89.4 18 0.3 10 8
part of the series 7139 7120 95 19 0.3 15 4
VIAF ID 2096 2092 27.9 4 0.2 3 1
GND ID 3049 3043 40.6 6 0.2 4 2
location 7209 7180 95.9 29 0.4 24 4 1
start time 6916 6914 92 2 0 2
end time 6912 6909 91.9 3 0 3
official website 596 586 7.9 10 1.7 9 1
main subject 1882 1722 25 160 8.5 131 23 2 2 1 1
described at URL 6512 6510 86.6 2 0 1 1
language used 87 84 1.2 3 3.4 3
is proceedings from 921 901 12.3 20 2.2 16 3 1
WikiCFP event ID 98 98 1.3 0 0

Truly tabular examples

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