Truly Tabular RDF

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

# 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

• academic conference
• scientific conference series
• international airport
• sovereign state
• brewery
• continent
• library
• novel series
• million city
• museum
• monastery
• space mission
• gravitational wave events
• supernova
• university