User:AKhatun/Wikidata Subgraph Query Analysis

Analysis on Subgraphs in Wikidata showed how large each of the subgraphs are in Wikidata and how connected they are. This page shows the results from analysis on the queries that relate to these subgraph. The questions that needed to be answered were:

• How many(percent) queries access each subgraph?
• How many queries access multiple subgraphs at once? i.e, how much overlap can we expect in subgraphs?
• How long do these queries take?
• How many user-agents access each subgraph? How many of them access lots of subgraphs, or are they confined to a small set of subgraphs? Do some of them dominate queries in mutiple subgraphs?
• Are there chunks of similar queries in these subgraphs? i.e, how diverse the queries in each subgraph are.

What are subgraph related queries

We define some parameters to identify whether a query touches on a subgraph based on the items and properties a query uses. Some queries may even touch on mutiple subgraphs. See more on what a subgraph means here. Note: Subgraphs have overlaps.

The parameters that define which subgraph a query belongs to are:

1. If the query uses the subgraph's Qid. Example: Q5 containing queries are part of Q5 subgraph.
2. If the query uses items that are instance of a particular subgraph.
3. If the query uses items that occur 99% of the times in a particular subgraph.
4. If the query uses properties that occur 99% of the times in a particular subgraph.
5. If the query uses literals that occur 99% of the times in a particular subgraph. The literals can occur with or without language tags. Both versions are compared to check for match. Note that whole literals are matched in queries and Wikidata. Queries that ask for partial matches, using regex for example, are not included. The assumption is that such queries are more likely to contain other items from a subgraph and are caught anyways. This of course excludes generic queries that wish to search the entirety of Wikidata for a certain label or other strings.

The following analysis uses Wikidata dump of 20211101 and WDQS public SPARQL queries of 10/2021. All query related values below are monthly counts.

Query count and time

• All queries here refer to queries with status code 200 and 500, i.e correct queries, successful or time-out.
• WDQS receives ~220M queries a month.
• Total query time for all queries for a month is ~16,000 hours.

The table below lists the top 50 most queried subgraphs with subgraph size and query time information. A breakdown of what caused the match is also present, which corresponds to the parameters mentioned in #What are subgraph related queries. It also ranks the subgraphs by size, query count, and query time consumed.

A more complete list containing 341 subgraphs, that form ~90% of Wikidata triples, is available here: [csvlink|all_subgraph_data.csv]

Top 50 most queries subgraphs in Wikidata with subgraph size information

Subgraph rank by size	Subgraph rank by query count	Subgraph rank by query time	Subgraph	Subgraph label	%of triples	%of entities	Query count	%count of all queries	Query time (hr)	%time of all queries	%count of query from Qid	%count of query from instance items	%count of query from items	%count of query from properties	%count of query from literals
3	1	1	Q5	human	7.324	9.986	68,659,369	31.058	6314	0.393	1.827	17.705	10.324	20.176	1.11
5	2	4	Q16521	taxon	2.871	3.427	56,437,140	25.529	495	0.031	22.986	1.251	23.665	0.965	0.496
6	3	3	Q101352	family name	1.546	0.509	5,564,173	2.517	640	0.04	0.064	2.425	2.34	0.016	0.032
15	4	2	Q11424	film	0.364	0.281	4,757,084	2.152	1613	0.1	0.563	1.308	1.089	0.008	0.407
34	5	7	Q4830453	business	0.108	0.207	4,041,395	1.828	343	0.021	0.953	0.788	0.416	0.0	0.101
7	6	9	Q4167410	Wikimedia disambiguation page	1.374	1.459	3,737,550	1.691	223	0.014	0.195	0.484	0.554	0.0	0.938
177	7	20	Q34770	language	0.013	0.011	1,713,196	0.775	73	0.005	0.008	0.757	0.009	0.0	0.005
1	8	13	Q13442814	scholarly article	49.668	39.794	1,649,268	0.746	142	0.009	0.005	0.261	0.278	0.124	0.386
4	9	17	Q4167836	Wikimedia category	5.85	5.165	1,383,343	0.626	96	0.006	0.019	0.594	0.152	0.0	0.01
10	10	14	Q11173	chemical compound	0.693	1.302	1,307,852	0.592	133	0.008	0.022	0.548	0.449	0.001	0.014
20	11	22	Q13406463	Wikimedia list article	0.252	0.352	1,283,160	0.58	73	0.005	0.018	0.409	0.357	0.0	0.048
63	12	6	Q5398426	television series	0.055	0.062	1,206,285	0.546	366	0.023	0.05	0.332	0.252	0.0	0.128
243	13	24	Q14204246	Wikimedia project page	0.008	0.033	1,114,113	0.504	62	0.004	0.009	0.227	0.016	0.0	0.275
92	14	11	Q6881511	enterprise	0.036	0.052	943,613	0.427	164	0.01	0.034	0.338	0.144	0.0	0.042
26	15	29	Q484170	commune of France	0.18	0.043	866,766	0.392	46	0.003	0.006	0.278	0.004	0.098	0.007
165	16	12	Q891723	public company	0.015	0.013	837,595	0.379	157	0.01	0.034	0.277	0.061	0.0	0.054
12	17	19	Q3305213	painting	0.432	0.578	834,752	0.378	79	0.005	0.012	0.332	0.187	0.005	0.012
91	18	16	Q43229	organization	0.037	0.08	806,840	0.365	123	0.008	0.128	0.213	0.097	0.0	0.012
89	19	8	Q4164871	position	0.037	0.128	788,077	0.356	332	0.021	0.004	0.343	0.016	0.0	0.003
28	20	30	Q482994	album	0.161	0.287	776,845	0.351	37	0.002	0.012	0.287	0.209	0.0	0.016
86	21	23	Q47461344	written work	0.038	0.078	774,947	0.351	67	0.004	0.244	0.085	0.039	0.0	0.003
62	22	35	Q7889	video game	0.056	0.047	741,401	0.335	30	0.002	0.006	0.195	0.256	0.005	0.007
16	23	21	Q486972	human settlement	0.302	0.602	721,789	0.327	73	0.005	0.095	0.22	0.107	0.0	0.006
8	24	18	Q7187	gene	0.927	1.273	628,916	0.284	94	0.006	0.107	0.063	0.007	0.021	0.113
25	25	46	Q532	village	0.201	0.292	584,789	0.265	21	0.001	0.001	0.246	0.109	0.0	0.013
70	26	27	Q732577	publication	0.048	0.076	512,416	0.232	53	0.003	0.229	0.003	0.23	0.0	0.0
42	27	45	Q7725634	literary work	0.077	0.176	468,204	0.212	22	0.001	0.017	0.16	0.104	0.0	0.007
138	28	57	Q18340514	events in a specific year or time period	0.019	0.048	463,683	0.21	17	0.001	0.0	0.2	0.056	0.0	0.005
54	29	60	Q215380	musical group	0.063	0.087	461,181	0.209	17	0.001	0.009	0.164	0.073	0.0	0.008
2	30	28	Q6999	astronomical object	8.75	8.942	448,032	0.203	51	0.003	0.0	0.175	0.085	0.015	0.003
41	31	56	Q22808320	Wikimedia human name disambiguation page	0.078	0.075	433,986	0.196	17	0.001	0.0	0.174	0.154	0.0	0.001
53	32	63	Q134556	single	0.065	0.103	431,003	0.195	16	0.001	0.001	0.167	0.138	0.0	0.004
37	33	32	Q3331189	version, edition, or translation	0.087	0.19	410,352	0.186	34	0.002	0.103	0.053	0.118	0.004	0.028
31	34	41	Q16970	church building	0.129	0.226	396,936	0.18	25	0.002	0.005	0.172	0.112	0.0	0.001
71	35	25	Q86850539	Whitaker's Latin frequency type C	0.048	0.011	355,247	0.161	56	0.003	0.0	0.0	0.0	0.0	0.16
11	36	65	Q8054	protein	0.67	1.05	349,573	0.158	16	0.001	0.079	0.034	0.002	0.02	0.066
49	37	167	Q2225692	fourth-level administrative division in Indonesia	0.07	0.088	344,964	0.156	5	0.0	0.0	0.147	0.098	0.0	0.009
223	38	87	Q571	book	0.009	0.022	340,900	0.154	12	0.001	0.114	0.016	0.01	0.0	0.023
112	39	76	Q476028	association football club	0.026	0.038	320,422	0.145	14	0.001	0.006	0.12	0.029	0.0	0.003
21	40	10	Q2668072	collection	0.248	0.534	312,822	0.142	166	0.01	0.056	0.084	0.058	0.0	0.001
113	41	54	Q15632617	fictional human	0.026	0.056	306,319	0.139	18	0.001	0.006	0.1	0.05	0.0	0.003
121	42	42	Q3957	town	0.023	0.015	294,685	0.133	24	0.001	0.047	0.079	0.014	0.0	0.002
133	43	58	Q506240	television film	0.02	0.019	290,899	0.132	17	0.001	0.009	0.098	0.07	0.0	0.02
136	44	5	Q15416	television program	0.019	0.05	286,609	0.13	386	0.024	0.024	0.084	0.072	0.0	0.01
72	45	79	Q105543609	musical work/composition	0.048	0.099	285,889	0.129	13	0.001	0.004	0.095	0.061	0.004	0.009
64	46	38	Q811979	architectural structure	0.055	0.119	282,739	0.128	28	0.002	0.09	0.035	0.024	0.0	0.001
23	47	51	Q4022	river	0.219	0.425	280,190	0.127	20	0.001	0.002	0.12	0.045	0.0	0.002
32	48	31	Q41176	building	0.125	0.287	271,666	0.123	36	0.002	0.034	0.084	0.065	0.002	0.001
45	49	50	Q55488	railway station	0.075	0.104	258,862	0.117	20	0.001	0.001	0.109	0.072	0.0	0.001
192	50	143	Q3464665	television series season	0.011	0.02	254,318	0.115	6	0.0	0.031	0.077	0.009	0.0	0.0

More on query time

The query time can be broken down to classes for better visualization. Below is a figure with the query class distribution (number of queries per query time class per subgraph) for the top 50 subgraphs. Some of the takeaways are:

• Most subgraphs have most queries in the range of 10-100ms
• Second most commons class is 100ms to 1s
• collection and photograph have most queries (~150k) timed at 1-10s. Around 10 more subgraphs have a little (~10-20k) query in this time range.

User agent

Analysis on user-agent is an approximation because these don't completely represent distint users. For example lots people use the same bot or script without changing the user-agent, or the same person or bot uses multiple user-agent strings. Yet based on the available data we can get an idea nevertheless.

• Total number of unique user agents across all subgraphs: 981,180
• First, a list of subgraphs with most and least distinct user-agents is listed. It seems the least number of user-agents a subgraph has is atleast 10. So the large subgraphs are used by mutiple users.
• The largest numbers of user-agents are present in a variety of type of subgraphs, gene-protien-biological_process-molecular_function appear to be similar among them. It is possible the same queries represent several of these subgraphs. More on subgraph connectivity in #Subgraph Connectivity.

Subgraphs with most user-agents Subgraph Subgraph label %Query #User agents %User agent Q11424 film 2.152 251420 0.256 Q8054 protein 0.158 234659 0.239 Q7187 gene 0.284 187029 0.191 Q2996394 biological process 0.072 124415 0.127 Q14860489 molecular function 0.044 89445 0.091 Q5 human 31.058 55377 0.056 Q898273 protein domain 0.019 38484 0.039 Q16521 taxon 25.529 25193 0.026 Q86850539 Whitaker's Latin frequency type C 0.161 20158 0.021 Q4167410 Wikimedia disambiguation page 1.691 13818 0.014 Q14204246 Wikimedia project page 0.504 13443 0.014 Q476028 association football club 0.145 12086 0.012 Q235557 file format 0.045 7701 0.008 Q1520033 count noun 0.05 7662 0.008 Q417841 protein family 0.007 4906 0.005 Q484170 commune of France 0.392 4764 0.005 Q4830453 business 1.828 4383 0.004 Q4164871 position 0.356 4319 0.004 Q7278 political party 0.109 4073 0.004 Q3918 university 0.104 3565 0.004

Subgraphs with least user-agents Subgraph Subgraph label %Query #User agents %User agent Q106006703 local regulations of the People's Republic of China 0.0 11 0.0 Q67015940 Government Boys' Primary School 0.0 13 0.0 Q7604693 Statutory Rules of Northern Ireland 0.0 13 0.0 Q106474968 ethnic group by settlement in Macedonia 0.003 15 0.0 Q6453643 decree law 0.0 15 0.0 Q97695005 committee group motion 0.0 15 0.0 Q100532807 Irish Statutory Instrument 0.0 16 0.0 Q10429085 report 0.0 19 0.0 Q99045339 written question 0.0 20 0.0 Q1505023 Interpellation 0.0 20 0.0 Q96739634 individual motion 0.0 21 0.0 Q67035425 ASTM standard 0.0 21 0.0 Q61278455 health sub-centre 0.001 23 0.0 Q26267864 Wikimedia KML file 0.005 23 0.0 Q3508250 Syndicat intercommunal 0.02 24 0.0 Q107102664 cell line from embryonic stem cells 0.0 24 0.0 Q7604686 UK Statutory Instrument 0.0 27 0.0 Q6451276 Congressional Research Service report 0.001 28 0.0 Q61443650 sub post office 0.0 33 0.0 Q26894053 basketball team season 0.009 34 0.0

• There are 50 subgraphs with more than 1000 user agents, and 300 subgraphs with less than 1000 user agents. Most subgraphs are therefore not queried overly-widely. The distribution of user-agent counts less than 1000 is shown in the figure below. This clearly shows the small number of user counts in most subgraphs.

• Next, the user agent vs query count distribution was analyzed for some of the top subgraphs. While user agent count gives us an idea of how many users may be using a subgraph, it is not clear whether all of them query the subgraph equally, or very few user agents perform most of the queries.
• ~30 out of 341 subgraphs have a user agent that queries >=50% of all queries of that particular subgraphs.
• 6 subgraphs have a user agent querying around 80-90% of the time.
• So the trend of dominating single source queries is not wide spread among subgraphs, but is present in few subraphs nonetheless.

The figure below shows the top 2 user-agent query percents for 341 subgraphs. This shows whether there is a dominating pattern in a subgraph with the top user agents per subgraph. This figure shows the top 2 user-agent query percents for 341 subgraphs. This shows whether there is a dominating pattern in a subgraph with the top user agents per subgraph.

The figure below shows 100 subgraphs with their user agent query usage distribution in percents. Usage greater than 50% is marked in red. A birds-eye view of the plots shows how some subgraphs have a dominating user agent while most subgraphs have at least 1 or 2 user agents that query the most. The rest of the user agents form the long tail of the distribution This figure shows 100 subgraphs with their user agent query usage distribution in percents. Usage greater than 50% is marked in red. A birds-eye view of the plots shows how some subgraphs have a dominating user agent while most subgraphs have at least 1 or 2 user agents that query the most. The rest of the user agents form the long tail 10% of the distribution.

User agent vs Subgraph

So far we have explored the user-agent count and distribution per subgraph. It is also important to note the user agent's query across subgraphs. In other words,

• Do users have a very specific use case and so the queries spans only a few subgraphs? or is it spread across a lot of subgraphs?
• Are there some user agents that query the most in mutiple subgraphs? This could be due to the nature of the use case or simply because several subgraphs overlap a lot.

We start by looking at how many user agents acces how many subgraphs. From the table below, we see that most user agents (89% of them) query one subgraphs only. Some user agents query a lot of subgraphs as well. A clearer picture is seem from the plot below.

Relationship between subgraphs and user agents #of Subgraphs (X) #of User agents querying X subgraphs %of User agents querying X subgraphs 1 875724 89.252 2 91962 9.373 5 3562 0.363 3 2388 0.243 6 1539 0.157 7 799 0.081 9 628 0.064 8 463 0.047 4 460 0.047 12 332 0.034 16 308 0.031 15 282 0.029 10 281 0.029 17 242 0.025 18 235 0.024 14 202 0.021 11 184 0.019 19 177 0.018 13 167 0.017 20 119 0.012 21 75 0.008 22 47 0.005 25 46 0.005 23 39 0.004 24 39 0.004 27 32 0.003 26 28 0.003 28 26 0.003 29 25 0.003 30 20 0.002 31 17 0.002 35 16 0.002 37 16 0.002 47 15 0.002 34 15 0.002 61 13 0.001 32 12 0.001 50 12 0.001 36 11 0.001 44 11 0.001 49 10 0.001 65 9 0.001 56 9 0.001 72 9 0.001 51 9 0.001 121 9 0.001 95 9 0.001 124 9 0.001 42 9 0.001 39 9 0.001

File:Ua vs subgraph.png

Next we isolate user agents from each subgraph who query drastically more (>=10% difference) than other user agents in the same subgraph, and perform at least 100k queries (0.05% of all queries) a month. A list of ~30 such user agents was found. A plot with subgraph distributions of all these user agents was observed to find some large buckets where they tend to query. The plot is shows below, followed by some explicit observations.

Percentages below are percent of all monthly queries. mix n match (UA #17): a lot of taxon queries (Q16521), 23% a lot of human queries (Q5), 4% UA #6: 1% in Business (Q4830453) UA #14: 1% in human (Q5) 0.5% in film (Q11424) UA #23: 1.73% in family name (Q101352) 1.73% in human (Q5) both have exact counts, meaning they could be the same queries that touch both human and family name subgraphs

For reference: 100% percent is 221,067,674 queries 10% percent is 22,106,767 queries 1% percent is 2,210,676 queries 0.1% percent is 221,067 queries 0.05% percent is 110,533 queries 0.01% percent is 22,106 queries

Subgraph connectivity through queries

Subgraph connectivity was explored to some extent using only Wikidata in Wikidata_Subgraph_Analysis. This was based on what items or properties were common between subgraphs and how many direct connections were present between them. A visualization was created to show the strength of this connectivity between subgraphs here: wikidata_graph. This section aims to analyze the connectivity of subgraphs through the queries, i.e, how often are some subgraphs queried together.

• Subgaph Queries: The total number of queries that touch on at least one of the top 341 subgraps is 72% of all queries.
• First we look at how many subgraphs do most queries access. The tables below show the least and most query groups by number of subgraphs accessed.
• 70% of all queries (97% of subgraph queries) touch on 1 or 2 subgraph. 64% of all queries (90% of subgraph queries) touch on only 1 subgraph.

Queries with most subgraphs accessed #of Subgraphs #of Queries 341 25 333 1 315 2 313 3 258 1 181 3 152 1 142 1 133 2 130 2 129 1 128 2 127 4 126 4 125 9

Queries with least subgraphs accessed #of Subgraphs #of Queries %of Queries 1 142507736 64.463 2 12464811 5.638 3 1767253 0.799 4 586173 0.265 5 364445 0.165 6 221485 0.1 7 188012 0.085 8 112922 0.051 9 102524 0.046 10 68871 0.031 11 50341 0.023 12 38102 0.017 13 34075 0.015 14 24003 0.011 15 17935 0.008

• It is hard to view which subgraphs occur together from the data above. So the subgraphs that occured together were broken into pairs and pars of subgraphs that occur together the most were listed.
• There are 57,970 subgraphs pairs that occur togther in queries. Total possible subgrah pair count is (340*341)/2 = 57,970. This shows that every subgraph is connected to every other subgraph through queries! Ofcourse the number of queries vary widely.
• A list of some of the most queried subgraphs is shown below.

Top pairs of subgraphs that are queried together

Subgraph 1		Subgraph 2		Query
Subgraph	Subgraph label	Subgraph	Subgraph label	#of Query	%of Query
Q101352	family name	Q5	human	4935675	2.233
Q4830453	business	Q6881511	enterprise	883757	0.4
Q11424	film	Q5	human	771698	0.349
Q4830453	business	Q891723	public company	735902	0.333
Q3305213	painting	Q4167410	Wikimedia disambiguation page	629633	0.285
Q4164871	position	Q5	human	541257	0.245
Q47461344	written work	Q732577	publication	493402	0.223
Q11424	film	Q14204246	Wikimedia project page	483338	0.219
Q6881511	enterprise	Q891723	public company	480426	0.217
Q4167410	Wikimedia disambiguation page	Q5	human	466217	0.211
Q14204246	Wikimedia project page	Q4167410	Wikimedia disambiguation page	436192	0.197
Q13406463	Wikimedia list article	Q5	human	394815	0.179
Q4830453	business	Q5	human	354945	0.161
Q13442814	scholarly article	Q4167410	Wikimedia disambiguation page	316720	0.143
Q13442814	scholarly article	Q5	human	282237	0.128
Q13406463	Wikimedia list article	Q18340514	events in a specific year or time period	274841	0.124
Q3331189	version, edition, or translation	Q5	human	273761	0.124
Q571	book	Q5	human	259234	0.117
Q16521	taxon	Q5	human	222118	0.1
Q4167410	Wikimedia disambiguation page	Q811979	architectural structure	204572	0.093
Q4167410	Wikimedia disambiguation page	Q838948	work of art	200810	0.091
Q5398426	television series	Q5	human	197997	0.09
Q47461344	written work	Q5	human	194750	0.088
Q43229	organization	Q4830453	business	179640	0.081
Q5	human	Q6881511	enterprise	172486	0.078
Q43229	organization	Q5	human	171567	0.078
Q2225692	fourth-level administrative division in Indonesia	Q532	village	171086	0.077
Q215380	musical group	Q5	human	168318	0.076
Q15632617	fictional human	Q5	human	163992	0.074
Q3305213	painting	Q838948	work of art	161979	0.073

• The distribution of the number of times each subgraph pair in wikidata occurs in queries is shown below. Note that (A,B) pair is the same as (B,A) pair, so there is no duplication in the plots. Since the plot is extremely skewed, three plots with various limits on the number of occurrences are shown. We can see how only a small number of pairs occur a lot together, they can be viewed from the table above. Whereas a huge number of pairs occur a very small number of times.

• Below is a heatmap of the number of queries, where both x and y axis represent subgraph indices (names of subgrahps not shown due to space)
• The diagonals show queries that use only 1 subgraph and are represented as Q5-Q5, or Q42-Q42 for example. Other are represented as Q5-Q42 or Q42-Q5 for example (Symmetrical plot).
• The tons of vertical and horizontal lines indicate there are lots of subgraphs that happen to pair with many other subgraphs. More analysis on this below.