Uncovering Biases Through Faceted Image Retrieval: Wikidata as a Data Provider for Art-Historical Research

Stefanie Schneider; Matthias Springstein; Julian Stalter; Daniel Ritter; Ralph Ewerth; Eric Müller-Budack

doi:10.5334/johd.436

Figures & Tables

Table 1

Results of the data acquisition process, showing the properties retrieved from Wikidata and their absolute frequencies.

IDENTIFIER	NAME	DESCRIPTION	FREQUENCY
`P18`	image	image of relevant illustration of the subject […]	36 179
`P1476`	title	published name of a work, such as a newspaper article […]	29 077
`P170`	creator	maker of this creative work or other object […]	33 616
`P571`	inception	time when an entity begins to exist […]	35 588
`P31`	instance of	type to which this subject corresponds/belongs […]	37 393
`P2048`	height	vertical length of an entity	35 276
`P2049`	width	width of an object	35 268
`P186`	made from material	material the subject or the object is made of or derived from […]	66 651
`P135`	movement	literary, artistic, scientific or philosophical movement or scene […]	2668
`P136`	genre	creative work’s genre or an artist’s field of work […]	33 741
`P180`	depicts	entity visually depicted in an image, literarily described in a work […]	62 848
`P921`	main subject	primary topic of a work or act of communication	13 253
`P195`	collection	art, museum, archival, […] of which the subject is part […]	56 189
`P276`	location	location of the object, structure or event […]	41 557
`P17`	country	sovereign state that this item is in […]	5712
`P495`	country of origin	country of origin of this item	1210

Front-end design of iART. **(a)** On the left side of the GUI, users can access facets organized in expandable panels, while the right side displays the search results in an image grid. **(b)** For nuanced control of the search results, a modal dialog provides advanced configuration options.

Table 2

Mean results for concept extraction, tuple extraction, and relationship extraction in terms of precision (P), recall (R), and F₁-score across different LLMs and prompting strategies. The averages are calculated over three iterations. The best-performing approach is indicated in bold.

LLM	PROMPT	CONCEPT EXTRACTION			TUPLE EXTRACTION			RELATION EXTRACTION
LLM	PROMPT	P	R	F₁	P	R	F₁	P	R	F₁
Micro
Qwen3	`CoT`	0.808	0.859	0.833	0.721	0.725	0.723	0.468	0.471	0.469
	`Role-CoT`	0.560	0.706	0.624	0.384	0.503	0.435	0.206	0.271	0.234
	`ReAct`	0.698	0.671	0.684	0.539	0.508	0.523	0.357	0.343	0.350
Gemma 3	`CoT`	0.776	0.857	0.815	0.639	0.665	0.652	0.422	0.448	0.435
	`Role-CoT`	0.571	0.782	0.660	0.379	0.497	0.430	0.150	0.237	0.184
	`ReAct`	0.757	0.816	0.785	0.609	0.620	0.614	0.355	0.365	0.360
LLaMA 3.1	`CoT`	0.186	0.074	0.106	0.215	0.109	0.145	0.167	0.085	0.112
	`Role-CoT`	0.503	0.362	0.421	0.444	0.294	0.354	0.260	0.179	0.212
	`ReAct`	0.393	0.205	0.269	0.391	0.217	0.279	0.265	0.147	0.189
Macro
Qwen3	`CoT`	0.789	0.857	0.802	0.710	0.687	0.684	0.524	0.518	0.511
	`Role-CoT`	0.651	0.782	0.662	0.519	0.537	0.490	0.294	0.302	0.285
	`ReAct`	0.608	0.621	0.590	0.581	0.547	0.543	0.386	0.390	0.375
Gemma 3	`CoT`	0.781	0.867	0.795	0.614	0.625	0.591	0.404	0.440	0.404
	`Role-CoT`	0.604	0.788	0.644	0.467	0.526	0.457	0.173	0.264	0.189
	`ReAct`	0.777	0.830	0.774	0.628	0.575	0.568	0.328	0.321	0.309
LLaMA 3.1	`CoT`	0.103	0.112	0.095	0.227	0.207	0.212	0.176	0.176	0.176
	`Role-CoT`	0.325	0.357	0.326	0.382	0.327	0.340	0.223	0.224	0.218
	`ReAct`	0.192	0.195	0.179	0.329	0.288	0.300	0.260	0.239	0.246

Table 3

Results for relation extraction across different LLMs and prompting strategies. For each model-prompt configuration, we report the number of concepts |ℂ|, the total number of predicted tuples |ℙ|, the total number of triplets ( $| \hat{T} |$ ), the number of Iconclass notations with valid triplets (|ℕ|), the number without valid triplets ( $| ℕ_{\emptyset} |$ ), and the mean number of triplets per Iconclass notations, i.e., $| \hat{T} | / | ℕ |$ .

LLM	PROMPT	\|ℂ\|	\|ℙ\|	$\| \hat{T} \|$	\|ℕ\|	$\| ℕ_{\emptyset} \|$	$\| \hat{T} \| / \| ℕ \|$
Qwen3	`CoT`	241	162	164	71	14	2.3
	`Role-CoT`	302	223	226	78	7	2.9
	`ReAct`	204	140	143	58	27	2.5
Gemma 3	`CoT`	256	182	186	83	2	2.2
	`Role-CoT`	325	231	279	84	1	3.3
	`ReAct`	247	176	178	81	4	2.2
LLaMA 3.1	`CoT`	25	13	13	8	77	1.6
	`Role-CoT`	131	67	72	35	50	2.1
	`ReAct`	57	24	24	11	74	2.2

Table 4

Qualitative comparison of ground-truth and predicted triplets (represented in the form subject-predicate-object) for selected Iconclass notations N, with associated descriptions D_N shown in parentheses.

N	GROUND TRUTH			PREDICTION
N	SUBJECT	PREDICATE	OBJECT	SUBJECT	PREDICATE	OBJECT
`93G` (House of Sleep: a gloomy cave through which runs the river Lethe; possibly with two gates, […])
	River Lethe	runs through	House of Sleep	House of Sleep	runs_through	river Lethe
	ivory gate	issues	deceptive dreams	ivory gate	issues	deceptive dreams
	horn gate	issues	true dreams	horn gate	issues	true dreams
	House of Sleep	contains	two gates	House of Sleep	has_gate	ivory gate
				House of Sleep	has_gate	horn gate
`11G311` (Michael resting beside the dragon’s corpse)
	Michael	rests beside	dragon’s corpse	Michael	resting_beside	dragon’s corpse
	Dragon	represented as	corpse
`11U4` (Mary and John the Baptist together with (e.g. kneeling before) the judging Christ, ’Deesis’ Last Judgement)
	Mary	kneels before	Christ	Mary	kneels_before	judging Christ
	John the Baptist	kneels before	Christ	John the Baptist	kneels_before	judging Christ
	Mary	together with	John the Baptist
	Christ	represented as	Judge
`29B` (Plants behaving as human beings or animals)
	plants	behave like	human beings	plants	behaving_as	human beings
	plants	behave like	animals	plants	behaving_as	animals
`94P68` ((story of) Perseus – death)
				Perseus	kills	Medusa