Wikimore

Model card
Model card
This page is an on-wiki machine learning model card.
	A model card is a document about a machine learning model that seeks to answer basic questions about the model.
Model Information Hub
Model creator(s)	Aaron Halfaker (User:EpochFail) and Amir Sarabadani
Model owner(s)	WMF Machine Learning Team (ml@wikimediafoundation.org)
Model interface	Ores homepage
Code	drafttopic Github, ORES training data, and ORES model binaries
Uses PII	No
In production?	Yes
Which projects?	Wikidata
	This model uses item features to predict the likelihood that the item belongs to a set of topics.

Motivation

How can we predict what general topic an item is in? Answering this question is useful for various analyses of Wikidata dynamics. However, it is difficult to group a very diverse range of Wikidata items into coherent, consistent topics manually.

This model, part of the ORES suite of models, analyzes an item to predict its likelihood of belonging to a set of topics. Similar models (though not necessarily with the same performance level or topics, are deployed across about a dozen other projects. There is also a language agnostic article topic model.

This model may be useful for high-level analyses of Wikidata dynamics (pageviews, item quality, edit trends) and filtering items.

Users and uses

Use this model for

high-level analyses of Wikidata dynamics such as pageview, item quality, or edit trends — e.g. How are pageview dynamics different between the physics and biology categories?
filtering to relevant items — e.g. filter items only to those in the music category.

Don't use this model for

definitively establishing what topic an items pertains to
automated editing of items or topics without a human in the loop

Current uses

This model is a part of ORES, and generally accessible via API. It is used for high-level analysis of Wikidata, platform research, and other on-wiki tasks.

Example API call:

https://ores.wikimedia.org/v3/scores/wikidatawiki/1907686315/itemtopic

Ethical considerations, caveats, and recommendations

This model was trained on data that is now several years old (from mid-2020). Underlying data drift may skew model outputs.
This model uses word2vec as a training feature. Word2vec, like other natural language embeddings, encodes the linguistic biases of underlying datasets — along the lines of gender, race, ethnicity, religion etc. Since Wikidata has known biases in its text, this model may encode and at times reproduce those biases.
This model has highly variable performance across different topics — consult the test statistics below to get a sense of inter-topic performance.

Model

Performance

Test data confusion matrix:

Test data confusion matrix

Label	n	True positive	False positive	False negative	True Negative
Culture.Biography.Biography*	16670	15762	908	464	46810
Culture.Biography.Women	4110	3125	985	679	59155
Culture.Food and drink	1318	613	705	126	62500
Culture.Internet culture	2966	1948	1018	140	60838
Culture.Linguistics	1466	934	532	56	62422
Culture.Literature	5367	3996	1371	404	58173
Culture.Media.Books	1974	1560	414	136	61834
Culture.Media.Entertainment	1733	857	876	162	62049
Culture.Media.Films	2295	1896	399	122	61527
Culture.Media.Media*	14383	11572	2811	1135	48426
Culture.Media.Music	2583	2027	556	247	61114
Culture.Media.Radio	1156	857	299	44	62744
Culture.Media.Software	1750	685	1065	307	61887
Culture.Media.Television	2230	1510	720	176	61538
Culture.Media.Video games	2147	1758	389	54	61743
Culture.Performing arts	1334	741	593	116	62494
Culture.Philosophy and religion	2702	1074	1628	285	60957
Culture.Sports	5925	5186	739	249	57770
Culture.Visual arts.Architecture	2648	1867	781	230	61066
Culture.Visual arts.Comics and Anime	1508	1007	501	140	62296
Culture.Visual arts.Fashion	1199	669	530	98	62647
Culture.Visual arts.Visual arts*	6070	4131	1939	554	57320
Geography.Geographical	3464	2226	1238	359	60121
Geography.Regions.Africa.Africa*	6449	4664	1785	414	57081
Geography.Regions.Africa.Central Africa	1145	697	448	83	62716
Geography.Regions.Africa.Eastern Africa	1114	704	410	56	62774
Geography.Regions.Africa.Northern Africa	1280	774	506	108	62556
Geography.Regions.Africa.Southern Africa	1244	859	385	81	62619
Geography.Regions.Africa.Western Africa	1142	774	368	75	62727
Geography.Regions.Americas.Central America	1331	707	624	87	62526
Geography.Regions.Americas.North America	7625	5064	2561	1169	55150
Geography.Regions.Americas.South America	1532	1082	450	142	62270
Geography.Regions.Asia.Asia*	11647	8432	3215	835	51462
Geography.Regions.Asia.Central Asia	1086	671	415	70	62788
Geography.Regions.Asia.East Asia	2717	1727	990	241	60986
Geography.Regions.Asia.North Asia	2076	1336	740	163	61705
Geography.Regions.Asia.South Asia	2366	1612	754	135	61443
Geography.Regions.Asia.Southeast Asia	1721	1059	662	119	62104
Geography.Regions.Asia.West Asia	2160	1473	687	129	61655
Geography.Regions.Europe.Eastern Europe	3533	2472	1061	234	60177
Geography.Regions.Europe.Europe*	12939	9372	3567	1810	49195
Geography.Regions.Europe.Northern Europe	4221	2571	1650	601	59122
Geography.Regions.Europe.Southern Europe	2438	1565	873	268	61238
Geography.Regions.Europe.Western Europe	3076	1934	1142	417	60451
Geography.Regions.Oceania	2638	1859	779	138	61168
History and Society.Business and economics	3502	1544	1958	569	59873
History and Society.Education	2243	1113	1130	255	61446
History and Society.History	3172	1154	2018	360	60412
History and Society.Military and warfare	3238	1677	1561	296	60410
History and Society.Politics and government	4590	2406	2184	329	59025
History and Society.Society	2971	897	2074	166	60807
History and Society.Transportation	3629	2615	1014	169	60146
STEM.Biology	2916	2237	679	91	60937
STEM.Chemistry	1270	690	580	138	62536
STEM.Computing	1968	828	1140	332	61644
STEM.Earth and environment	1627	918	709	114	62203
STEM.Engineering	2195	1284	911	141	61608
STEM.Libraries & Information	1174	605	569	87	62683
STEM.Mathematics	1137	307	830	107	62700
STEM.Medicine & Health	1726	769	957	180	62038
STEM.Physics	1219	448	771	107	62618
STEM.STEM*	16449	12609	3840	2766	44729
STEM.Space	1365	932	433	47	62532
STEM.Technology	3648	1396	2252	424	59872

Test data sample rates:

Test data sample rates

Label	Sample	Population
Culture.Biography.Biography*	0.261	0.12
Culture.Biography.Women	0.064	0.015
Culture.Food and drink	0.021	0.003
Culture.Internet culture	0.046	0.004
Culture.Linguistics	0.023	0.008
Culture.Literature	0.084	0.015
Culture.Media.Books	0.031	0.004
Culture.Media.Entertainment	0.027	0.004
Culture.Media.Films	0.036	0.012
Culture.Media.Media*	0.225	0.055
Culture.Media.Music	0.04	0.021
Culture.Media.Radio	0.018	0.002
Culture.Media.Software	0.027	0.001
Culture.Media.Television	0.035	0.009
Culture.Media.Video games	0.034	0.003
Culture.Performing arts	0.021	0.003
Culture.Philosophy and religion	0.042	0.01
Culture.Sports	0.093	0.06
Culture.Visual arts.Architecture	0.041	0.011
Culture.Visual arts.Comics and Anime	0.024	0.002
Culture.Visual arts.Fashion	0.019	0.001
Culture.Visual arts.Visual arts*	0.095	0.018
Geography.Geographical	0.054	0.021
Geography.Regions.Africa.Africa*	0.101	0.008
Geography.Regions.Africa.Central Africa	0.018	0.001
Geography.Regions.Africa.Eastern Africa	0.017	0.001
Geography.Regions.Africa.Northern Africa	0.02	0.001
Geography.Regions.Africa.Southern Africa	0.019	0.001
Geography.Regions.Africa.Western Africa	0.018	0.001
Geography.Regions.Americas.Central America	0.021	0.003
Geography.Regions.Americas.North America	0.119	0.063
Geography.Regions.Americas.South America	0.024	0.007
Geography.Regions.Asia.Asia*	0.182	0.052
Geography.Regions.Asia.Central Asia	0.017	0.001
Geography.Regions.Asia.East Asia	0.042	0.012
Geography.Regions.Asia.North Asia	0.032	0.006
Geography.Regions.Asia.South Asia	0.037	0.016
Geography.Regions.Asia.Southeast Asia	0.027	0.006
Geography.Regions.Asia.West Asia	0.034	0.012
Geography.Regions.Europe.Eastern Europe	0.055	0.018
Geography.Regions.Europe.Europe*	0.202	0.081
Geography.Regions.Europe.Northern Europe	0.066	0.029
Geography.Regions.Europe.Southern Europe	0.038	0.014
Geography.Regions.Europe.Western Europe	0.048	0.02
Geography.Regions.Oceania	0.041	0.016
History and Society.Business and economics	0.055	0.01
History and Society.Education	0.035	0.008
History and Society.History	0.05	0.011
History and Society.Military and warfare	0.051	0.015
History and Society.Politics and government	0.072	0.028
History and Society.Society	0.046	0.008
History and Society.Transportation	0.057	0.016
STEM.Biology	0.046	0.034
STEM.Chemistry	0.02	0.002
STEM.Computing	0.031	0.003
STEM.Earth and environment	0.025	0.005
STEM.Engineering	0.034	0.006
STEM.Libraries & Information	0.018	0.001
STEM.Mathematics	0.018	0
STEM.Medicine & Health	0.027	0.006
STEM.Physics	0.019	0.001
STEM.STEM*	0.257	0.065
STEM.Space	0.021	0.004
STEM.Technology	0.057	0.005

Test data performance:

Test data performance

Label	Match rate	Filter rate	Recall	Precision	f1	Accuracy	ROC AUC	PR AUC
Culture.Biography.Biography*	0.122	0.878	0.946	0.929	0.937	0.985	0.982	0.952
Culture.Biography.Women	0.023	0.977	0.76	0.504	0.606	0.985	0.975	0.589
Culture.Food and drink	0.003	0.997	0.465	0.371	0.413	0.997	0.937	0.352
Culture.Internet culture	0.005	0.995	0.657	0.517	0.578	0.996	0.96	0.549
Culture.Linguistics	0.006	0.994	0.637	0.852	0.729	0.996	0.954	0.656
Culture.Literature	0.018	0.982	0.745	0.619	0.676	0.989	0.965	0.726
Culture.Media.Books	0.006	0.994	0.79	0.609	0.688	0.997	0.971	0.659
Culture.Media.Entertainment	0.005	0.995	0.495	0.429	0.459	0.995	0.947	0.433
Culture.Media.Films	0.011	0.989	0.826	0.829	0.828	0.996	0.974	0.813
Culture.Media.Media*	0.066	0.934	0.805	0.67	0.731	0.968	0.966	0.813
Culture.Media.Music	0.02	0.98	0.785	0.807	0.795	0.992	0.974	0.818
Culture.Media.Radio	0.002	0.998	0.741	0.711	0.726	0.999	0.962	0.741
Culture.Media.Software	0.005	0.995	0.391	0.094	0.152	0.994	0.946	0.094
Culture.Media.Television	0.009	0.991	0.677	0.68	0.678	0.994	0.964	0.664
Culture.Media.Video games	0.003	0.997	0.819	0.732	0.773	0.999	0.977	0.801
Culture.Performing arts	0.004	0.996	0.555	0.478	0.514	0.997	0.947	0.414
Culture.Philosophy and religion	0.009	0.991	0.397	0.472	0.431	0.989	0.91	0.339
Culture.Sports	0.057	0.943	0.875	0.929	0.901	0.988	0.976	0.933
Culture.Visual arts.Architecture	0.011	0.989	0.705	0.672	0.688	0.993	0.969	0.673
Culture.Visual arts.Comics and Anime	0.004	0.996	0.668	0.416	0.513	0.997	0.966	0.558
Culture.Visual arts.Fashion	0.002	0.998	0.558	0.242	0.338	0.998	0.95	0.215
Culture.Visual arts.Visual arts*	0.022	0.978	0.681	0.566	0.618	0.985	0.952	0.666
Geography.Geographical	0.019	0.981	0.643	0.7	0.67	0.987	0.956	0.698
Geography.Regions.Africa.Africa*	0.013	0.987	0.723	0.462	0.564	0.991	0.96	0.639
Geography.Regions.Africa.Central Africa	0.002	0.998	0.609	0.244	0.349	0.998	0.954	0.321
Geography.Regions.Africa.Eastern Africa	0.001	0.999	0.632	0.262	0.371	0.999	0.957	0.253
Geography.Regions.Africa.Northern Africa	0.003	0.997	0.605	0.321	0.42	0.998	0.945	0.343
Geography.Regions.Africa.Southern Africa	0.002	0.998	0.691	0.411	0.515	0.998	0.959	0.514
Geography.Regions.Africa.Western Africa	0.002	0.998	0.678	0.297	0.413	0.999	0.96	0.277
Geography.Regions.Americas.Central America	0.003	0.997	0.531	0.569	0.55	0.997	0.932	0.494
Geography.Regions.Americas.North America	0.061	0.939	0.664	0.682	0.673	0.959	0.949	0.726
Geography.Regions.Americas.South America	0.007	0.993	0.706	0.681	0.693	0.996	0.963	0.691
Geography.Regions.Asia.Asia*	0.053	0.947	0.724	0.715	0.719	0.97	0.949	0.756
Geography.Regions.Asia.Central Asia	0.002	0.998	0.618	0.306	0.41	0.999	0.952	0.462
Geography.Regions.Asia.East Asia	0.012	0.988	0.636	0.665	0.65	0.992	0.95	0.625
Geography.Regions.Asia.North Asia	0.006	0.994	0.644	0.579	0.609	0.995	0.946	0.55
Geography.Regions.Asia.South Asia	0.013	0.987	0.681	0.839	0.752	0.993	0.953	0.708
Geography.Regions.Asia.Southeast Asia	0.006	0.994	0.615	0.668	0.641	0.996	0.942	0.557
Geography.Regions.Asia.West Asia	0.01	0.99	0.682	0.794	0.734	0.994	0.953	0.662
Geography.Regions.Europe.Eastern Europe	0.017	0.983	0.7	0.771	0.733	0.991	0.951	0.71
Geography.Regions.Europe.Europe*	0.091	0.909	0.724	0.642	0.681	0.945	0.943	0.744
Geography.Regions.Europe.Northern Europe	0.027	0.973	0.609	0.643	0.626	0.979	0.948	0.644
Geography.Regions.Europe.Southern Europe	0.013	0.987	0.642	0.673	0.657	0.991	0.949	0.618
Geography.Regions.Europe.Western Europe	0.02	0.98	0.629	0.657	0.643	0.986	0.95	0.63
Geography.Regions.Oceania	0.014	0.986	0.705	0.839	0.766	0.993	0.96	0.75
History and Society.Business and economics	0.014	0.986	0.441	0.315	0.367	0.985	0.936	0.248
History and Society.Education	0.008	0.992	0.496	0.489	0.493	0.992	0.944	0.4
History and Society.History	0.01	0.99	0.364	0.403	0.382	0.987	0.916	0.315
History and Society.Military and warfare	0.013	0.987	0.518	0.622	0.565	0.988	0.933	0.521
History and Society.Politics and government	0.02	0.98	0.524	0.731	0.611	0.981	0.925	0.603
History and Society.Society	0.005	0.995	0.302	0.48	0.371	0.992	0.871	0.318
History and Society.Transportation	0.014	0.986	0.721	0.809	0.762	0.993	0.963	0.712
STEM.Biology	0.028	0.972	0.767	0.948	0.848	0.991	0.962	0.816
STEM.Chemistry	0.003	0.997	0.543	0.294	0.382	0.997	0.958	0.27
STEM.Computing	0.007	0.993	0.421	0.182	0.254	0.993	0.951	0.149
STEM.Earth and environment	0.004	0.996	0.564	0.594	0.579	0.996	0.947	0.522
STEM.Engineering	0.006	0.994	0.585	0.596	0.591	0.995	0.947	0.51
STEM.Libraries & Information	0.002	0.998	0.515	0.203	0.291	0.998	0.948	0.238
STEM.Mathematics	0.002	0.998	0.27	0.068	0.109	0.998	0.942	0.125
STEM.Medicine & Health	0.006	0.994	0.446	0.499	0.471	0.994	0.933	0.398
STEM.Physics	0.002	0.998	0.368	0.168	0.23	0.998	0.945	0.126
STEM.STEM*	0.104	0.896	0.767	0.477	0.588	0.93	0.955	0.768
STEM.Space	0.004	0.996	0.683	0.795	0.735	0.998	0.963	0.686
STEM.Technology	0.009	0.991	0.383	0.219	0.279	0.99	0.928	0.213

Implementation

Model architecture

{
    "type": "GradientBoosting",
    "params": {
        "scale": false,
        "center": false,
        "labels": [
            "Culture.Biography.Biography*",
            "Culture.Biography.Women",
            "Culture.Food and drink",
            "Culture.Internet culture",
            "Culture.Linguistics",
            "Culture.Literature",
            "Culture.Media.Books",
            "Culture.Media.Entertainment",
            "Culture.Media.Films",
            "Culture.Media.Media*",
            "Culture.Media.Music",
            "Culture.Media.Radio",
            "Culture.Media.Software",
            "Culture.Media.Television",
            "Culture.Media.Video games",
            "Culture.Performing arts",
            "Culture.Philosophy and religion",
            "Culture.Sports",
            "Culture.Visual arts.Architecture",
            "Culture.Visual arts.Comics and Anime",
            "Culture.Visual arts.Fashion",
            "Culture.Visual arts.Visual arts*",
            "Geography.Geographical",
            "Geography.Regions.Africa.Africa*",
            "Geography.Regions.Africa.Central Africa",
            "Geography.Regions.Africa.Eastern Africa",
            "Geography.Regions.Africa.Northern Africa",
            "Geography.Regions.Africa.Southern Africa",
            "Geography.Regions.Africa.Western Africa",
            "Geography.Regions.Americas.Central America",
            "Geography.Regions.Americas.North America",
            "Geography.Regions.Americas.South America",
            "Geography.Regions.Asia.Asia*",
            "Geography.Regions.Asia.Central Asia",
            "Geography.Regions.Asia.East Asia",
            "Geography.Regions.Asia.North Asia",
            "Geography.Regions.Asia.South Asia",
            "Geography.Regions.Asia.Southeast Asia",
            "Geography.Regions.Asia.West Asia",
            "Geography.Regions.Europe.Eastern Europe",
            "Geography.Regions.Europe.Europe*",
            "Geography.Regions.Europe.Northern Europe",
            "Geography.Regions.Europe.Southern Europe",
            "Geography.Regions.Europe.Western Europe",
            "Geography.Regions.Oceania",
            "History and Society.Business and economics",
            "History and Society.Education",
            "History and Society.History",
            "History and Society.Military and warfare",
            "History and Society.Politics and government",
            "History and Society.Society",
            "History and Society.Transportation",
            "STEM.Biology",
            "STEM.Chemistry",
            "STEM.Computing",
            "STEM.Earth and environment",
            "STEM.Engineering",
            "STEM.Libraries & Information",
            "STEM.Mathematics",
            "STEM.Medicine & Health",
            "STEM.Physics",
            "STEM.STEM*",
            "STEM.Space",
            "STEM.Technology"
        ],
        "multilabel": true,
        "population_rates": null,
        "ccp_alpha": 0.0,
        "criterion": "friedman_mse",
        "init": null,
        "learning_rate": 0.1,
        "loss": "deviance",
        "max_depth": 5,
        "max_features": "log2",
        "max_leaf_nodes": null,
        "min_impurity_decrease": 0.0,
        "min_impurity_split": null,
        "min_samples_leaf": 1,
        "min_samples_split": 2,
        "min_weight_fraction_leaf": 0.0,
        "n_estimators": 150,
        "n_iter_no_change": null,
        "presort": "deprecated",
        "random_state": null,
        "subsample": 1.0,
        "tol": 0.0001,
        "validation_fraction": 0.1,
        "verbose": 0,
        "warm_start": false,
        "label_weights": {}
    }
}

Output schema

{
    "title": "Scikit learn-based classifier score with probability",
    "type": "object",
    "properties": {
        "prediction": {
            "description": "The most likely labels predicted by the estimator",
            "type": "array",
            "items": {
                "type": "string"
            }
        },
        "probability": {
            "description": "A mapping of probabilities onto each of the potential output labels",
            "type": "object",
            "properties": {
                "Culture.Biography.Biography*": {
                    "type": "number"
                },
                "Culture.Biography.Women": {
                    "type": "number"
                },
                "Culture.Food and drink": {
                    "type": "number"
                },
                "Culture.Internet culture": {
                    "type": "number"
                },
                "Culture.Linguistics": {
                    "type": "number"
                },
                "Culture.Literature": {
                    "type": "number"
                },
                "Culture.Media.Books": {
                    "type": "number"
                },
                "Culture.Media.Entertainment": {
                    "type": "number"
                },
                "Culture.Media.Films": {
                    "type": "number"
                },
                "Culture.Media.Media*": {
                    "type": "number"
                },
                "Culture.Media.Music": {
                    "type": "number"
                },
                "Culture.Media.Radio": {
                    "type": "number"
                },
                "Culture.Media.Software": {
                    "type": "number"
                },
                "Culture.Media.Television": {
                    "type": "number"
                },
                "Culture.Media.Video games": {
                    "type": "number"
                },
                "Culture.Performing arts": {
                    "type": "number"
                },
                "Culture.Philosophy and religion": {
                    "type": "number"
                },
                "Culture.Sports": {
                    "type": "number"
                },
                "Culture.Visual arts.Architecture": {
                    "type": "number"
                },
                "Culture.Visual arts.Comics and Anime": {
                    "type": "number"
                },
                "Culture.Visual arts.Fashion": {
                    "type": "number"
                },
                "Culture.Visual arts.Visual arts*": {
                    "type": "number"
                },
                "Geography.Geographical": {
                    "type": "number"
                },
                "Geography.Regions.Africa.Africa*": {
                    "type": "number"
                },
                "Geography.Regions.Africa.Central Africa": {
                    "type": "number"
                },
                "Geography.Regions.Africa.Eastern Africa": {
                    "type": "number"
                },
                "Geography.Regions.Africa.Northern Africa": {
                    "type": "number"
                },
                "Geography.Regions.Africa.Southern Africa": {
                    "type": "number"
                },
                "Geography.Regions.Africa.Western Africa": {
                    "type": "number"
                },
                "Geography.Regions.Americas.Central America": {
                    "type": "number"
                },
                "Geography.Regions.Americas.North America": {
                    "type": "number"
                },
                "Geography.Regions.Americas.South America": {
                    "type": "number"
                },
                "Geography.Regions.Asia.Asia*": {
                    "type": "number"
                },
                "Geography.Regions.Asia.Central Asia": {
                    "type": "number"
                },
                "Geography.Regions.Asia.East Asia": {
                    "type": "number"
                },
                "Geography.Regions.Asia.North Asia": {
                    "type": "number"
                },
                "Geography.Regions.Asia.South Asia": {
                    "type": "number"
                },
                "Geography.Regions.Asia.Southeast Asia": {
                    "type": "number"
                },
                "Geography.Regions.Asia.West Asia": {
                    "type": "number"
                },
                "Geography.Regions.Europe.Eastern Europe": {
                    "type": "number"
                },
                "Geography.Regions.Europe.Europe*": {
                    "type": "number"
                },
                "Geography.Regions.Europe.Northern Europe": {
                    "type": "number"
                },
                "Geography.Regions.Europe.Southern Europe": {
                    "type": "number"
                },
                "Geography.Regions.Europe.Western Europe": {
                    "type": "number"
                },
                "Geography.Regions.Oceania": {
                    "type": "number"
                },
                "History and Society.Business and economics": {
                    "type": "number"
                },
                "History and Society.Education": {
                    "type": "number"
                },
                "History and Society.History": {
                    "type": "number"
                },
                "History and Society.Military and warfare": {
                    "type": "number"
                },
                "History and Society.Politics and government": {
                    "type": "number"
                },
                "History and Society.Society": {
                    "type": "number"
                },
                "History and Society.Transportation": {
                    "type": "number"
                },
                "STEM.Biology": {
                    "type": "number"
                },
                "STEM.Chemistry": {
                    "type": "number"
                },
                "STEM.Computing": {
                    "type": "number"
                },
                "STEM.Earth and environment": {
                    "type": "number"
                },
                "STEM.Engineering": {
                    "type": "number"
                },
                "STEM.Libraries & Information": {
                    "type": "number"
                },
                "STEM.Mathematics": {
                    "type": "number"
                },
                "STEM.Medicine & Health": {
                    "type": "number"
                },
                "STEM.Physics": {
                    "type": "number"
                },
                "STEM.STEM*": {
                    "type": "number"
                },
                "STEM.Space": {
                    "type": "number"
                },
                "STEM.Technology": {
                    "type": "number"
                }
            }
        }
    }
}

Example input and output

Input:

https://ores.wikimedia.org/v3/scores/wikidatawiki/1907686315/itemtopic

Output:

Example output

{
    "wikidatawiki": {
        "models": {
            "itemtopic": {
                "version": "1.2.0"
            }
        },
        "scores": {
            "1907686315": {
                "itemtopic": {
                    "score": {
                        "prediction": [
                            "STEM.STEM*"
                        ],
                        "probability": {
                            "Culture.Biography.Biography*": 0.009059893345632097,
                            "Culture.Biography.Women": 0.0006924491258526178,
                            "Culture.Food and drink": 0.0006399242658997215,
                            "Culture.Internet culture": 0.0009384780459913412,
                            "Culture.Linguistics": 0.0018606277391225432,
                            "Culture.Literature": 0.003990388751181737,
                            "Culture.Media.Books": 0.0006214752106656115,
                            "Culture.Media.Entertainment": 0.001104834881085509,
                            "Culture.Media.Films": 0.0011465477594696284,
                            "Culture.Media.Media*": 0.009497882960118977,
                            "Culture.Media.Music": 0.0005314326820035878,
                            "Culture.Media.Radio": 0.0001418663128807519,
                            "Culture.Media.Software": 0.0006122966374525156,
                            "Culture.Media.Television": 0.0011153877562536376,
                            "Culture.Media.Video games": 0.0006372239889671269,
                            "Culture.Performing arts": 0.0006531356388159476,
                            "Culture.Philosophy and religion": 0.01399521934257544,
                            "Culture.Sports": 0.0018462250677368348,
                            "Culture.Visual arts.Architecture": 0.0016560396166840437,
                            "Culture.Visual arts.Comics and Anime": 0.0005305955236163667,
                            "Culture.Visual arts.Fashion": 0.000537788411976724,
                            "Culture.Visual arts.Visual arts*": 0.009907875401930734,
                            "Geography.Geographical": 0.01571363482516823,
                            "Geography.Regions.Africa.Africa*": 0.020280349224975614,
                            "Geography.Regions.Africa.Central Africa": 0.0007006250310735848,
                            "Geography.Regions.Africa.Eastern Africa": 0.000981468869640802,
                            "Geography.Regions.Africa.Northern Africa": 0.015712323087656205,
                            "Geography.Regions.Africa.Southern Africa": 0.001221937118377821,
                            "Geography.Regions.Africa.Western Africa": 0.0008305320623083369,
                            "Geography.Regions.Americas.Central America": 0.001306842712476455,
                            "Geography.Regions.Americas.North America": 0.030570993625411366,
                            "Geography.Regions.Americas.South America": 0.009381192562807516,
                            "Geography.Regions.Asia.Asia*": 0.08763779333893186,
                            "Geography.Regions.Asia.Central Asia": 0.0021630529281042718,
                            "Geography.Regions.Asia.East Asia": 0.01090968773383821,
                            "Geography.Regions.Asia.North Asia": 0.029951228290233667,
                            "Geography.Regions.Asia.South Asia": 0.005977584426712786,
                            "Geography.Regions.Asia.Southeast Asia": 0.0028688045552628266,
                            "Geography.Regions.Asia.West Asia": 0.0009526856502617891,
                            "Geography.Regions.Europe.Eastern Europe": 0.029972291587851183,
                            "Geography.Regions.Europe.Europe*": 0.13296776378542635,
                            "Geography.Regions.Europe.Northern Europe": 0.016907973275604154,
                            "Geography.Regions.Europe.Southern Europe": 0.005813048163270592,
                            "Geography.Regions.Europe.Western Europe": 0.005037055635498127,
                            "Geography.Regions.Oceania": 0.007780720915153282,
                            "History and Society.Business and economics": 0.005890874106250135,
                            "History and Society.Education": 0.0017680572320172617,
                            "History and Society.History": 0.01973006391755843,
                            "History and Society.Military and warfare": 0.006573635883462243,
                            "History and Society.Politics and government": 0.007573132449112524,
                            "History and Society.Society": 0.04381007914549254,
                            "History and Society.Transportation": 0.002797769913886188,
                            "STEM.Biology": 0.005780672890531569,
                            "STEM.Chemistry": 0.0022570835539507676,
                            "STEM.Computing": 0.0018290751421398967,
                            "STEM.Earth and environment": 0.0795914195853073,
                            "STEM.Engineering": 0.004058097854564882,
                            "STEM.Libraries & Information": 0.0010339015208737487,
                            "STEM.Mathematics": 0.0017040157655581244,
                            "STEM.Medicine & Health": 0.005650365932513206,
                            "STEM.Physics": 0.020150498627265184,
                            "STEM.STEM*": 0.8790296717258461,
                            "STEM.Space": 0.11458869168317454,
                            "STEM.Technology": 0.012381701761546463
                        }
                    }
                }
            }
        }
    }
}

Data

Data pipeline

The data to train was fetched from a set of revision IDs. Then various pieces of information about the revision were extracted using automated processes, and the revision text was fed into word2vec to get an item embedding. Finally, labels are derived from the mid-level WikiProject categories that the item is associated with.

Training data

The training dataset for this model is available here. Training data was automatically and randomly separated from test data during training using the drafttopic git repository (which trains both drafttopic and articletopic models).

Test data

Test data was automatically and randomly split off from train data using the drafttopic git repository (which trains both drafttopic and articletopic models). The model then makes a prediction on that data, which is compared to the underlying ground truth to calculate performance statistics.

Licenses

Code: MIT license
Model: MIT license

Citation

Cite this model card as:

@misc{
  Triedman_Bazira_2023_Wikidata_item_topic,
  title={ Wikidata item topic model card },
  author={ Triedman, Harold and Bazira, Kevin },
  year={ 2023 },
  url={ https://meta.wikimedia.org/wiki/Machine_learning_models/Production/Wikidata_item_topic }
}

Category:Machine learning models Category:Wikidata