Non-Parametric Class Completeness Estimators for Collaborative Knowledge Graphs -- The Case of Wikidata

Cristina Sarasua; Djellel Difallah; Gianluca Demartini; Michael Luggen; Philippe Cudr\'e-Mauroux

REVIEW 5 major objections 5 minor 24 references

Non-Parametric Class Completeness Estimators for Collaborative Knowledge Graphs -- The Case of Wikidata

T0 review · 5 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper shows that a knowledge graph's edit history is enough to estimate class completeness.

desk verdict A useful and reproducible application of species richness estimators to Wikidata edit logs, with independent ground truth on eight classes; the main soft spot is the unvalidated sampling model, not the estimator math. read the letter →

arxiv 1909.01109 v1 pith:M3XJKGIP submitted 2019-09-03 cs.DB cs.SI

Michael Luggen , Djellel Difallah , Cristina Sarasua , Gianluca Demartini , Philippe Cudré-Mauroux This is my paper · ORCID

classification cs.DBcs.SI

keywords knowledgegraphcompletenessclasscardinalityestimationWikidataspeciesrichnesscapture-recapturenon-parametricestimatorsedithistorycollaborativegraphs

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that a knowledge graph's edit history can be used to estimate how many instances a class ought to have, and therefore how complete the class is. It treats every edit that mentions an entity of a class as a capture event, borrows species-richness estimators from ecology, and applies them to monthly samples of Wikidata edits. The authors argue that the resulting estimates of true class size are reliable enough to distinguish classes that are complete from classes that still have missing instances. If true, this gives Wikidata editors and consumers a practical way to find knowledge gaps without manually checking candidate entities.

What carries the argument

The machinery is the frequency-of-frequencies vector computed from mentions: $f_1$ counts instances mentioned exactly once, $f_2$ counts instances mentioned exactly twice, and so on, with $f_0$ the unobserved instances that the estimators try to recover. Four non-parametric estimators use this vector: Jack1 and Jack2 leave one sample period out; N1-UNIF uses the sample-coverage estimate $\hat{S} = 1 - f_1/n$; SOR caps $f_1$ at two standard deviations above the mean to reduce singleton bursts; and the coverage-based estimator adds a coefficient-of-variation correction. The convergence metric $\rho$ averages the relative distance between the estimate and the observed distinct count over the last $w$ sample periods, providing a completeness signal that does not require ground truth.

What would settle it

Take a class with a known true size and a known history of bulk imports, then recompute Jack1 and SOR estimates from the edit log: if the estimators overshoot the true size whenever a bot inserts many instances at once, and the $\rho$ metric nonetheless drops below 0.001, then the stationarity and independence assumptions do not hold.

Watch

Extended reading notes

Core claim

The paper's central claim is that the true size $N$ of a finite class in a collaborative knowledge graph can be estimated from the edit history alone, by treating each month's mentions of class instances as a capture-recapture sample. Given the currently observed count $D$, the class is complete when $D = N$, so an estimate of $N$ becomes a completeness estimate. On Wikidata classes whose true sizes are known from external sources, the jackknife estimator Jack1 and the singleton-outlier-reduction estimator SOR consistently give the lowest error, while the convergence metric $\rho$ separates complete classes ($\rho < 0.001$) from incomplete ones ($\rho > 0.1$).

Load-bearing premise

The method works only if the mentions of a class's instances in the edit log are like random draws from a fixed list, with each instance having a constant chance of being mentioned in any month and edits made independently of one another.

Editorial extensions

If this is right

A Wikidata consumer can ask whether a class is complete by checking whether the convergence metric $\rho$ stays below 0.001 over the last few monthly samples, without needing an external ground-truth count.
Editors and projects can direct effort toward classes with high $\rho$ values, since those are the classes the estimators judge to be far from complete.
The method generalizes to any collaborative knowledge graph that keeps an action log with timestamps, not just Wikidata.
For classes that are already complete, the estimators slowly converge toward the true size from below, so a low error does not require waiting for the class to finish growing.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Editorial inference: the $\rho$ threshold could be turned into a monitoring signal for bulk-import workflows, because the paper itself notes that bursts of inserts cause some estimators to overestimate; a class whose $\rho$ suddenly drops after a mass import should be re-checked rather than trusted.
Editorial inference: classes maintained mostly by one bot or a small group of editors should be expected to produce less reliable estimates than classes edited by many independent volunteers, since independence of mentions is the load-bearing assumption.
Editorial inference: a natural testable extension is to weight mentions by page-view attention, which the paper lists as future work; if popularity drives mention probability, the stationary assumption is violated and estimates should degrade on celebrity-heavy classes.
Editorial inference: the same frequency-of-frequencies machinery could be applied to detect systematic misclassification in the ontology, since a wave of reclassifications would look like a burst of mentions that changes the apparent class size.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

This paper takes established species-richness estimators (Jackknife, Good-Turing, Chao92, SOR) and applies them to Wikidata edit mentions to estimate class cardinality and completeness. That application is new, and the rho convergence metric is a sensible addition. The empirical work is honest: eight classes with independent ground truth, and Jack1 and SOR beat the distinct-count lower bound on most of them. They also ship code, preprocessed data, and per-class results, which makes the work reproducible.

The main problem is the load-bearing sampling model in Section 3.2. The estimators and rho all assume independent, stationary, with-replacement observations on a closed class. The paper acknowledges that dependencies exist but says "we have not observed any significant correlations" without giving any statistic. More importantly, Wikidata editing is heavily driven by bots and bulk imports, and the paper itself shows a batch-import burst causing overshooting in Fig. 3(h). That is exactly what a violation of the independence/stationarity assumptions predicts. So the gains in Table 1 are only demonstrated for the particular mix of human and bot activity in eight selected classes, not for collaborative KGs generally.

The other soft spots are minor by comparison. There are no confidence intervals anywhere, so the point estimates in Table 1 cannot be separated from sampling noise. The rho metric inherits whatever bias the underlying estimators have, so calling a class "converged" is only as good as the estimator. And while the related work is cited well, no direct comparison is made to existing KG completeness methods (ReCoin, Galarraga et al., Soulet et al.), though those target different notions of completeness and the omission is not fatal.

The paper is clearly written, the math is standard, and the authors are upfront about several limitations, including burst sensitivity and the closed-class restriction. The central idea is plausible and the empirical evidence is suggestive, but the sampling model needs validation or at least a robustness check before the headline claim is fully supported. The right fix would be to measure the degree of assumption violation, report confidence intervals, and compare against a simple baseline (e.g., distinct count plus a perturbation). This is a solid candidate for peer review, not a desk reject, because it is a legitimate new application with reproducible artifacts and a useful practical tool. I would engage with it, and I would cite the rho metric and the empirical comparison if I worked on KG data quality.

Referee Report

5 major / 5 minor

Summary. The paper proposes a capture-recapture methodology to estimate the true cardinality of classes in Wikidata from edit histories. It formalizes class completeness estimation, introduces four non-parametric estimators (Jack1, N1-UNIF, SOR, Chao92) built on frequency-of-frequency counts, and defines an error metric φ (against external ground truth) and a convergence metric ρ (against the observed distinct count over a trailing window). The experimental study covers eight Wikidata classes with authoritative ground truth and reports per-estimator φ and ρ values, a discussion of burst-insertion effects, and large-scale examples of classes with low and high ρ. The authors conclude that Jack1 and SOR are the most accurate estimators and that ρ can be used to flag incomplete classes.

Significance. The paper addresses an important practical problem—measuring class completeness in collaboratively curated KGs—and the design has several strengths: the estimators are imported from independent ecological statistics rather than fitted to the data; the evaluation uses external authoritative counts for eight classes; the distinct-count lower bound is included as a baseline; and the authors release code, data, and a public dashboard. If the sampling model is valid, the approach provides a scalable way to prioritize editing effort in Wikidata. However, the current evidence for the model is indirect; the paper asserts independence and stationarity of edit mentions without diagnostics, and reports only point estimates. The contribution is therefore promising but not yet sufficiently validated for its stated claims.

major comments (5)

[Section 3.2; Eqs. (1)–(9)] Assumptions 2–4 (independence, with-replacement sampling, and time-invariant mention probabilities) are asserted rather than tested. The paper states 'we have not observed any significant correlations in the edits' without reporting any statistic, and Section 4.3 and Fig. 3(h) document batch insertions that violate these assumptions. Since f1 and f2 are the only inputs to Jack1, SOR, sample coverage, and Chao92, a systematic violation directly biases every estimator. Please provide diagnostics such as autocorrelation of class-level mention counts, comparison of estimates computed from human-only versus bot and bulk-import edit subsets, or a sensitivity analysis that removes burst periods, to bound the resulting bias.
[Section 3.4; Table 1] No uncertainty is reported for any estimate. Variance estimators are available for the jackknife (Burnham and Overton) and for Chao92, and a nonparametric bootstrap is straightforward from the frequency counts; without them, the differences in φ between estimators (e.g., Jack1 27.4 versus SOR 36.0 for Video Game Consoles, or Jack1 1538 versus SOR 2663 for Hospitals) cannot be distinguished from sampling noise. Please add confidence intervals or standard errors to Table 1 and to the ρ comparisons.
[Section 4.2; Table 1] The claim that 'Jack1 and SOR consistently achieve the lowest error rate across all classes' is not supported by the table. SOR has the lowest φ in only one class (Skyscrapers, 650.4); Jack1 has the lowest φ in six classes, but for Municipalities of the CZ the best estimator is N1-UNIF (22.2), and Jack1 (86.3) and SOR (31.3) are both worse than the distinct lower bound (26.6). Please either rephrase the conclusion to reflect the per-class pattern or provide a statistical test of the claimed dominance.
[Section 3.4, Eq. (11); Section 4.3, Table 2] The convergence metric ρ measures the distance between an estimator and the observed distinct count D_i, not between the estimator and the true class size. If an estimator tracks D_i closely, ρ can be near zero even for an incomplete class; conversely, an incomplete class can have an intermediate ρ (Cathedrals of Mexico has N=93, D=63, and SOR ρ=0.0162, which is neither below 0.001 nor above 0.1). The binary thresholds therefore need to be validated against ground truth rather than illustrated by random examples.
[Section 4.1; Eq. (11)] The user-specified parameters—sample period length (30 days), convergence window (w=4), and the SOR singleton cap (2σ+μ)—are fixed without a sensitivity analysis. Because all estimates and ρ values depend on these choices, the reported thresholds and estimator rankings may change under different settings; please report how Table 1 and Table 2 vary with the sample period and w.

minor comments (5)

[Section 3.4, Eq. (10)] The definition of φ is garbled in the manuscript; please rewrite it with explicit summation limits and indices so that Table 1 is reproducible.
[Section 3.3, Eq. (7)] The definitions of μ and σ are typeset incorrectly ('F∑ ... |F|−1', 'vuu√'); please clarify the summation index and whether the denominator is |F|−1 or |F|−2.
[Section 4.2] The class label 'Paintings by Vincent van Gogh (T = 864)' should use N for the class size, and the composite class notation in Fig. 3(g)–(h) is hard to parse.
[Listing 1.1] Listing 1.1 relies on an 'edit:' prefix that is not available on the public Wikidata endpoint; a reproducible description of how mentions were extracted from the Wikibase XML dump would strengthen the paper.
[Section 4.2, Fig. 3(e)] For Mountains, the ground truth is acknowledged to be 'suggestive'; given that this class contributes the largest errors in Table 1, the ranking for that row should be interpreted with caution.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the estimators are standard species-ecology statistics applied to Wikidata edit frequencies, evaluated against external ground-truth class sizes, and the only self-citation is contextual rather than load-bearing.

full rationale

The derivation chain is self-contained. The four estimators (Eqs. 1, 5, 6-7, 9) are standard species-richness statistics (Burnham-Overton jackknife, Good-Turing sample coverage, and Chao-Lee abundance coverage) applied to frequency counts f1 and f2 extracted from Wikidata edit mentions; no parameter is fitted to the ground-truth class sizes used for evaluation. The experimental evaluation compares estimator outputs against external authoritative counts (e.g., OECD municipalities, USGS volcanoes, Wikipedia lists) using the phi error metric, so the empirical claims are externally falsified rather than constructed from the inputs. The convergence metric rho in Eq. 11 is a transparent diagnostic that measures the relative gap between an estimator and the observed distinct count over the last w samples; it is not a fitted predictor, and the paper explicitly validates its low/high thresholds on classes whose completeness is known from ground truth. The only self-citation, [16], appears in a related-work sentence about edit patterns and is not used to justify any equation, assumption, or exclusion of alternatives; it is contextual. The sampling assumptions in Section 3.2 (closed classes, independence, with-replacement draws, time-invariant mention probabilities) are asserted rather than statistically verified, and bot-driven or bulk-import bursts may violate them, but that is a correctness risk about the applicability of the statistical model, not circular reasoning.

Assumptions & free parameters 3 free parameters · 7 assumptions · 0 invented entities

The central claim rests primarily on four statistical sampling assumptions that are asserted and not empirically validated. The estimators themselves are standard external methods, and the only hand-chosen values are the sample period, convergence window, and SOR threshold. No new physical or model entities are introduced.

free parameters (3)

Sample period length = 30 days
Chosen to ensure at least one observation per period on most classes; the window definition changes frequency counts f_i and therefore all estimates.
Convergence window w = 4 samples (roughly 4 months)
Used in the rho metric; chosen by the authors and directly affects which classes look converged.
SOR singleton cap = 2 sigma + mu above the mean
Adapted from Trushkowsky et al. as a heuristic to trim singleton counts; not derived from Wikidata data or calibrated to ground truth.

assumptions (7)

domain assumption Classes of interest are closed and countable as of the beginning of the experiment.
Section 3.2, Assumption 1. Limits the method to finite classes with no arrivals or departures; open classes such as events are explicitly out of scope, and the paper notes closed estimators overestimate in open populations.
domain assumption Observations are independent events.
Section 3.2, Assumption 2. Admitted as a simplification; the paper says no significant correlations were observed but provides no supporting analysis, and systematic edit patterns like adding Name followed by Date of Birth are acknowledged as possible.
domain assumption Observations within a class are random and with replacement.
Section 3.2, Assumption 3. Wikidata edits are not uniform random draws; popular entities and bulk imports dominate mention counts. This assumption is load-bearing for species estimators.
domain assumption The probability of observing an instance does not change over time.
Section 3.2, Assumption 4. Violated by import bursts and changing editor attention; the paper's own experiments show bursts cause overestimation.
domain assumption External ground-truth sources accurately report true class sizes.
Section 4.2. The error metric phi is computed against these sources; the Mountains source is described as "rather suggestive", and errors in ground truth directly bias the reported errors.
domain assumption Misclassified or untyped instances are treated as missing.
Section 4, data preparation. The paper calls this desirable because a declarative query would also miss them, but it means D and the estimates exclude incorrectly typed entities while external ground-truth counts may include them.
standard math The cited species-richness estimators are valid for the stated population model.
The paper applies Jackknife, Good-Turing, SOR, and Chao92 formulas from the statistical literature without re-derivation; if the capture model conditions fail, the formulas are biased.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Non-Parametric Class Completeness Estimators for Collaborative Knowledge Graphs -- The Case of Wikidata." pith.science (2026). https://pith.science/paper/M3XJKGIP

@misc{pith2026190901109,
  author       = {Pith},
  title        = {Pith review of: Non-Parametric Class Completeness Estimators for Collaborative Knowledge Graphs -- The Case of Wikidata},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/M3XJKGIP}},
  note         = {Machine review of arXiv:1909.01109}
}

read the original abstract

Collaborative Knowledge Graph platforms allow humans and automated scripts to collaborate in creating, updating and interlinking entities and facts. To ensure both the completeness of the data as well as a uniform coverage of the different topics, it is crucial to identify underrepresented classes in the Knowledge Graph. In this paper, we tackle this problem by developing statistical techniques for class cardinality estimation in collaborative Knowledge Graph platforms. Our method is able to estimate the completeness of a class - as defined by a schema or ontology - hence can be used to answer questions such as "Does the knowledge base have a complete list of all {Beer Brands|Volcanos|Video Game Consoles}?" As a use-case, we focus on Wikidata, which poses unique challenges in terms of the size of its ontology, the number of users actively populating its graph, and its extremely dynamic nature. Our techniques are derived from species estimation and data-management methodologies, and are applied to the case of graphs and collaborative editing. In our empirical evaluation, we observe that i) the number and frequency of unique class instances drastically influence the performance of an estimator, ii) bursts of inserts cause some estimators to overestimate the true size of the class if they are not properly handled, and iii) one can effectively measure the convergence of a class towards its true size by considering the stability of an estimator against the number of available instances.

Figures

Figures reproduced from arXiv: 1909.01109 by the authors.

**Figure 1.** The edits (Ei) of the Knowledge Graph (representing new edges) are leveraged to identify mentions. The source and target of each edge are collected to create a mention from the entity involved. Sample period #4 contains 3 edits, in which we identify 6 mentions, from which we extract 2 observations for class monument (despite the 3 mentions of entities of that class because m1 and m2 are only counted once), 1 observa… view at source ↗

**Figure 2.** The evolution of Wikidata: a temporal view on how the in- and out-degree distributions have evolved since the inception of the project. Classes and Instances in the case of Wikidata Wikidata can be interpreted as an RDF graph [8], with a data model that differentiates between entities (including classes and instances) and properties. We define classes and instances in the Wikidata graph G = (V, E) as follows: Single… view at source ↗

**Figure 3.** Estimators used on Single Domain (a)-(f) and Composite classes (g)-(h). Size Estimates on Single Domain Classes First, we inspect the results of estimating the size of a class when the query involves a single class definition. The first five figures show incomplete classes. In [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

24 extracted references · 24 canonical work pages

[1]

In: Companion Proceedings of the The Web Conference

Balaraman, V., Razniewski, S., Nutt, W.: Recoin: Relative completeness in wiki- data. In: Companion Proceedings of the The Web Conference. pp. 1787–1792 (2018) Non-Parametric Class Completeness Estimators 17

work page 2018
[2]

Journal of the American Statistical Association 88(421), 364–373 (1993)

Bunge, J., Fitzpatrick, M.: Estimating the number of species: a review. Journal of the American Statistical Association 88(421), 364–373 (1993)

work page 1993
[3]

Ecology 60(5), 927–936 (1979)

Burnham, K.P., Overton, W.S.: Robust estimation of population size when capture probabilities vary among animals. Ecology 60(5), 927–936 (1979)

work page 1979
[4]

Journal of the American statistical Association 87(417), 210–217 (1992)

Chao, A., Lee, S.M.: Estimating the number of classes via sample coverage. Journal of the American statistical Association 87(417), 210–217 (1992)

work page 1992
[5]

Biometrics 70(3), 671–682 (2014)

Chiu, C.H., Wang, Y.T., Walther, B.A., Chao, A.: An improved nonparametric lower bound of species richness via a modiﬁed good–turing frequency formula. Biometrics 70(3), 671–682 (2014)

work page 2014
[6]

ACM Trans

Darari, F., Nutt, W., Pirr` o, G., Razniewski, S.: Completeness management for rdf data sources. ACM Trans. Web 12(3), 18:1–18:53 (2018)

work page 2018
[7]

In: WSDM

Difallah, D., Filatova, E., Ipeirotis, P.: Demographics and dynamics of mechanical turk workers. In: WSDM. pp. 135–143. ACM (2018)

work page 2018
[8]

In: ISWC

Erxleben, F., G¨ unther, M., Kr¨ otzsch, M., Mendez, J., Vrandecic, D.: Introducing wikidata to the linked data web. In: ISWC. pp. 50–65 (2014)

work page 2014

Show all 24 references

[9]

In: WSDM

Gal´ arraga, L., Razniewski, S., Amarilli, A., Suchanek, F.M.: Predicting complete- ness in knowledge bases. In: WSDM. pp. 375–383 (2017)

2017
[10]

Biometrika 40(3-4), 237–264 (1953)

Good, I.J.: The population frequencies of species and the estimation of population parameters. Biometrika 40(3-4), 237–264 (1953)

1953
[11]

Biometrics pp

Heltshe, J.F., Forrester, N.E.: Estimating species richness using the jackknife pro- cedure. Biometrics pp. 1–11 (1983)

1983
[12]

In: SEMANTICS

Kaﬀee, L., Simperl, E.: The human face of the web of data: A cross-sectional study of labels. In: SEMANTICS. pp. 66–77 (2018)

2018
[13]

ACM Comput

Mannino, M.V., Chu, P., Sager, T.: Statistical proﬁle estimation in database sys- tems. ACM Comput. Surv. 20(3), 191–221 (1988)

1988
[14]

In: ICDE

Neumann, T., Moerkotte, G.: Characteristic sets: Accurate cardinality estimation for rdf queries with multiple joins. In: ICDE. pp. 984–994. IEEE (2011)

2011
[15]

Distributed and Parallel Databases 28(2-3), 119–156 (2010)

Papapetrou, O., Siberski, W., Nejdl, W.: Cardinality estimation and dynamic length adaptation for bloom ﬁlters. Distributed and Parallel Databases 28(2-3), 119–156 (2010)

2010
[16]

Computer Supported Cooperative Work (CSCW) (2018)

Sarasua, C., Checco, A., Demartini, G., Difallah, D., Feldman, M., Pintscher, L.: The evolution of power and standard wikidata editors: Comparing editing behavior over time to predict lifespan and volume of edits. Computer Supported Cooperative Work (CSCW) (2018)

2018
[17]

In: ISWC

Soulet, A., Giacometti, A., Markhoﬀ, B., Suchanek, F.M.: Representativeness of knowledge bases with the generalized benfords law. In: ISWC. pp. 374–390 (2018)

2018
[18]

In: ISWC

Tanon, T.P., Stepanova, D., Razniewski, S., Mirza, P., Weikum, G.: Completeness- aware rule learning from knowledge graphs. In: ISWC. pp. 507–525 (2017)

2017
[19]

In: ICDE

Trushkowsky, B., Kraska, T., Franklin, M.J., Sarkar, P.: Crowdsourced enumera- tion queries. In: ICDE. pp. 673–684. IEEE (2013)

2013
[20]

Com- munications of the ACM 57(10), 78–85 (2014)

Vrandeˇ ci´ c, D., Kr¨ otzsch, M.: Wikidata: A free collaborative knowledgebase. Com- munications of the ACM 57(10), 78–85 (2014)

2014
[21]

Parasitology 116(4), 395–405 (1998)

Walther, B., Morand, S.: Comparative performance of species richness estimation methods. Parasitology 116(4), 395–405 (1998)

1998
[22]

Journal of Management Information Systems 12(4), 5–33 (1996)

Wang, R.Y., Strong, D.M.: Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems 12(4), 5–33 (1996)

1996
[23]

Wulczyn, E., West, R., Zia, L., Leskovec, J.: Growing wikipedia across languages via recommendation. In: WWW. pp. 975–985 (2016)

2016
[24]

Semantic Web Journal (2015)

Zaveri, A., Rula, A., Maurino, A., Pietrobon, R., Lehmann, J., Auer, S.: Quality assessment for Linked Data: A survey. Semantic Web Journal (2015)

2015

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

[1] [1]

In: Companion Proceedings of the The Web Conference

Balaraman, V., Razniewski, S., Nutt, W.: Recoin: Relative completeness in wiki- data. In: Companion Proceedings of the The Web Conference. pp. 1787–1792 (2018) Non-Parametric Class Completeness Estimators 17

work page 2018

[2] [2]

Journal of the American Statistical Association 88(421), 364–373 (1993)

Bunge, J., Fitzpatrick, M.: Estimating the number of species: a review. Journal of the American Statistical Association 88(421), 364–373 (1993)

work page 1993

[3] [3]

Ecology 60(5), 927–936 (1979)

Burnham, K.P., Overton, W.S.: Robust estimation of population size when capture probabilities vary among animals. Ecology 60(5), 927–936 (1979)

work page 1979

[4] [4]

Journal of the American statistical Association 87(417), 210–217 (1992)

Chao, A., Lee, S.M.: Estimating the number of classes via sample coverage. Journal of the American statistical Association 87(417), 210–217 (1992)

work page 1992

[5] [5]

Biometrics 70(3), 671–682 (2014)

Chiu, C.H., Wang, Y.T., Walther, B.A., Chao, A.: An improved nonparametric lower bound of species richness via a modiﬁed good–turing frequency formula. Biometrics 70(3), 671–682 (2014)

work page 2014

[6] [6]

ACM Trans

Darari, F., Nutt, W., Pirr` o, G., Razniewski, S.: Completeness management for rdf data sources. ACM Trans. Web 12(3), 18:1–18:53 (2018)

work page 2018

[7] [7]

In: WSDM

Difallah, D., Filatova, E., Ipeirotis, P.: Demographics and dynamics of mechanical turk workers. In: WSDM. pp. 135–143. ACM (2018)

work page 2018

[8] [8]

In: ISWC

Erxleben, F., G¨ unther, M., Kr¨ otzsch, M., Mendez, J., Vrandecic, D.: Introducing wikidata to the linked data web. In: ISWC. pp. 50–65 (2014)

work page 2014