Crowdsourcing chart digitizer: task design and quality control for making legacy open data machine-readable
- 977 Downloads
Despite recent open data initiatives in many countries, a significant percentage of the data provided is in non-machine-readable formats like image format rather than in a machine-readable electronic format, thereby restricting their usability. Various types of software for digitizing data chart images have been developed. However, such software is designed for manual use and thus requires human intervention, making it unsuitable for automatically extracting data from a large number of chart images. This paper describes the first unified framework for converting legacy open data in chart images into a machine-readable and reusable format by using crowdsourcing. Crowd workers are asked not only to extract data from an image of a chart but also to reproduce the chart objects in a spreadsheet. The properties of the reproduced chart objects give their data structures, including series names and values, which are useful for automatic processing of data by computer. Since results produced by crowdsourcing inherently contain errors, a quality control mechanism was developed that improves accuracy by aggregating tables created by different workers for the same chart image and by utilizing the data structures obtained from the reproduced chart objects. Experimental results demonstrated that the proposed framework and mechanism are effective. The proposed framework is not intended to compete with chart digitizing software, and workers can use it if they feel it is useful for extracting data from charts. Experiments in which workers were encouraged to use such software showed that even if workers used it, the extracted data still contained errors. This indicates that quality control is necessary even if workers use software to extract data from chart images.
KeywordsCrowdsourcing Open data Statistical chart Data extraction Spreadsheet
The most prominent of the recent open data initiatives to publish various kinds of data in electronic format is ones for statistical data gathered by governmental agencies . Publishing such data is expected to improve government transparency, facilitate citizen participation, and create new business opportunities. These recent initiatives have led to the creation of data catalog sites by many countries, including the USA1, the UK2, and Japan3, that provide data under an open reuse license.
The data are available on the Web (in whatever format) with an open license.
The data are available in a machine-readable structured format (e.g., Microsoft Excel) instead of an image format.
The first two plus the data are in a non-proprietary format (e.g., CSV).
The first three plus open standards from the World Wide Web Consortium (W3C), the Resource Description Framework (RDF) with the SPARQL Protocol and RDF Query Language (SPARQL) are used to identify things.
The first four plus the data are linked to other people’s data to provide context.
However, a significant percentage of published statistical data was published as charts or graphs in image or PDF files. For example, of the 10,410 datasets provided by the Japanese government data site, 5452 are provided as PDF files. In the US data catalog site, 4838 of the 104,793 datasets are provided as PDF files. Such datasets earn only one star in Berners-Lee’s rating scheme and are not readily reusable because extracting data from figures and tables in PDF files is not easy even if they are provided with open licenses. One of the major reasons for such hasty data publishing was limited budgets and human resources in governmental agencies. They cannot afford to convert such data into machine-readable formats by themselves. The percentage of data published in machine-readable formats, such as CSV and RDF, will increase, but a certain amount of data will continue to be published in PDF or image files for a while. Furthermore, legacy data are generally published in such formats.
There have been certain demands for extracting values from statistical charts among the scientific community, typically for reusing data published in old papers. To meet such demands, various types of chart digitizing software such as WebPlotDigitizer5 and DataThief6 have been developed. However, such software is designed for manual use and thus requires human intervention, such as in calibrating the chart axes, making it unsuitable for automatically extracting data from a large number of data charts.
Figure 1 shows examples of charts used in the 2013 White Paper on Tourism7, which was published by the Japan Tourism Agency. Some charts are very complicated or in nonstandard formats. For example, in the line chart (b), lines representing different data series cross each other. The pie chart (c) is represented as a cylinder in a 3D space rather than a simple circle. In chart (f), both bars and lines are used to represent the data. Such variety and complexity make it difficult to automatically extract data using chart digitizing software.
Human computation  has been attracting attention as a way to solve problems that are difficult to solve solely with a computer but are solvable with some human help. The increasing size of the workforce available online provided by crowdsourcing services such as Amazon Mechanical Turk8 has been pushing widespread application of human computation to data processing tasks in various domains. This will help overcome the bottleneck in promoting open data due to limited human resources. Open data are especially suitable for being processed by human computation because the data confidentiality/privacy issue, which is sometimes a barrier to crowdsourced data processing, does not have to be considered.
Data charts are designed to help people better understand data, and people are better at understanding them than computers. We have thus taken a human computation approach to the datafication of legacy data: use crowdsourcing to extract structured data from charts in legacy file formats such as image and PDF files. Doing this will improve the ranking of such data from one star in Berners-Lee’s scheme to two or three stars. To the best of our knowledge, this paper presents the first unified framework for converting legacy open data in chart images into a machine-readable, reusable format by using crowdsourcing. Also presented is a quality control mechanism that improves the accuracy of extracted tables by aggregating tables created by different workers for the same chart image and by utilizing the data structures obtained from the reproduced chart objects. Testing showed that the proposed framework and mechanism are effective.
The goal of this paper is to demonstrate the feasibility of our approach in terms of the four components of crowdsourcing: the requester, the crowd, the task, and the platform . Note that our crowdsourcing framework was not developed to compete with chart digitizing software. In fact, such software is not excluded from our framework, and workers can use it if they feel it is useful for extracting data from charts. Since current chart digitizing software needs human intervention and thus is susceptible to human error, quality control is needed to obtain high-quality results. In that sense, our proposed framework is independent of using/not using chart digitizing software. We include experiments in which workers were encouraged to use chart digitizing software as well.
The remainder of the paper is organized as follows. Section 2 discusses related work on open data and crowdsourcing. Section 3 describes our framework for digitizing chart images using crowdsourcing, and Sect. 4 details the quality control mechanism used in our framework. Section 5 presents experiments using real-case data to demonstrate the feasibility and effectiveness of our framework, and Sect. 6 shows experimental results when we encouraged workers to use chart digitizing software within our framework. Finally, Sect. 7 summarizes the key points and describes future research directions.
2 Related work
Studies to promote open data in accordance with the road map put forth by Berners-Lee have been conducted by researchers investigating the Semantic Web. However, their research interests have focused mainly on pulling three-star data up to the fourth or fifth levels and building services on top of them—few have focused on dealing with one-star data.
Han et al.  developed an open-source tool for converting spreadsheet data into RDF format. Users define the relationships between the columns of a spreadsheet table in a map graph by using a graphical interface. A Web service then takes the spreadsheet or CSV file and the map graph and provides an RDF file as output. Mulwad et al.  presented techniques for automatically inferring the semantics of column headers and cell values and the relationships between columns. Their techniques are based on graphical models and probabilistic reasoning augmented with background knowledge from the Linked Open Data cloud (Web of Linked Data ).
The RDF Data Cube Vocabulary9 is a W3C recommendation for publishing and sharing statistical data on the Web. In the Data Cube model, data observations (values in table cells) are characterized by dimensions, attributes, and measures. Meroño-Peñuela et al.  converted historical census data into RDF format by using the Data Cube Vocabulary and a semiautomatic process: First, an expert manually annotated tables in Microsoft Excel workbooks, and then software called TabLinker10 was used to convert them into RDF data cubes.
Government data made public with open licenses are called open government data (OGD) and are considered important for enhancing the transparency of governance and improving public services by promoting participatory decision making. Shadbolt et al.  described a project for integrating CSV data and spreadsheet data published on the UK data catalog site in the Web of Linked Data. Kalampokis et al.  asserted that the real value of OGD comes from performing data analytics on top of combined datasets from different sources. As a test case, they used various published datasets including the unemployment rate dataset published in spreadsheet form on data.gov.uk and datasets regarding the outcome of UK general elections published in spreadsheet form on the Guardian newspaper’s Web site under an open license. They converted the datasets into RDF data cubes and performed data analytics using the combined datasets. They showed that there was a correlation between the probability of one of the two main political parties winning a particular constituency and the unemployment rate for that constituency.
Crowdsourcing is a means for asking many workers to perform tasks via the Internet. There are various types of crowdsourcing, which can be classified in terms of workers, requesters, tasks, and platforms . Crowd workers can be paid via a crowdsourcing market such as Amazon Mechanical Turk or be volunteers. In the latter case, games with a purpose  are usually used. Human computation is a computing paradigm used to solve problems that are difficult to solve solely by computer but are relatively easy to solve if human assistance is provided. It is generally achieved by asking people to complete small tasks, called microtasks or human intelligence tasks, by crowdsourcing. Human computation approaches have been successfully applied to various domains including computer vision and natural language processing, for which current artificial intelligence is still no match for human intelligence. The importance of explicit or implicit involvement of human intelligence is also emphasized in metasynthetic engineering of complex systems , in which human intelligence and machine intelligence are integrated into problem-solving systems. Human intelligence is expected to provide qualitative reasoning capability and complement quantitative machine intelligence.
One of the most successful examples of human computation is reCAPTCHA , which is a system both for verifying that an on-line user is actually human and for deciphering words unrecognized by optical character recognition (OCR) software used in, for example, a book digitization project. Two image files are presented to the user, one containing a word known by the computer and used for verification, and one containing an unrecognized word. The user must type the characters shown in both files. If the known word is typed correctly, the user is recognized as human. If enough users interpret the unknown word as a certain word, that word is considered valid. This process can be considered to be digitizing characters in images using human computation.
A British newspaper The Guardian conducted experiments to analyze the receipts of the Members of Parliament using crowdsourcing.11 Photocopies of handwritten receipts were provided in image files, and users were asked to review each of them and type the content, label them in terms of level of interest, and write some comments. This can be considered as an example of datafication of open data provided in image files. In contrast, our work focuses on digitizing statistical charts in image files.
AskSheet  is a system that uses crowdsourcing to make spreadsheet tables. It does not ask workers to make an entire table but instead asks them to gather the information needed to fill in the blank cells in a spreadsheet. As with our approach, it uses a quality control mechanism to compensate for erroneous inputs by workers, but the mechanism is applied to individual values while ours is applied to the entire table. Fan et al.  proposed a framework for utilizing crowd workers to aggregate tables. They prepared a concept catalog and asked the workers to select the concept that best represented the values in each column. The selected concepts were used to match the columns that appeared in different tables but corresponded semantically. They focused only on one-dimensional tables and assumed that there were no incorrect values in the tables. In contrast, we focused on two-dimensional tables and assumed that there were incorrect values. Ermilov et al.  proposed a formalization of tabular data as well as its mapping and transformation to RDF, which enable the crowdsourcing of large-scale semantic mapping of tabular data. They mentioned automatic header recognition in CSV files as important future work. In our approach, headers in tables can be easily recognized by referring to the properties of the chart objects in the spreadsheets.
The Semantic Web is an endeavor to make the Web machine-understandable, so both machine and human intelligence are naturally needed to achieve it. Human computation practices have been applied to various subproblems as part of enabling the Semantic Web [16, 17, 18, 19, 20].
3 Framework for digitizing chart images using crowdsourcing
In this section we describe our framework for digitizing chart images using crowdsourcing. We present our task design, which makes it easy to extract structured data, and discuss the feasibility of our framework in terms of the requester, the workers, the tasks, and the platform.
3.1 Task design
While Berners-Lee ranks CSV above Microsoft Excel since the former is an open format while the latter uses a proprietary one, in practice, the distinction is not substantial because recent versions of Excel use the Office Open XML format, and data in this format are readable by other software. Thus, we use Excel spreadsheets as the format in which to save data extracted from chart images.
3.2 Structured data extraction through visualization
A chart (Chart) has several data series (Series).
Each data series (Series) has a name (Name).
Each data series (Series) has x-axis values (XValues) and values (Values).
3.3 Feasibility of our crowdsourcing framework
According to Hosseini et al. , crowdsourcing can be classified on the basis of its four components: the crowdsourcer, the crowd, the crowdsourced task, and the crowdsourcing platform. In our framework, the crowdsourcer (requester) is a governmental agency that owns legacy data. Such an agency has a clear motivation for using crowdsourcing since it does not have enough human resources to convert legacy data into a machine-readable format, and crowdsourcing can reduce the monetary cost of converting the data.
The crowd in our case consists of people who can use spreadsheet software such as Microsoft Excel. The key to the success of our crowdsourcing approach is the availability of such users. Fortunately, Excel is one of the most commonly used business software products worldwide. While we do not have the precise number of Excel users, as of November 2014 more than 1.2 billion people were using one or more of the Office products, which include Excel.13 Among the 109,344 workers registered with Lancers14, a crowdsourcing marketplace in Japan, 17,917 have “Excel” in their profiles. The task of extracting data from a chart image is not a simple microtask but a complex task that requires certain software skills. In Sect. 5, we demonstrate that we were able to gather workers with the skills needed to complete the task.
In our case study, we used Lancers but we can use any crowdsourcing platform that has basic functions such as task assignment and reward payment. The quality control mechanism using data aggregation is performed only on the requester’s computer using Visual Basic .NET (VB.NET) software. Thus, our framework can be easily implemented by most governmental agencies.
4 Quality control mechanism
Human-created tables may contain errors such as typos and missing values. This is especially true for crowdsourcing as the workers are usually anonymous and not well trained. The requester thus has to take into account errors and introduce a quality control mechanism that makes the results error-tolerant. A common approach to quality control in crowdsourcing is introducing redundancy by asking multiple workers to do the same task. For example, in classification tasks such as image classification, using the majority criterion generally improves the accuracy of the final result.
4.1 Alignment of rows and columns
In general, the order of rows and columns in a table is arbitrary (except obvious cases in which the order is naturally defined such as for dates), so different workers may order the rows and columns differently. For example, different workers may arrange the row labels (“London,” “New York,” “Tokyo”) in the chart in Fig. 4 differently. Therefore, when aggregating the tables created by different workers, we first have to align the rows and columns among the different tables.
The names of rows (or columns) are the most important clue for judging whether two rows (columns) are identical; however, the names may contain errors or are sometimes missing in tables created by crowd workers. In that case, if the values in the rows (columns) are the same, the rows (columns) can be judged to be the same. Therefore, we introduce the similarity of two rows (columns) considering both their names and values and use it to find matching between rows (columns).
Calculate the similarities between all rows produced by the two workers.
Assume that row pairs with high similarity contain identical data, and pair them up.
Pair any remaining unmatched rows with dummy rows.
4.2 Aggregating table headers and cell values
Since the results produced by crowdsourcing may contain errors, after the rows and columns of the tables are matched, the corresponding values from the tables are integrated to obtain a single table. The majority criterion is used to determine the final values for table headers since nominal values such as names are frequently used. The median value is used for cell values since numerical values are frequently used and the median is more robust to outliers than the average. Considering human errors as outliers rather than noise (as they are in instrument measurements) is appropriate because crowd workers can make errors of great magnitude. For example, consider a case in which the inputs from three workers are 1982, 1892, and 1982 and the actual value is 1982; the median matches the actual value while the average value greatly differs.
5 Case study
In this section, we describe our experiments on digitizing actual statistical chart images by using crowdsourcing. Our goal was to evaluate the feasibility of our framework and the effectiveness of the quality control mechanism.
5.1 Dataset and software
We evaluated our proposed framework and quality control mechanism experimentally by using chart images from the 2013 White Paper on Tourism15 published by the Japan Tourism Agency. The white paper is published under a Creative Commons CC-BY license, and most of the statistical data are provided as figures embedded in HTML documents or PDF files, i.e., as one-star open data in Berners-Lee’s scheme. Among the 104 images used in the white paper, 61 explicitly show values as data labels, and we used them as the gold standard for evaluating the correctness of the extracted values.
We compared the results for two crowdsourcing tasks. One simply asked workers to extract data from charts and put them in a spreadsheet (“Create Table” tasks), and the other asked workers to reproduce charts in a spreadsheet (“Reproduce Chart” tasks). We asked five workers to complete each task. We used the Lancers crowdsourcing service and paid 200 JPY (approximately 2 dollars) for each completed task. A worker was not required to do all tasks—23 different workers did at least one Create Table task, and 30 different workers did at least one Reproduce Chart task.
We implemented the table aggregation software using VB.NET. This software took the Excel files created by the workers as input and searched each file for a Chart object. If multiple Chart objects were found, it used only the one with the first index number. For each Series object in the Chart object, it extracted the values of the Series.Name, Series.XValues, and Series.Values properties from the corresponding cells in the worksheet as the row headers, column headers, and cell values, respectively. The table aggregation algorithm was then applied to the set of worker tables (with \(\alpha _x=\alpha _z=0.9\)), and the aggregated table was stored as a worksheet in a new Excel file.
5.2 Accuracy of worker tables
5.3 Accuracies of aggregated tables
We next measured the accuracies of the aggregated tables. At least three tables are required so that using the majority criterion or the median works well. We compared two different settings. In one, all five worker-generated tables were used for each chart image; in the other, three randomly selected tables were used for each image. Figure 13 shows the percentages for different types of errors after aggregation. Aggregation greatly improved the accuracy for cell values. It also eliminated most of the incorrect and missing headers, but it was not very effective for reducing the incomplete headers.
6 Analysis of results when workers used chart digitizing software
As discussed in Sect. 2, there are various types of chart digitizing software available for digitizing chart images in documents such as research papers. Such software is useful for obtaining the values of data points in a graph, but it is not fully automatic and thus needs human intervention. Our framework does not prevent workers from using such software. In this section, we describe the experiments we conducted to analyze the effect of using it. In these experiments, we recommended to the workers that they use chart digitizing software if they thought it would be useful and asked them, if they used such software, to identify the software used.
6.1 Example of chart digitizing software
- First, the user is asked to click two reference points on the X-axis and two reference points on the Y-axis.
Then the user is asked to enter the X-values of the points on the X-axis and the Y-values of the points on the Y-axis.
The software calculates the scales of the X- and Y-axes by using the values of the reference points.
In “manual” mode, the user is asked to specify each point on each line, and the software calculates the X–Y values of the point.
In “automatic” mode, the user specifies the color of the line in the chart, and the software automatically calculate the X–Y values of points on the line.
6.2 Experimental settings
7 Summary and future work
Converting legacy open data in, for example, image files into machine-readable format is an important step toward realizing the potential of the Linked Open Data cloud, but it is labor-intensive, and there have been few related research efforts. We proposed using crowdsourcing to digitize chart images and introduced a task design. In this design, crowd workers are asked to reproduce chart images as embedded chart objects in spreadsheets, which enables automatic identification of table data structures from the properties of the chart objects. To reduce the number of errors inherent in crowdsourced results, we developed a quality control mechanism. Multiple workers are asked to digitize the same chart image, and the tables they create are aggregated. Experimental results demonstrated that our approach is effective, but many tasks remain for future work. They can be grouped into four main areas, as described below.
7.1 Task design
Several lessons were drawn from the results of our experiments. The inconsistency in the use of units could be reduced by defining units as cell styles in advance and asking the workers to use them. In the experiments, we asked the workers to upload their Excel files to a file server, and we downloaded them manually to a local machine, on which we ran a table aggregation program. This procedure is prone to problems, such as lost and misidentified files. Performing all the processes in a single cloud environment would greatly improve the throughput and accuracy of the chart digitizing tasks.
7.2 Table aggregation
We assumed that the workers had consistent abilities, and we used simple aggregation methods for table integration; however, workers in a crowdsourcing setting generally have various degrees of ability. Application of more sophisticated statistical quality control methods that consider worker-dependent abilities (e.g., [21, 22]) is a possible future direction for improving integration accuracy.
7.3 Integrating chart digitizing software in the framework
As shown in Sect. sec:software, using chart digitizing software did not always reduce the error size, but if appropriately used, it could contribute to reducing it. The usefulness of software depends on the type of chart to be digitized, so recommending to users whether or not to use software for each task and, if so, recommending specific software could contribute to producing more accurate results. Different types of software cause different degrees of error, which may have different statistical patterns. This means that utilizing information on whether or not software was used and, if it was, what type of software was used should help reduce the error size in the final results.
7.4 Converting tables into RDF format
The next step according to the roadmap for the Linked Open Data cloud is converting tables into RDF format. The dimensions, measures, and attributes in the RDF Data Cube Vocabulary, a framework for representing statistical tables, generally correspond to headers, values, and units of values in statistical tables. After the headers and values are extracted using a chart digitizing approach, we have to relate them to a formally defined vocabulary. This process is also difficult to perform solely by computer; it requires human intelligence. Therefore, the use of crowdsourcing to convert tables into RDF format is important future work. Collecting publicly available spreadsheets with charts and extracting names from them would help in constructing a vocabulary for describing statistical data.
7.5 Structured data extraction through visualization
We reproduced image-formatted charts in spreadsheets to enable us to extract table-formatted data from them. However, there are many other data types that are not provided in visualized formats such as CSV-like texts and spreadsheets without charts. Producing charts from these non-visualized data would make the data easier to understand; moreover, such processes would help in extracting the structures of the data as a by-product. This is referred to as unsupervised visualization of the data while chart reproduction from images is referred to as supervised visualization.
SO was supported by a Grant-in-Aid for Scientific Research (No. 15K1214805) from the Japan Society for the Promotion of Science.
Compliance with ethical standards
Conflicts of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
- 1.Oyama, S., Baba, Y., Ohmukai, I., Dokoshi, H., Kashima, H.: From one star to three stars: upgrading legacy open data using crowdsourcing. In: Proceeding of the 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA 2015) (2015)Google Scholar
- 2.Shadbolt, N., O’Hara, K., Berners-Lee, T., Gibbins, N., Glaser, H., Hall, W., schraefel, m. c.: Linked open government data: lessons from data.gov.uk. IEEE Intell. Syst. 27(3), 16 (2012)Google Scholar
- 3.Law, E., von Ahn, L.: Human Computation. Morgan & Claypool Publishers, San Rafael (2011)Google Scholar
- 4.Hosseini, M., Phalp, K., Taylor, J., Ali, R.: The four pillars of crowdsourcing: a reference model. In: Proceedings of the 2014 IEEE Eighth International Conference on Research Challenges in Information Science (RCIS 2014), pp. 1–12 (2014)Google Scholar
- 5.Han, L., Finin, T., Parr, C., Sachs, J., Joshi, A.: RDF123: from spreadsheets to RDF. In: Proceedings of the 7th International Semantic Web Conference (ISWC 2008), pp. 451–466 (2008)Google Scholar
- 6.Mulwad, V., Finin, T., Joshi, A.: Automatically generating government linked data from tables. In: Working Notes of AAAI Fall Symposium on Open Government Knowledge: AI Opportunities and Challenges (2011)Google Scholar
- 8.Meroño-Peñuela, A., Hoekstra, R., Scharnhorst, A., Guéret, C., Ashkpour, A.: Longitudinal queries over linked census data. In: Proceedings of the 10th Extended Semantic Web Conference (ESWC 2013), pp. 306–307 (2013)Google Scholar
- 9.Kalampokis, E., Tambouris, E., Tarabanis, K.: Linked open government data analytics. In: Proceedings of the IFIP Electronic Government Conference (EGOV 2013), pp. 99–110 (2013)Google Scholar
- 10.von Ahn, L., Dabbish, L.: Designing games with a purpose. Commun. ACM 51(8), 58 (2008)Google Scholar
- 13.Quinn, A.J., Bederson, B.B.: AskSheet: efficient human computation for decision making with spreadsheets. In: Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW 2014), pp. 1456–1466 (2014)Google Scholar
- 14.Fan, J., Lu, M., Ooi, B.C., Tan, W.C., Zhang, M.: A hybrid machine-crowdsourcing system for matching web tables. In: Proceedings of the 30th IEEE 30th International Conference on Data Engineering (ICDE 2014), pp. 976–987 (2014)Google Scholar
- 15.Ermilov, I., Auer, S., Stadler, C.: User-driven semantic mapping of tabular data. In: Proceedings of the 9th International Conference on Semantic Systems (I-SEMANTICS 2013), pp. 105–112 (2013)Google Scholar
- 16.Simperl, E., Norton, B., Vrandečić, D.: Crowdsourcing tasks in linked data management. In: Proceedings of the 2nd Workshop on Consuming Linked Data (COLD 2011) (2011)Google Scholar
- 17.Demartini, G., Difallah, D.E., Cudré-Mauroux, P.: ZenCrowd: leveraging probabilistic reasoning and crowdsourcing techniques for large-scale entity linking. In: Proceedings of the 21st International Conference on World Wide Web (WWW 2012), pp. 469–478 (2012)Google Scholar
- 18.Noy, N.F., Mortensen, J., Musen, M.A., Alexander, P.R.: Mechanical turk as an ontology engineer?: using microtasks as a component of an ontology-engineering workflow. In: Proceedings of the 5th Annual ACM Web Science Conference (WebSci 2013), pp. 262–271 (2013)Google Scholar
- 19.Acosta, M., Zaveri, A., Simperl, E., Kontokostas, D., Auer, S., Lehmann, J.: Crowdsourcing linked data quality assessment. In: Proceedings of the 12th International Semantic Web Conference (ISWC 2013), pp. 260–276 (2013)Google Scholar
- 20.DiFranzo, D., Hendler, J.: The semantic web and the next generation of human computation. In: Handbook of Human Computation, pp. 523–530. Springer, New York (2013)Google Scholar
- 21.Dawid, A.P., Skene, A.M.: Maximum likelihood estimation of observer error-rates using the EM algorithm. J. R. Stat. Soc. Ser. C (Appl. Stat.) 28(1), 20–28 (1979)Google Scholar
- 22.Whitehill, J., Ruvolo, P., Wu, T., Bergsma, J., Movellan, J.: Whose vote should count more: optimal integration of labels from labelers of unknown expertise. In: Advances in Neural Information Processing Systems 22 (2009)Google Scholar