Accurate cell counting is essential in various biomedical research and clinical applications, including cancer diagnosis, stem cell research, and immunology. Manual counting is laborintensive and error-prone, motivating automation through deep learning techniques. However, training reliable deep learning models requires large amounts of high-quality annotated data, which is difficult and time-consuming to produce manually. Consequently, existing cell-counting datasets are often limited, frequently containing fewer than 500 images. In this work, we introduce a large-scale annotated dataset comprising 3,023 images from immunocytochemistry experiments related to cellular differentiation, containing over 430,000 manually annotated cell locations. The dataset presents significant challenges: high cell density, overlapping and morphologically diverse cells, a long-tailed distribution of cell count per image, and variation in staining protocols. We benchmark three categories of existing methods: regression-based, crowd-counting, and cell-counting techniques on a test set with cell counts ranging from 10 to 2,126 cells per image. We also evaluate how the Segment Anything Model (SAM) can be adapted for microscopy cell counting using only dot-annotated datasets. As a case study, we implement a density-map-based adaptation of SAM (SAM-Counter) and report a mean absolute error (MAE) of 22.12, which outperforms existing approaches (second-best MAE of 27.46). Our results underscore the value of the dataset and the benchmarking framework for driving progress in automated cell counting and provide a robust foundation for future research and development.

Aspect-Based Sentiment Analysis (ABSA) comprises several subtasks: aspect term extraction (ATE), opinion term extraction (OTE), aspect term sentiment extraction (ATSE), aspect-opinion pair extraction (AOPE), and aspect sentiment triplet extraction (ASTE). Existing unified frameworks for ABSA rely heavily on large-scale annotated data, limiting scalability across domains. We propose UAOS, a double-layer unified span extraction framework that performs all five ABSA subtasks under weak supervision. Our approach first extracts aspect-opinion pairs using universal dependency-based rules from unannotated corpora. Sentiment labels for these pairs are generated via a novel zero-shot, domain-agnostic prompt-based method. The resulting weak labels train a unified span extraction architecture equipped with canonical correlation analysis for early stopping and a self-training mechanism to mitigate noise and bias in supervision. Extensive experiments on four ABSA benchmarks demonstrate that UAOS achieves competitive or superior performance compared to fully supervised baselines. It improves upon the state-of-the-art ODAO by +1.54 F1 for ATE, +0.56 for OTE, and +0.82 for AOPE. In ATSE and ASTE, where no weakly supervised baselines exist, UAOS outperforms several supervised models, setting new benchmarks. To assess domain generalizability, we evaluate UAOS on a psychology/education-domain dataset of student reflections spanning four instructional conditions. Without in-domain fine-tuning, it achieves macro F1 scores of 71.05 (ATE), 74.39 (OTE), 68.24 (AOPE), and 60.56 (ASTE). These results highlight the model’s ability to generalize to out-of-distribution, non-commercial text, underscoring its scalability for low-resource ABSA applications.

The MIS2-TrueFact is geared towards bringing academic, industry, and government researchers and practitioners together to tackle the challenges in misinformation, misbehavior, and data quality issues on the web with heterogeneous and multi-modal sources of information including texts, images, videos, relational data, social networks, and knowledge graphs.

The aspect-opinion extraction tasks extract aspect terms and opinion terms from reviews. The supervised extraction methods achieve state-of-the-art performance but require large-scale human-annotated training data. Thus, they are restricted for open-domain tasks due to the lack of training data. This work addresses this challenge and simultaneously mines aspect terms, opinion terms, and their correspondence in a joint model. We propose an Open-Domain Aspect-Opinion Co-Mining (ODAO) method with a Double-Layer span extraction framework. Instead of acquiring human annotations, ODAO first generates weak labels for unannotated corpus by employing rules-based on universal dependency parsing. Then, ODAO utilizes this weak supervision to train a double-layer span extraction framework to extract aspect terms (ATE), opinion terms (OTE), and aspect-opinion pairs (AOPE). ODAO applies canonical correlation analysis as an early stopping indicator to avoid the model over-fitting to the noise to tackle the noisy weak supervision. ODAO applies a self-training process to gradually enrich the training data to tackle the weak supervision bias issue. We conduct extensive experiments and demonstrate the power of the proposed ODAO. The results on four benchmark datasets for aspect-opinion co-extraction and pair extraction tasks show that ODAO can achieve competitive or even better performance compared with the state-of-the-art fully supervised methods.

Relation extraction (RE) is an important task for many natural language processing applications. Document-level relation extraction task aims to extract the relations within a document and poses many challenges to the RE tasks as it requires reasoning across sentences and handling multiple relations expressed in the same document. Existing state-of-the-art document-level RE models use the graph structure to better connect long-distance correlations. In this work, we propose SagDRE model, which further considers and captures the original sequential information from the text. The proposed model learns sentence-level directional edges to capture the information flow in the document and uses the token-level sequential information to encode the shortest paths from one entity to the other. In addition, we propose an adaptive margin loss to address the long-tailed multi-label problem of document-level RE tasks, where multiple relations can be expressed in a document for an entity pair and there are a few popular relations. The loss function aims to encourage separations between positive and negative classes. The experimental results on datasets from various domains demonstrate the effectiveness of the proposed methods.

Diverse Natural Language Processing tasks employ constituency parsing to understand the syntactic structure of a sentence according to a phrase structure grammar. Many state-of-the-art constituency parsers are proposed, but they may provide different results for the same sentences, especially for corpora outside their training domains. This paper adopts the truth discovery idea to aggregate constituency parse trees from different parsers by estimating their reliability in the absence of ground truth. Our goal is to consistently obtain high-quality aggregated constituency parse trees. We formulate the constituency parse tree aggregation problem in two steps, structure aggregation and constituent label aggregation. Specifically, we propose the first truth discovery solution for tree structures by minimizing the weighted sum of Robinson-Foulds (RF) distances, a classic symmetric distance metric between two trees. Extensive experiments are conducted on benchmark datasets in different languages and domains. The experimental results show that our method, CPTAM, outperforms the state-of-the-art aggregation baselines. We also demonstrate that the weights estimated by CPTAM can adequately evaluate constituency parsers in the absence of ground truth.

Annotation quality and quantity positively affect the learning performance of sequence labeling, a vital task in Natural Language Processing. Hiring domain experts to annotate a corpus is very costly in terms of money and time. Crowdsourcing platforms, such as Amazon Mechanical Turk (AMT), have been deployed to assist in this purpose. However, the annotations collected this way are prone to human errors due to the lack of expertise of the crowd workers. Existing literature in annotation aggregation assumes that annotations are independent and thus faces challenges when handling the sequential label aggregation tasks with complex dependencies. To conquer the challenges, we propose an optimization-based method that infers the ground truth labels using annotations provided by workers for sequential labeling tasks. The proposed Aggregation method for Sequential Labels from Crowds (AggSLC) jointly considers the characteristics of sequential labeling tasks, workers’ reliabilities, and advanced machine learning techniques. Theoretical analysis on the algorithm’s convergence further demonstrates that the proposed AggSLC halts after a finite number of iterations. We evaluate AggSLC on different crowdsourced datasets for Named Entity Recognition (NER) tasks and Information Extraction tasks in biomedical (PICO), as well as a simulated dataset. Our results show that the proposed method outperforms the state-of-the-art aggregation methods. To achieve insights into the framework, we study the effectiveness of AggSLC’s components through ablation studies.

The Third International TrueFact Workshop: Making a Credible Web for Tomorrow is geared towards bringing academic, industry and government researchers and practitioners together to tackle the challenges in misinformation, data quality, truth finding, fact-checking, credibility analysis and rumor detection -- in heterogeneous and multi-modal sources of information including texts, images, videos, relational data, social networks and knowledge graphs.

A key challenge of big data analytics is how to collect a large volume of (labeled) data. Crowdsourcing aims to address this challenge via aggregating and estimating high-quality data (e.g., sentiment label for text) from pervasive clients/users. Existing studies on crowdsourcing focus on designing new methods to improve the aggregated data quality from unreliable/noisy clients. However, the security aspects of such crowdsourcing systems remain under-explored to date. We aim to bridge this gap in this work. Specifically, we show that crowdsourcing is vulnerable to data poisoning attacks, in which malicious clients provide carefully crafted data to corrupt the aggregated data. We formulate our proposed data poisoning attacks as an optimization problem that maximizes the error of the aggregated data. Our evaluation results on one synthetic and two real-world benchmark datasets demonstrate that the proposed attacks can substantially increase the estimation errors of the aggregated data. We also propose two defenses to reduce the impact of malicious clients. Our empirical results show that the proposed defenses can substantially reduce the estimation errors of the data poisoning attacks.

Supervised deep learning methods have achieved state-of-the-art performance on the task of named entity recognition (NER). However, such methods suffer from high cost and low efficiency in training data annotation, leading to highly specialized NER models that cannot be easily adapted to new domains. Recently, distant supervision has been applied to replace human annotation, thanks to the fast development of domain-specific knowledge bases. However, the generated noisy labels pose significant challenges in learning effective neural models with distant supervision. We propose PatNER, a distantly supervised NER model that effectively deals with noisy distant supervision from domain-specific dictionaries. PatNER does not require human-annotated training data but only relies on unlabeled data and incomplete domain-specific dictionaries for distant supervision. It incorporates the distant labeling uncertainty into the neural model training to enhance distant supervision. We go beyond the traditional sequence labeling framework and propose a more effective fuzzy neural model using the tie-or-break tagging scheme for the NER task. Extensive experiments on three benchmark datasets in two domains demonstrate the power of PatNER. Case studies on two additional real-world datasets demonstrate that PatNER improves the distant NER performance in both entity boundary detection and entity type recognition. The results show a great promise in supporting high quality named entity recognition with domain-specific dictionaries on a wide variety of entity types.

Scientific literature, as one of the major knowledge resources, provides abundant textual evidence that has great potential to support high-quality scientific hypothesis validation. In this paper, we study the problem of textual evidence mining in scientific literature: given a scientific hypothesis as a query triplet, find the textual evidence sentences in scientific literature that support the input query. A critical challenge for textual evidence mining in scientific literature is to retrieve high-quality textual evidence without human supervision. Because it is non-trivial to obtain a large set of human-annotated articles containing evidence sentences in scientific literature. To tackle this challenge, we propose EvidenceMiner, a high-quality textual evidence retrieval method for scientific literature without human-annotated training examples. To achieve high-quality textual evidence retrieval, we leverage heterogeneous information from both existing knowledge bases and massive unstructured text. We propose to construct a large heterogeneous information network (HIN) to build connections between the user-input queries and the candidate evidence sentences. Based on the constructed HIN, we propose a novel HIN embedding method that directly embeds the nodes onto a spherical space to improve the retrieval performance. Quantitative experiments on a huge biomedical literature corpus (over 4 million sentences) demonstrate that EvidenceMiner significantly outperforms baseline methods for unsupervised textual evidence retrieval. Case studies also demonstrate that our HIN construction and embedding greatly benefit many downstream applications such as textual evidence interpretation and synonym meta-pattern discovery.

GitHub has become an important platform for code sharing and scientific exchange. With the massive number of repositories available, there is a pressing need for topic-based search. Even though the topic label functionality has been introduced, the majority of GitHub repositories do not have any labels, impeding the utility of search and topic-based analysis. This work targets the automatic repository classification problem as keyword-driven hierarchical classification. Specifically, users only need to provide a label hierarchy with keywords to supply as supervision. This setting is flexible, adaptive to the users' needs, accounts for the different granularity of topic labels and requires minimal human effort. We identify three key challenges of this problem, namely (1) the presence of multi-modal signals; (2) supervision scarcity and bias; (3) supervision format mismatch. In recognition of these challenges, we propose the HiGitClass framework, comprising of three modules: heterogeneous information network embedding; keyword enrichment; topic modeling and pseudo document generation. Experimental results on two GitHub repository collections confirm that HiGitClass is superior to existing weakly-supervised and dataless hierarchical classification methods, especially in its ability to integrate both structured and unstructured data for repository classification. Code and datasets related to this paper are available at https://github.com/yuzhimanhua/HiGitClass.

The task of temporal slot filling (TSF) is to extract the values (or called facts) of specific attributes for a given entity from text data and find the time points when the values were valid. It is challenging to find precise time points with incomplete and noisy temporal contexts in the text. In this work, we propose an unsupervised approach of two modules that mutually enhance each other: one is a reliability estimator on fact extractors conditionally to the temporal contexts; the other is a fact trustworthiness estimator based on the extractor's reliability. The iterative learning process reduces the noise of the extractions. Experiments demonstrate that our approach, with the novel design, can accurately and efficiently extract precise temporal facts from newspaper corpora.

The demand for automatic extraction of true information (i.e., truths) from conflicting multi-source data has soared recently. A variety of truth discovery methods have witnessed great successes via jointly estimating source reliability and truths. All existing truth discovery methods focus on providing a point estimator for each object's truth, but in many real-world applications, confidence interval estimation of truths is more desirable, since confidence interval contains richer information. To address this challenge, in this paper, we propose a novel truth discovery method (ETCIBoot) to construct confidence interval estimates as well as identify truths, where the bootstrapping techniques are nicely integrated into the truth discovery procedure. Due to the properties of bootstrapping, the estimators obtained by ETCIBoot are more accurate and robust compared with the state-of-the-art truth discovery approaches. The proposed framework is further adapted to deal with large-scale truth discovery task in distributed paradigm. Theoretically, we prove the asymptotical consistency of the confidence interval obtained by ETCIBoot. Experimentally, we demonstrate that ETCIBoot is not only effective in constructing confidence intervals but also able to obtain better truth estimates.

Truth discovery aims at obtaining the most credible information from multiple sources that provide noisy and conflicting values. Due to the ubiquitous existence of data conflict in practice, truth discovery has been attracting a lot of research attention recently. Unfortunately, existing truth discovery models all miss an important issue of truth discovery - the truth evolution problem. In many real-life scenarios, the latent true value often keeps changing dynamically over time instead of staying static. We study the dynamic truth discovery problem in the space of numerical truth discovery. This problem cannot be addressed by existing models because of the new challenges of capturing time-evolving source dependency in a continuous space as well as handling missing data on the fly. We propose a model named EvolvT for dynamic truth discovery on numerical data. With the hidden Markov framework, EvolvT captures three key aspects of dynamic truth discovery with a unified model: truth transition regularity, source quality, and source dependency. The most distinguishable feature of the modeling part of EvolvT is that it employs Kalman filtering to model truth evolution. As such, EvolvT not only can principally infer source dependency in a continuous space, but also can handle missing data in a natural way. We establish an expectation-maximization (EM) algorithm for parameter inference of EvolvT and present an efficient online version for the parameter inference procedure. Our experiments on real-world applications demonstrate its advantages over the state-of-the-art truth discovery approaches.

Pattern-based methods have been successful in information extraction and NLP research. Previous approaches learn the quality of a textual pattern as relatedness to a certain task based on statistics of its individual content (e.g., length, frequency) and hundreds of carefully-annotated labels. However, patterns of good content-quality may generate heavily conflicting information due to the big gap between relatedness and correctness. Evaluating the correctness of information is critical in (entity, attribute, value)-tuple extraction. In this work, we propose a novel method, called TruePIE, that finds reliable patterns which can extract not only related but also correct information. TruePIE adopts the self-training framework and repeats the training-predicting-extracting process to gradually discover more and more reliable patterns. To better represent the textual patterns, pattern embeddings are formulated so that patterns with similar semantic meanings are embedded closely to each other. The embeddings jointly consider the local pattern information and the distributional information of the extractions. To conquer the challenge of lacking supervision on patterns' reliability, TruePIE can automatically generate high quality training patterns based on a couple of seed patterns by applying the arity-constraints to distinguish highly reliable patterns (i.e., positive patterns) and highly unreliable patterns (i.e., negative patterns). Experiments on a huge news dataset (over 25GB) demonstrate that the proposed TruePIE significantly outperforms baseline methods on each of the three tasks: reliable tuple extraction, reliable pattern extraction, and negative pattern extraction.

Soliciting answers from online users is an efficient and effective solution to many challenging tasks. Due to the variety in the quality of users, it is important to infer their ability to provide correct answers during aggregation. Therefore, truth discovery methods can be used to automatically capture the user quality and aggregate user-contributed answers via a weighted combination. Despite the fact that truth discovery is an effective tool for answer aggregation, existing work falls short of the protection towards the privacy of participating users. To fill this gap, we propose perturbation-based mechanisms that provide users with privacy guarantees and maintain the accuracy of aggregated answers. We first present a one-layer mechanism, in which all the users adopt the same probability to perturb their answers. Aggregation is then conducted on perturbed answers but the aggregation accuracy could drop accordingly. To improve the utility, a two-layer mechanism is proposed where users are allowed to sample their own probabilities from a hyper distribution. We theoretically compare the one-layer and two-layer mechanisms, and prove that they provide the same privacy guarantee while the two-layer mechanism delivers better utility. This advantage is brought by the fact that the two-layer mechanism can utilize the estimated user quality information from truth discovery to reduce the accuracy loss caused by perturbation, which is confirmed by experimental results on real-world datasets. Experimental results also demonstrate the effectiveness of the proposed two-layer mechanism in privacy protection with tolerable accuracy loss in aggregation.

Truth discovery, with the goal of inferring true information from massive data through aggregating the information from multiple data sources, has attracted significant attention in recent years. It has demonstrated great advantages in real applications since it can automatically learn the reliability degrees of the data sources without supervision and in turn helps to find more reliable information. In many applications, however, the data may arrive in a stream and present various temporal patterns. Unfortunately, there is no existing truth discovery work that can handle such time series data. To tackle this challenge, we propose a novel online truth discovery framework that incorporates the predictions on the time series data into the truth estimation process. By jointly considering the multi-source information and the temporal patterns of the time series data, the proposed framework can improve the accuracy of the truth discovery results as well as the time series prediction. The effectiveness of the proposed framework is validated on both synthetic and real-world datasets.

As an effective way to solicit useful information from the crowd, crowdsourcing has emerged as a popular paradigm to solve challenging tasks. However, the data provided by the participating workers are not always trustworthy. In real world, there may exist malicious workers in crowdsourcing systems who conduct the data poisoning attacks for the purpose of sabotage or financial rewards. Although data aggregation methods such as majority voting are conducted on workers» labels in order to improve data quality, they are vulnerable to such attacks as they treat all the workers equally. In order to capture the variety in the reliability of workers, the Dawid-Skene model, a sophisticated data aggregation method, has been widely adopted in practice. By conducting maximum likelihood estimation (MLE) using the expectation maximization (EM) algorithm, the Dawid-Skene model can jointly estimate each worker»s reliability and conduct weighted aggregation, and thus can tolerate the data poisoning attacks to some degree. However, the Dawid-Skene model still has weakness. In this paper, we study the data poisoning attacks against such crowdsourcing systems with the Dawid-Skene model empowered. We design an intelligent attack mechanism, based on which the attacker can not only achieve maximum attack utility but also disguise the attacking behaviors. Extensive experiments based on real-world crowdsourcing datasets are conducted to verify the desirable properties of the proposed mechanism.

Drug side-effects become a worldwide public health concern, which are the fourth leading cause of death in the United States. Pharmaceutical industry has paid tremendous effort to identify drug side-effects during the drug development. However, it is impossible and impractical to identify all of them. Fortunately, drug side-effects can also be reported on heterogeneous platforms (i.e., data sources), such as FDA Adverse Event Reporting System and various online communities. However, existing supervised and semi-supervised approaches are not practical as annotating labels are expensive in the medical field. In this paper, we propose a novel and effective unsupervised model Sifter to automatically discover drug side-effects. Sifter enhances the estimation on drug side-effects by learning from various online platforms and measuring platform-level and user-level quality simultaneously. In this way, Sifter demonstrates better performance compared with existing approaches in terms of correctly identifying drug side-effects. Experimental results on five real-world datasets show that Sifter can significantly improve the performance of identifying side-effects compared with the state-of-the-art approaches.

Nowadays, increasingly more people are receiving medical diagnoses from healthcare-related question answering platforms as people can get diagnoses quickly and conveniently. However, such diagnoses from non-expert crowdsourcing users are noisy or even wrong due to the lack of medical domain knowledge, which can cause serious consequences. To unleash the power of crowdsourcing on healthcare question answering, it is important to identify trustworthy answers and filter out noisy ones from user-generated data. Truth discovery methods estimate user reliability degrees and infer trustworthy information simultaneously, and thus these methods can be adopted to discover trustworthy diagnoses from crowdsourced answers. However, existing truth discovery methods do not take into account the rich semantic meanings of the answers. In the light of this challenge, we propose a method to automatically capture the semantic meanings of answers, where answers are represented as real-valued vectors in the semantic space. To learn such vector representations from noisy user-generated data, we tightly combine the truth discovery and vector learning processes. In this way, the learned vector representations enable truth discovery method to model the semantic relations among answers, and the information trustworthiness inferred by truth discovery can help the procedure of vector representation learning. To demonstrate the effectiveness of the proposed method, we collect a large-scale real-world dataset that involves 219,527 medical diagnosis questions and 23,657 non-expert users. Experimental results show that the proposed method improves the accuracy of identified trustworthy answers due to the successful consideration of answers' semantic meanings. Further, we demonstrate the fast convergence and good scalability of the proposed method, which makes it practical for real-world applications.

In the age of big data, information for the same entity can be obtained from different sources, which is inevitably conflicting. Therefore, aggregation methods are needed to identify the trustworthy information from such conflicting data. Truth discovery, which improves the aggregation results by estimating source trustworthiness and discovering truths simultaneously, has become an emerging field. Most truth discovery methods assume that sources make their claims independently, which may not be true in practice. As a matter of fact, influences among sources are ubiquitous and the claims made by one source may be influenced by others. Although there is some work that considers source correlation, those methods are designed to handle categorical claims, which is not general enough to represent the complicated real world applications. To tackle these challenges in truth discovery, we propose an unsupervised probabilistic model named IATD. The model takes source correlations as prior for influence derivation. To model influences among sources, we introduce "claim trustworthiness", which fuses the trustworthiness of the source which provides the claim and the trustworthiness of its influencers. Besides, the proposed model can handle different data types using different distributions in the probabilistic model. Experiments on real-world datasets show that IATD model can improve the aggregation performance compared with the state-of-the-art truth discovery approaches. The properties of IATD model are further illustrated using simulated datasets.

Recent years have witnessed an astonishing growth of crowd-contributed data, which has become a powerful information source that covers almost every aspect of our lives. This big treasure trove of information has fundamentally changed the ways in which we learn about our world. Crowdsourcing has attracted considerable attentions with various approaches developed to utilize these enormous crowdsourced data from different perspectives. From the data collection perspective, crowdsourced data can be divided into two types: "passively" crowdsourced data and "actively" crowdsourced data; from task perspective, crowdsourcing research includes information aggregation, budget allocation, worker incentive mechanism, etc. To answer the need of a systematic introduction of the field and comparison of the techniques, we will present an organized picture on crowdsourcing methods in this tutorial. The covered topics will be interested for both advanced researchers and beginners in this field.

The demand for automatic extraction of true information (i.e., truths) from conflicting multi-source data has soared recently. A variety of truth discovery methods have witnessed great successes via jointly estimating source reliability and truths. All existing truth discovery methods focus on providing a point estimator for each object's truth, but in many real-world applications, confidence interval estimation of truths is more desirable, since confidence interval contains richer information. To address this challenge, in this paper, we propose a novel truth discovery method (ETCIBoot) to construct confidence interval estimates as well as identify truths, where the bootstrapping techniques are nicely integrated into the truth discovery procedure. Due to the properties of bootstrapping, the estimators obtained by ETCIBoot are more accurate and robust compared with the state-of-the-art truth discovery approaches. Theoretically, we prove the asymptotical consistency of the confidence interval obtained by ETCIBoot. Experimentally, we demonstrate that ETCIBoot is not only effective in constructing confidence intervals but also able to obtain better truth estimates.

In the past decade, commercial crowdsourcing platforms have revolutionized the ways of classifying and annotating data, especially for large datasets. Obtaining labels for a single instance can be inexpensive, but for large datasets, it is important to allocate budgets wisely. With limited budgets, requesters must trade-off between the quantity of labeled instances and the quality of the final results. Existing budget allocation methods can achieve good quantity but cannot guarantee high quality of individual instances under a tight budget. However, in some scenarios, requesters may be willing to label fewer instances but of higher quality. Moreover, they may have different requirements on quality for different tasks. To address these challenges, we propose a flexible budget allocation framework called Requallo. Requallo allows requesters to set their specific requirements on the labeling quality and maximizes the number of labeled instances that achieve the quality requirement under a tight budget. The budget allocation problem is modeled as a Markov decision process and a sequential labeling policy is produced. The proposed policy greedily searches for the instance to query next as the one that can provide the maximum reward for the goal. The Requallo framework is further extended to consider worker reliability so that the budget can be better allocated. Experiments on two real-world crowdsourcing tasks as well as a simulated task demonstrate that when the budget is tight, the proposed Requallo framework outperforms existing state-of-the-art budget allocation methods from both quantity and quality aspects.

In many applications, one can obtain descriptions about the same objects or events from a variety of sources. As a result, this will inevitably lead to data or information conflicts. One important problem is to identify the true information (i.e., thetruths) among conflicting sources of data. It is intuitive to trust reliable sources more when deriving the truths, but it is usually unknown which one is more reliablea priori. Moreover, each source possesses a variety of properties with different data types. An accurate estimation of source reliability has to be made by modeling multiple properties in a unified model. Existing conflict resolution work either does not conduct source reliability estimation, or models multiple properties separately. In this paper, we propose to resolve conflicts among multiple sources of heterogeneous data types. We model the problem using an optimization framework where truths and source reliability are defined as two sets of unknown variables. The objective is to minimize the overall weighted deviation between the truths and the multi-source observations where each source is weighted by its reliability. Different loss functions can be incorporated into this framework to recognize the characteristics of various data types, and efficient computation approaches are developed. The proposed framework is further adapted to deal with streaming data in an incremental fashion and large-scale data in MapReduce model. Experiments on real-world weather, stock, and flight data as well as simulated multi-source data demonstrate the advantage of jointly modeling different data types in the proposed framework.

In the era of big data, information regarding the same objects can be collected from increasingly more sources. Unfortunately, there usually exist conflicts among the information coming from different sources. To tackle this challenge, truth discovery, i.e., to integrate multi-source noisy information by estimating the reliability of each source, has emerged as a hot topic. In many real world applications, however, the information may come sequentially, and as a consequence, the truth of objects as well as the reliability of sources may be dynamically evolving. Existing truth discovery methods, unfortunately, cannot handle such scenarios. To address this problem, we investigate the temporal relations among both object truths and source reliability, and propose an incremental truth discovery framework that can dynamically update object truths and source weights upon the arrival of new data. Theoretical analysis is provided to show that the proposed method is guaranteed to converge at a fast rate. The experiments on three real world applications and a set of synthetic data demonstrate the advantages of the proposed method over state-of-the-art truth discovery methods.

In crowdsourced data aggregation task, there exist conflicts in the answers provided by large numbers of sources on the same set of questions. The most important challenge for this task is to estimate source reliability and select answers that are provided by high-quality sources. Existing work solves this problem by simultaneously estimating sources' reliability and inferring questions' true answers (i.e., the truths). However, these methods assume that a source has the same reliability degree on all the questions, but ignore the fact that sources' reliability may vary significantly among different topics. To capture various expertise levels on different topics, we propose FaitCrowd, a fine grained truth discovery model for the task of aggregating conflicting data collected from multiple users/sources. FaitCrowd jointly models the process of generating question content and sources' provided answers in a probabilistic model to estimate both topical expertise and true answers simultaneously. This leads to a more precise estimation of source reliability. Therefore, FaitCrowd demonstrates better ability to obtain true answers for the questions compared with existing approaches. Experimental results on two real-world datasets show that FaitCrowd can significantly reduce the error rate of aggregation compared with the state-of-the-art multi-source aggregation approaches due to its ability of learning topical expertise from question content and collected answers.

In the era of Big Data, data entries, even describing the same objects or events, can come from a variety of sources, where a data source can be a web page, a database or a person. Consequently, conflicts among sources become inevitable. To resolve the conflicts and achieve high quality data, truth discovery and crowdsourcing aggregation have been studied intensively. However, although these two topics have a lot in common, they are studied separately and are applied to different domains. To answer the need of a systematic introduction and comparison of the two topics, we present an organized picture on truth discovery and crowdsourcing aggregation in this tutorial. They are compared on both theory and application levels, and their related areas as well as open questions are discussed.

In many applications, one can obtain descriptions about the same objects or events from a variety of sources. As a result, this will inevitably lead to data or information conflicts. One important problem is to identify the true information (i.e., the truths) among conflicting sources of data. It is intuitive to trust reliable sources more when deriving the truths, but it is usually unknown which one is more reliable a priori. Moreover, each source possesses a variety of properties with different data types. An accurate estimation of source reliability has to be made by modeling multiple properties in a unified model. Existing conflict resolution work either does not conduct source reliability estimation, or models multiple properties separately. In this paper, we propose to resolve conflicts among multiple sources of heterogeneous data types. We model the problem using an optimization framework where truths and source reliability are defined as two sets of unknown variables. The objective is to minimize the overall weighted deviation between the truths and the multi-source observations where each source is weighted by its reliability. Different loss functions can be incorporated into this framework to recognize the characteristics of various data types, and efficient computation approaches are developed. Experiments on real-world weather, stock and flight data as well as simulated multi-source data demonstrate the necessity of jointly modeling different data types in the proposed framework.

In many real world applications, the same item may be described by multiple sources. As a consequence, conflicts among these sources are inevitable, which leads to an important task: how to identify which piece of information is trustworthy, i.e., the truth discovery task. Intuitively, if the piece of information is from a reliable source, then it is more trustworthy, and the source that provides trustworthy information is more reliable. Based on this principle, truth discovery approaches have been proposed to infer source reliability degrees and the most trustworthy information (i.e., the truth) simultaneously. However, existing approaches overlook the ubiquitous long-tail phenomenon in the tasks, i.e., most sources only provide a few claims and only a few sources make plenty of claims, which causes the source reliability estimation for small sources to be unreasonable. To tackle this challenge, we propose a confidence-aware truth discovery (CATD) method to automatically detect truths from conflicting data with long-tail phenomenon. The proposed method not only estimates source reliability, but also considers the confidence interval of the estimation, so that it can effectively reflect real source reliability for sources with various levels of participation. Experiments on four real world tasks as well as simulated multi-source long-tail datasets demonstrate that the proposed method outperforms existing state-of-the-art truth discovery approaches by successful discounting the effect of small sources.