Xin Luna Dong

Integrating conflicting data: the role of source dependence

Many data management applications, such as setting up Web portals, managing enterprise data, managing community data, and sharing scientific data, require integrating data from multiple sources. Each of these sources provides a set of values and different sources can often provide conflicting values. To present quality data to users, it is critical that data integration systems can resolve conflicts and discover true values. Typically, we expect a true value to be provided by more sources than any particular false one, so we can take the value provided by the majority of the sources as the truth. Unfortunately, a false value can be spread through copying and that makes truth discovery extremely tricky. In this paper, we consider how to find true values from conflicting information when there are a large number of sources, among which some may copy from others. We present a novel approach that considers dependence between data sources in truth discovery. Intuitively, if two data sources provide a large number of common values and many of these values are rarely provided by other sources (e.g., particular false values), it is very likely that one copies from the other. We apply Bayesian analysis to decide dependence between sources and design an algorithm that iteratively detects dependence and discovers truth from conflicting information. We also extend our model by considering accuracy of data sources and similarity between values. Our experiments on synthetic data as well as real-world data show that our algorithm can significantly improve accuracy of truth discovery and is scalable when there are a large number of data sources.

Truth discovery and copying detection in a dynamic world

Modern information management applications often require integrating data from a variety of data sources, some of which may copy or buy data from other sources. When these data sources model a dynamically changing world (e.g., peoples contact information changes over time, restaurants open and go out of business), sources often provide out-of-date data. Errors can also creep into data when sources are updated often. Given out-of-date and erroneous data provided by different, possibly dependent, sources, it is challenging for data integration systems to provide the true values. Straightforward ways to resolve such inconsistencies (e.g., voting) may lead to noisy results, often with detrimental consequences. In this paper, we study the problem of finding true values and determining the copying relationship between sources, when the update history of the sources is known. We model the quality of sources over time by their coverage, exactness and freshness. Based on these measures, we conduct a probabilistic analysis. First, we develop a Hidden Markov Model that decides whether a source is a copier of another source and identifies the specific moments at which it copies. Second, we develop a Bayesian model that aggregates information from the sources to decide the true value for a data item, and the evolution of the true values over time. Experimental results on both real-world and synthetic data show high accuracy and scalability of our techniques.

Truth finding on the deep web: is the problem solved?

The amount of useful information available on the Web has been growing at a dramatic pace in recent years and people rely more and more on the Web to fulfill their information needs. In this paper, we study truthfulness of Deep Web data in two domains where we believed data are fairly clean and data quality is important to peoples lives: Stock and Flight. To our surprise, we observed a large amount of inconsistency on data from different sources and also some sources with quite low accuracy. We further applied on these two data sets state-of-the-art data fusion methods that aim at resolving conflicts and finding the truth, analyzed their strengths and limitations, and suggested promising research directions. We wish our study can increase awareness of the seriousness of conflicting data on the Web and in turn inspire more research in our community to tackle this problem.

Data fusion: resolving data conflicts for integration

The amount of information produced in the world increases by 30% every year and this rate will only go up. With advanced network technology, more and more sources are available either over the Internet or in enterprise intranets. Modern data management applications, such as setting up Web portals, managing enterprise data, managing community data, and sharing scientific data, often require integrating available data sources and providing a uniform interface for users to access data from different sources; such requirements have been driving fruitful research on data integration over the last two decades [11, 13].

Global detection of complex copying relationships between sources

Web technologies have enabled data sharing between sources but also simplified copying (and often publishing without proper attribution). The copying relationships can be complex: some sources copy from multiple sources on different subsets of data; some co-copy from the same source, and some transitively copy from another. Understanding such copying relationships is desirable both for business purposes and for improving many key components in data integration, such as resolving conflicts across various sources, reconciling distinct references to the same real-world entity, and efficiently answering queries over multiple sources. Recent works have studied how to detect copying between a pair of sources, but the techniques can fall short in the presence of complex copying relationships. In this paper we describe techniques that discover global copying relationships between a set of structured sources. Towards this goal we make two contributions. First, we propose a global detection algorithm that identifies co-copying and transitive copying, returning only source pairs with direct copying. Second, global detection requires accurate decisions on copying direction; we significantly improve over previous techniques on this by considering various types of evidence for copying and correlation of copying on different data items. Experimental results on real-world data and synthetic data show high effectiveness and efficiency of our techniques.

From data fusion to knowledge fusion

The task of data fusion is to identify the true values of data items (e.g., the true date of birth for Tom Cruise) among multiple observed values drawn from different sources (e.g., Web sites) of varying (and unknown) reliability. A recent survey [20] has provided a detailed comparison of various fusion methods on Deep Web data. In this paper, we study the applicability and limitations of different fusion techniques on a more challenging problem: knowledge fusion. Knowledge fusion identifies true subject-predicate-object triples extracted by multiple information extractors from multiple information sources. These extractors perform the tasks of entity linkage and schema alignment, thus introducing an additional source of noise that is quite different from that traditionally considered in the data fusion literature, which only focuses on factual errors in the original sources. We adapt state-of-the-art data fusion techniques and apply them to a knowledge base with 1.6B unique knowledge triples extracted by 12 extractors from over 1B Web pages, which is three orders of magnitude larger than the data sets used in previous data fusion papers. We show great promise of the data fusion approaches in solving the knowledge fusion problem, and suggest interesting research directions through a detailed error analysis of the methods.

Fusing data with correlations

Many applications rely on Web data and extraction systems to accomplish knowledge-driven tasks. Web information is not curated, so many sources provide inaccurate, or conflicting information. Moreover, extraction systems introduce additional noise to the data. We wish to automatically distinguish correct data and erroneous data for creating a cleaner set of integrated data. Previous work has shown that a naive voting strategy that trusts data provided by the majority or at least a certain number of sources may not work well in the presence of copying between the sources. However, correlation between sources can be much broader than copying: sources may provide data from complementary domains (negative correlation), extractors may focus on different types of information (negative correlation), and extractors may apply common rules in extraction (positive correlation, without copying). In this paper we present novel techniques modeling correlations between sources and applying it in truth finding. We provide a comprehensive evaluation of our approach on three real-world datasets with different characteristics, as well as on synthetic data, showing that our algorithms outperform the existing state-of-the-art techniques.

Incremental record linkage

Record linkage clusters records such that each cluster corresponds to a single distinct real-world entity. It is a crucial step in data cleaning and data integration. In the big data era, the velocity of data updates is often high, quickly making previous linkage results obsolete. This paper presents an end-to-end framework that can incrementally and efficiently update linkage results when data updates arrive. Our algorithms not only allow merging records in the updates with existing clusters, but also allow leveraging new evidence from the updates to fix previous linkage errors. Experimental results on three real and synthetic data sets show that our algorithms can significantly reduce linkage time without sacrificing linkage quality.

Characterizing and selecting fresh data sources

Data integration is a challenging task due to the large numbers of autonomous data sources. This necessitates the development of techniques to reason about the benefits and costs of acquiring and integrating data. Recently the problem of source selection (i.e., identifying the subset of sources that maximizes the profit from integration) was introduced as a preprocessing step before the actual integration. The problem was studied for static sources and used the accuracy of data fusion to quantify the integration profit. In this paper, we study the problem of source selection considering dynamic data sources whose content changes over time. We define a set of time-dependent metrics, including coverage, freshness and accuracy, to characterize the quality of integrated data. We show how statistical models for the evolution of sources can be used to estimate these metrics. While source selection is NP-complete, we show that for a large class of practical cases, near-optimal solutions can be found, propose an algorithmic framework with theoretical guarantees for our problem and show its effectiveness with an extensive experimental evaluation on both real-world and synthetic data.

Record linkage with uniqueness constraints and erroneous values

Many data-management applications require integrating data from a variety of sources, where different sources may refer to the same real-world entity in different ways and some may even provide erroneous data. An important task in this process is to recognize and merge the various references that refer to the same entity. In practice, some attributes satisfy a uniqueness constraint---each real-world entity (or most entities) has a unique value for the attribute (e.g., business contact phone, address, and email). Traditional techniques tackle this case by first linking records that are likely to refer to the same real-world entity, and then fusing the linked records and resolving conflicts if any. Such methods can fall short for three reasons: first, erroneous values from sources may prevent correct linking; second, the real world may contain exceptions to the uniqueness constraints and always enforcing uniqueness can miss correct values; third, locally resolving conflicts for linked records may overlook important global evidence. This paper proposes a novel technique to solve this problem. The key component of our solution is to reduce the problem into a k-partite graph clustering problem and consider in clustering both similarity of attribute values and the sources that associate a pair of values in the same record. Thus, we perform global linkage and fusion simultaneously, and can identify incorrect values and differentiate them from alternative representations of the correct value from the beginning. In addition, we extend our algorithm to be tolerant to a few violations of the uniqueness constraints. Experimental results show accuracy and scalability of our technique.