Data fusion addresses the problem of consolidating data from disparate information providers into a single unified interface. The different data sources often provide conflicting information for the same data item. Recently, several automated data fusion models have been proposed to resolve conflicts and identify correct data. Although quite effective, these data fusion models do not achieve a close-to-perfect accuracy. We present the demonstration of a system that leverages users as first-class citizens to confirm data conflicts and rapidly improve the effectiveness of fusion. This demonstration is built on solutions proposed in our previous work [1]. To utilize the user judiciously, our system presents claims in an order that is the most beneficial to effectiveness of fusion across data items. We describe ranking algorithms that are built on concepts from information theory and decision theory, and do not need access to ground truth. We describe the user input framework and demonstrate how conflict resolution can be expedited with minimal feedback from the user. We show that: (a) the framework can be easily adopted to existing data fusion models without any internal changes to the models, and (b) the framework can integrate both perfect and imperfect feedback from users.

In domains such as the Web, sensor networks and social media, sources often provide conflicting information for the same data item. Several data fusion techniques have been proposed recently to resolve conflicts and identify correct data. The performance of these fusion systems, while quite accurate, is far from perfect. In this paper, we propose to leverage user feedback for validating data conflicts and rapidly improving the performance of fusion. To present the most beneficial data items for the user to validate, we take advantage of the level of consensus among sources, and the output of fusion to generate an effective ordering of items. We first evaluate data items individually, and then define a novel decision-theoretic framework based on the concept of value of perfect information (VPI) to order items by their ability to boost the performance of fusion. We further derive approximate formulae to scale up the decision-theoretic framework to large-scale data. We empirically evaluate our algorithms on three real-world datasets with different characteristics, and show that the accuracy of fusion can be significantly improved even while requesting feedback on a few data items. We also show that the performance of the proposed methods depends on the characteristics of data, and assess the trade-off between the amount of feedback acquired, and the effectiveness and efficiency of the methods.

We focus on the problem of "tell me more" information related to a given fact in Wikipedia. We use the novel notion of role to link information in an infobox with different places in the text of the same Wikipedia page (space) as well as information across different revisions of the page (time). In this way, it is possible to link together pieces of information that may not represent the same real world entity, yet have served in the same role. To achieve this, we introduce a novel structure called ladder that allows such spatial and temporal linking and we show how to effectively and efficiently construct such structures from Wikipedia data.

The advent of Web 2.0 gave birth to a new kind of application where content is generated through the collaborative contribution of many different users. This form of content generation is believed to generate data of higher quality since the “wisdom of the crowds” makes its way into the data. However, a number of specific data quality issues appear within such collaboratively generated data. Apart from normal updates, there are cases of intentional harmful changes known as vandalism as well as naturally occurring disagreements on topics which don't have an agreed upon viewpoint, known as controversies. While much work has focused on identifying vandalism, there has been little prior work on detecting controversies, especially at a fine granularity. Knowing about controversies when processing user-generated content is essential to understand the quality of the data and the trust that should be given to them. Controversy detection is a challenging task, since in the highly dynamic context of user updates, one needs to differentiate among normal updates, vandalisms and actual controversies. We describe a novel technique that finds these controversial issues by analyzing the edits that have been performed on the data over time. We apply the developed technique on Wikipedia, the world's largest known collaboratively generated database and we show that our approach has higher precision and recall than baseline approaches as well as is capable of finding previously unknown controversies.

Evolving data has attracted considerable research attention. Researchers have focused on modeling and querying of schema/instance-level structural changes, such as, insertion, deletion and modification of attributes. Databases with such a functionality are known as temporal databases. A limitation of the temporal databases is that they treat changes as independent events, while often the appearance (or elimination) of some structure in the database is the result of an evolution of some existing structure. We claim that maintaining the causal relationship between the two structures is of major importance since it allows additional reasoning to be performed and answers to be generated for queries that previously had no answers. We present the TrenDS, a system for exploiting the evolution relationships between the structures in the database. In particular, our system combines different structures that are associated through evolution relationships into virtual structures to be used during query answering. The virtual structures define ``possible'' database instances, in a fashion similar to the possible worlds in the probabilistic databases. TrenDS uses a query answering mechanism that allows queries to be answered over these possible databases without materializing them. Evaluation of such queries raises many technical challenges, since it requires the discovery of Steiner forests on the evolution graphs.

The problem of managing evolving data has attracted considerable research attention. Researchers have focused on the modeling and querying of schema/instance-level structural changes, such as, addition, deletion and modification of attributes. Databases with such a functionality are known as temporal databases. A limitation of the temporal databases is that they treat changes as independent events, while often the appearance (or elimination) of some structure in the database is the result of an evolution of some existing structure. We claim that maintaining the causal relationship between the two structures is of major importance since it allows additional reasoning to be performed and answers to be generated for queries that previously had no answers. We present here a novel framework for exploiting the evolution relationships between the structures in the database. In particular, our system combines different structures that are associated through evolution relationships into virtual structures to be used during query answering. The virtual structures define "possible" database instances, in a fashion similar to the possible worlds in the probabilistic databases. The framework includes a query answering mechanism that allows queries to be answered over these possible databases without materializing them. Evaluation of such queries raises many interesting technical challenges, since it requires the discovery of Steiner forests on the evolution graphs. On this problem we have designed and implemented a new dynamic programming algorithm with exponential complexity in the size of the input query and polynomial complexity in terms of both the attribute and the evolution data sizes.