Graph-Based Weakly-Supervised Methods for Information Extraction & Integration

The variety and complexity of potentially-related data resources available for querying --- webpages, databases, data warehouses --- has been growing ever more rapidly. There is a growing need to pose integrative queries across multiple such sources, exploiting foreign keys and other means of interlinking data to merge information from diverse sources. This has traditionally been the focus of research within Information Extraction (IE) and Information Integration (II) communities, with IE focusing on converting unstructured sources into structured sources, and II focusing on providing a unified view of diverse structured data sources. However, most of the current IE and II methods, which can potentially be applied to the pro blem of integration across sources, require large amounts of human supervision, often in the form of annotated data. This need for extensive supervision makes existing methods expensive to deploy and difficult to maintain. In this thesis, we develop techniques that generalize from limited human input, via weakly-supervised methods for IE and II. In particular, we argue that graph-based representation of data and learning over such graphs can result in effective and scalable methods for large-scale Information Extraction and Integration. Within IE, we focus on the problem of assigning semantic classes to entities. First we develop a context pattern induction method to extend small initial entity lists of various semantic classes. We also demonstrate that features derived from such extended entity lists can significantly improve performance of state-of-the-art discriminative taggers. The output of pattern-based class-instance extractors is often high-precision and low-recall in nature, which is inadequate for many real world applications. We use Adsorption, a graph based label propagation algorithm, to significantly increase recall of an initial high-precision, low-recall pattern-based extractor by combining evidences from unstructured and structured text corpora. Building on Adsorption, we propose a new label propagation algorithm, Modified Adsorption (MAD), and demonstrate its effectiveness on various real-world datasets. Additionally, we also show how class-instance acquisition performance in the graph-based SSL setting can be improved by incorporating additional semantic constraints available in independently developed knowledge bases. Within Information Integration, we develop a novel system, Q, which draws ideas from machine learning and databases to help a non-expert user construct data-integrating queries based on keywords (across databases) and interactive feedback on answers. We also present an information need-driven strategy for automatically incorporating new sources and their information in Q. We also demonstrate that Q\u27s learning strategy is highly effective in combining the outputs of ``black box\u27\u27 schema matchers and in re-weighting bad alignments. This removes the need to develop an expensive mediated schema which has been necessary for most previous systems

Learning To Scale Up Search-Driven Data Integration

A recent movement to tackle the long-standing data integration problem is a compositional and iterative approach, termed “pay-as-you-go” data integration. Under this model, the objective is to immediately support queries over “partly integrated” data, and to enable the user community to drive integration of the data that relate to their actual information needs. Over time, data will be gradually integrated. While the pay-as-you-go vision has been well-articulated for some time, only recently have we begun to understand how it can be manifested into a system implementation. One branch of this effort has focused on enabling queries through keyword search-driven data integration, in which users pose queries over partly integrated data encoded as a graph, receive ranked answers generated from data and metadata that is linked at query-time, and provide feedback on those answers. From this user feedback, the system learns to repair bad schema matches or record links. Many real world issues of uncertainty and diversity in search-driven integration remain open. Such tasks in search-driven integration require a combination of human guidance and machine learning. The challenge is how to make maximal use of limited human input. This thesis develops three methods to scale up search-driven integration, through learning from expert feedback: (1) active learning techniques to repair links from small amounts of user feedback; (2) collaborative learning techniques to combine users’ conflicting feedback; and (3) debugging techniques to identify where data experts could best improve integration quality. We implement these methods within the Q System, a prototype of search-driven integration, and validate their effectiveness over real-world datasets

Decision making under uncertainty

Almost all important decision problems are inevitably subject to some level of uncertainty either about data measurements, the parameters, or predictions describing future evolution. The significance of handling uncertainty is further amplified by the large volume of uncertain data automatically generated by modern data gathering or integration systems. Various types of problems of decision making under uncertainty have been subject to extensive research in computer science, economics and social science. In this dissertation, I study three major problems in this context, ranking, utility maximization, and matching, all involving uncertain datasets. First, we consider the problem of ranking and top-k query processing over probabilistic datasets. By illustrating the diverse and conflicting behaviors of the prior proposals, we contend that a single, specific ranking function may not suffice for probabilistic datasets. Instead we propose the notion of parameterized ranking functions, that generalize or can approximate many of the previously proposed ranking functions. We present novel exact or approximate algorithms for efficiently ranking large datasets according to these ranking functions, even if the datasets exhibit complex correlations or the probability distributions are continuous. The second problem concerns with the stochastic versions of a broad class of combinatorial optimization problems. We observe that the expected value is inadequate in capturing different types of risk-averse or risk-prone behaviors, and instead we consider a more general objective which is to maximize the expected utility of the solution for some given utility function. We present a polynomial time approximation algorithm with additive error ε for any ε > 0, under certain conditions. Our result generalizes and improves several prior results on stochastic shortest path, stochastic spanning tree, and stochastic knapsack. The third is the stochastic matching problem which finds interesting applications in online dating, kidney exchange and online ad assignment. In this problem, the existence of each edge is uncertain and can be only found out by probing the edge. The goal is to design a probing strategy to maximize the expected weight of the matching. We give linear programming based constant-factor approximation algorithms for weighted stochastic matching, which answer an open question raised in prior work

Web Data Integration for Non-Expert Users

oday, there is an abundance of structured data available on the web in the form of RDF graphs and relational (i.e., tabular) data. This data comes from heterogeneous sources, and realizing its full value requires integrating these sources so that they can be queried together. Due to the scale and heterogeneity of the data sources on the web, integrating them is typically an automatic process. However, automatic data integration approaches are not completely accurate since they infer semantics from syntax in data sources with a high degree of heterogeneity. Therefore, these automatic approaches can be considered as a first step to quickly get reasonable quality data integration output that can be used in issuing queries over the data sources. A second step is refining this output over time while it is being used. Interacting with the data sources through the output of the data integration system and refining this output requires expertise in data management, which limits the scope of this activity to power users and consequently limits the usability of data integration systems. This thesis focuses on helping non-expert users to access heterogeneous data sources through data integration systems, without requiring the users to have prior knowledge of the queried data sources or exposing them to the details of the output of the data integration system. In addition, the users can provide feedback over the answers to their queries, which can then be used to refine and improve the quality of the data integration output. The thesis studies both RDF and relational data. For RDF data, the thesis focuses on helping non-expert users to query heterogeneous RDF data sources, and utilizing their feedback over query answers to improve the quality of the interlinking between these data sources. For relational data, the thesis focuses on improving the quality of the mediated schema for a set of relational data sources and the semantic mappings between these sources based on user feedback over query answers

Advanced distributed data integration infrastructure and research data management portal

The amount of data available due to the rapid spread of advanced information technology is exploding. At the same time, continued research on data integration systems aims to provide users with uniform data access and efficient data sharing. The ability to share data is particularly important for interdisciplinary research, where a comprehensive picture of the subject requires large amounts of data from disparate data sources from a variety of disciplines. While there are numerous data sets available from various groups worldwide, the existing data sources are principally oriented toward regional comparative efforts rather than global applications. They vary widely both in content and format. Such data sources cannot be easily integrated, and maintained by small groups of developers. I propose an advanced infrastructure for large-scale data integration based on crowdsourcing. In particular, I propose a novel architecture and algorithms to efficiently store dynamically incoming heterogeneous datasets enabling both data integration and data autonomy. My proposed infrastructure combines machine learning algorithms and human expertise to perform efficient schema alignment and maintain relationships between the datasets. It provides efficient data exploration functionality without requiring users to write complex queries, as well as performs approximate information fusion when exact match does not exist. Finally, I introduce Col*Fusion system that implements the proposed advance data integration infrastructure

Reducing End-User Burden in Everyday Data Organization.

As digital data permeates every aspect of our daily life, more and more end-users are organizing their everyday data electronically. In fact, end-users are already used to managing their personal data such as contact books and calendars in electronic devices. Meanwhile, the desire for organizing more information into the computer is expanding for a broader group of users. For example, a scientist may need to regularly manage a substantial amount of science data on his desktop. However, to organize such everyday data is challenging for these end-users, because they have limited knowledge about data schema, which is key to data management tasks such as database design, data transformation and data integration. While the user is struggling with these schema tasks, various cognitive and operational burdens emerge. First, when designing her data collection, the user has the burden to abstract her mental model of her real-life data into a reasonable schema design. Moreover, when incorporating external data sources, there is a burden to understand the source semantics and a burden to transform the data from those sources into the user's own data collection. Meanwhile, if the user wants to filter the data, she has the burden to understand and specify the selection condition. Finally, when existing sources are update, there is a burden to understand and fuse these updates. This dissertation introduces various approaches to help the end-user reduce these burdens. To ease the design pain, the dissertation proposes a system with a next-generation spreadsheet for the end-user to easily design and evolve her schema. To facilitate incorporation of external data sources, a sample-driven schema mapping approach is introduced so that the user can freely provide sample instances in her own collection and the system will automatically deduce the desired schema mapping from the sources to the collection. In a similar flavor, this dissertation proposes an approach to facilitate the user in specifying selection conditions via example data points she wants to select. Finally, to help the user incorporate source data updates into her data collection, the dissertation proposes a technique to incrementally update the integrated data using previous integration results.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99778/1/eql_1.pd