Unsupervised String Transformation Learning for Entity Consolidation

Data integration has been a long-standing challenge in data management with many applications. A key step in data integration is entity consolidation. It takes a collection of clusters of duplicate records as input and produces a single "golden record" for each cluster, which contains the canonical value for each attribute. Truth discovery and data fusion methods, as well as Master Data Management (MDM) systems, can be used for entity consolidation. However, to achieve better results, the variant values (i.e., values that are logically the same with different formats) in the clusters need to be consolidated before applying these methods. For this purpose, we propose a data-driven method to standardize the variant values based on two observations: (1) the variant values usually can be transformed to the same representation (e.g., "Mary Lee" and "Lee, Mary") and (2) the same transformation often appears repeatedly across different clusters (e.g., transpose the first and last name). Our approach first uses an unsupervised method to generate groups of value pairs that can be transformed in the same way (i.e., they share a transformation). Then the groups are presented to a human for verification and the approved ones are used to standardize the data. In a real-world dataset with 17,497 records, our method achieved 75% recall and 99.5% precision in standardizing variant values by asking a human 100 yes/no questions, which completely outperformed a state of the art data wrangling tool

ERBlox: Combining Matching Dependencies with Machine Learning for Entity Resolution

Entity resolution (ER), an important and common data cleaning problem, is about detecting data duplicate representations for the same external entities, and merging them into single representations. Relatively recently, declarative rules called "matching dependencies" (MDs) have been proposed for specifying similarity conditions under which attribute values in database records are merged. In this work we show the process and the benefits of integrating four components of ER: (a) Building a classifier for duplicate/non-duplicate record pairs built using machine learning (ML) techniques; (b) Use of MDs for supporting the blocking phase of ML; (c) Record merging on the basis of the classifier results; and (d) The use of the declarative language "LogiQL" -an extended form of Datalog supported by the "LogicBlox" platform- for all activities related to data processing, and the specification and enforcement of MDs.Comment: Final journal version, with some minor technical corrections. Extended version of arXiv:1508.0601

A new semantic similarity join method using diffusion maps and long string table attributes

With the rapid increase of the distributed data sources, and in order to make information integration, there is a need to combine the information that refers to the same entity from different sources. However, there are no global conventions that control the format of the data, and it is impractical to impose such global conventions. Also, there could be some spelling errors in the data as it is entered manually in most of the cases. For such reasons, the need to find and join similar records instead of exact records is important in order to integrate the data. Most of the previous work has concentrated on similarity join when the join attribute is a short string attribute, such as person name and address. However, most databases contain long string attributes as well, such as product description and paper abstract, and up to our knowledge, no work has been done in this direction. The use of long string attributes is promising as these attributes contain much more information than short string attributes, which could improve the similarity join performance. On the other hand, most of the literature work did not consider the semantic similarities during the similarity join process. To address these issues, 1) we showed that the use of long attributes outperformed the use of short attributes in the similarity join process in terms of similarity join accuracy with a comparable running time under both supervised and unsupervised learning scenarios; 2) we found the best semantic similarity method to join long attributes in both supervised and unsupervised learning scenarios; 3) we proposed efficient semantic similarity join methods using long attributes under both supervised and unsupervised learning scenarios; 4) we proposed privacy preserving similarity join protocols that supports the use of long attributes to increase the similarity join accuracy under both supervised and unsupervised learning scenarios; 5) we studied the effect of using multi-label supervised learning on the similarity join performance; 6) we found an efficient similarity join method for expandable databases

BlogForever D2.6: Data Extraction Methodology

This report outlines an inquiry into the area of web data extraction, conducted within the context of blog preservation. The report reviews theoretical advances and practical developments for implementing data extraction. The inquiry is extended through an experiment that demonstrates the effectiveness and feasibility of implementing some of the suggested approaches. More specifically, the report discusses an approach based on unsupervised machine learning that employs the RSS feeds and HTML representations of blogs. It outlines the possibilities of extracting semantics available in blogs and demonstrates the benefits of exploiting available standards such as microformats and microdata. The report proceeds to propose a methodology for extracting and processing blog data to further inform the design and development of the BlogForever platform

Toward Concept-Based Text Understanding and Mining

There is a huge amount of text information in the world, written in natural languages. Most of the text information is hard to access compared with other well-structured information sources such as relational databases. This is because reading and understanding text requires the ability to disambiguate text fragments at several levels, syntactically and semantically, abstracting away details and using background knowledge in a variety of ways. One possible solution to these problems is to implement a framework of concept-based text understanding and mining, that is, a mechanism of analyzing and integrating segregated information, and a framework of organizing, indexing, accessing textual information centered around real-world concepts. A fundamental difficulty toward this goal is caused by the concept ambiguity of natural language. In text, the real-world entities are referred using their names. The variability in writing a given concept, along with the fact that different concepts/enities may have very similar writings, poses a significant challenge to progress in text understanding and mining. Supporting concept-based natural language understanding requires resolving conceptual ambiguity, and in particular, identifying whether different mentions of real world entities, within and across documents, actually represent the same concept. This thesis systematically studies this fundamental problem. We study and propose different machine learning techniques to address different aspects of this problem and show that as more information can be exploited, the learning techniques developed accordingly, can continuously improve the identification accuracy. In addition, we extend our global probabilistic model to address a significant application -- semantic integration between text and databases

Quality and complexity measures for data linkage and deduplication

Summary. Deduplicating one data set or linking several data sets are increasingly important tasks in the data preparation steps of many data mining projects. The aim of such linkages is to match all records relating to the same entity. Research interest in this area has increased in recent years, with techniques originating from statistics, machine learning, information retrieval, and database research being combined and applied to improve the linkage quality, as well as to increase performance and efficiency when linking or deduplicating very large data sets. Different measures have been used to characterise the quality and complexity of data linkage algorithms, and several new metrics have been proposed. An overview of the issues involved in measuring data linkage and deduplication quality and complexity is presented in this chapter. It is shown that measures in the space of record pair comparisons can produce deceptive quality results. Various measures are discussed and recommendations are given on how to assess data linkage and deduplication quality and complexity. Key words: data or record linkage, data integration and matching, deduplication, data mining pre-processing, quality and complexity measures

Semantic Interaction in Web-based Retrieval Systems : Adopting Semantic Web Technologies and Social Networking Paradigms for Interacting with Semi-structured Web Data

Existing web retrieval models for exploration and interaction with web data do not take into account semantic information, nor do they allow for new forms of interaction by employing meaningful interaction and navigation metaphors in 2D/3D. This thesis researches means for introducing a semantic dimension into the search and exploration process of web content to enable a significantly positive user experience. Therefore, an inherently dynamic view beyond single concepts and models from semantic information processing, information extraction and human-machine interaction is adopted. Essential tasks for semantic interaction such as semantic annotation, semantic mediation and semantic human-computer interaction were identified and elaborated for two general application scenarios in web retrieval: Web-based Question Answering in a knowledge-based dialogue system and semantic exploration of information spaces in 2D/3D