Towards Parameter-free Blocking for Scalable Record Linkage

Linking or matching databases is becoming increasingly important in many data mining projects, as linked data can contain information that is not available otherwise, or that would be too expensive to collect. A main challenge when linking large databases is the complexity of the linkage process: potentially each record in one database has to be compared with all records in the other database. Various techniques, collectively know as `blocking', have been developed to deal with this quadratic complexity. Most of these techniques require several parameters to be set by the user in order to achieve good results. In this paper we evaluate six blocking techniques within a common framework with regard to the number and quality of the candidate record pairs generated. We propose a modification to two existing techniques that reduces the variance in the quality of the blocking results over a range of parameter values, enabling more robust, practical record linkage without the need of time consuming manual parameter tuning

Schema-agnostic progressive entity resolution

Entity Resolution (ER) is the task of finding entity profiles that correspond to the same real-world entity. Progressive ER aims to efficiently resolve large datasets when limited time and/or computational resources are available. In practice, its goal is to provide the best possible partial solution by approximating the optimal comparison order of the entity profiles. So far, Progressive ER has only been examined in the context of structured (relational) data sources, as the existing methods rely on schema knowledge to save unnecessary comparisons: they restrict their search space to similar entities with the help of schema-based blocking keys (i.e., signatures that represent the entity profiles). As a result, these solutions are not applicable in Big Data integration applications, which involve large and heterogeneous datasets, such as relational and RDF databases, JSON files, Web corpus etc. To cover this gap, we propose a family of schema-agnostic Progressive ER methods, which do not require schema information, thus applying to heterogeneous data sources of any schema variety. First, we introduce two na\uefve schema-agnostic methods, showing that straightforward solutions exhibit a poor performance that does not scale well to large volumes of data. Then, we propose four different advanced methods. Through an extensive experimental evaluation over 7 real-world, established datasets, we show that all the advanced methods outperform to a significant extent both the na\uefve and the state-of-the-art schema-based ones. We also investigate the relative performance of the advanced methods, providing guidelines on the method selection

Semantic-aware blocking for entity resolution

In this paper, we propose a semantic-aware blocking framework for entity resolution (ER). The proposed framework is built using locality-sensitive hashing (LSH) techniques, which efficiently unifies both textual and semantic features into an ER blocking process. In order to understand how similarity metrics may affect the effectiveness of ER blocking, we study the robustness of similarity metrics and their properties in terms of LSH families. Then, we present how the semantic similarity of records can be captured, measured, and integrated with LSH techniques over multiple similarity spaces. In doing so, the proposed framework can support efficient similarity searches on records in both textual and semantic similarity spaces, yielding ER blocking with improved quality. We have evaluated the proposed framework over two real-world data sets, and compared it with the state-of-the-art blocking techniques. Our experimental study shows that the combination of semantic similarity and textual similarity can considerably improve the quality of blocking. Furthermore, due to the probabilistic nature of LSH, this semantic-aware blocking framework enables us to build fast and reliable blocking for performing entity resolution tasks in a large-scale data environment

A survey on Data Extraction and Data Duplication Detection

Text mining, also known as Intelligent Text Analysis is an important research area. It is very difficult to focus on the most appropriate information due to the high dimensionality of data. Feature Extraction is one of the important techniques in data reduction to discover the most important features. Processing massive amount of data stored in a unstructured form is a challenging task. Several pre-processing methods and algorithms are needed to extract useful features from huge amount of data. Dealing with collection of text documents, it is also very important to filter out duplicate data. Once duplicates are deleted, it is recommended to replace the removed duplicates. This Paper review the literature on duplicate detection and data fusion (remov e and replace duplicates).The survey provides existing text mining techniques to extract relevant features, detect duplicates and to replace the duplicate data to get fine grained knowledge to the user

End-to-End Entity Resolution for Big Data: A Survey

One of the most important tasks for improving data quality and the reliability of data analytics results is Entity Resolution (ER). ER aims to identify different descriptions that refer to the same real-world entity, and remains a challenging problem. While previous works have studied specific aspects of ER (and mostly in traditional settings), in this survey, we provide for the first time an end-to-end view of modern ER workflows, and of the novel aspects of entity indexing and matching methods in order to cope with more than one of the Big Data characteristics simultaneously. We present the basic concepts, processing steps and execution strategies that have been proposed by different communities, i.e., database, semantic Web and machine learning, in order to cope with the loose structuredness, extreme diversity, high speed and large scale of entity descriptions used by real-world applications. Finally, we provide a synthetic discussion of the existing approaches, and conclude with a detailed presentation of open research directions

A Qualitative Literature Review on Linkage Techniques for Data Integration

The data linkage techniques ”entity linking” and ”record linkage” get rising attention as they enable the integration of multiple data sources for data, web, and text mining approaches. This has resulted in the development of numerous algorithms and systems for these techniques in recent years. The goal of this publication is to provide an overview of these numerous data linkage techniques. Most papers deal with record linkage and structured data. Processing unstructured data through entity linking is rising attention with the trend Big Data. Currently, deep learning algorithms are being explored for both linkage techniques. Most publications focus their research on a single process step or the entire process of ”entity linking” or ”record linkage”. However, the papers have the limitation that the used approaches and techniques have always been optimized for only a few data sources

A Taxonomy of Privacy-Preserving Record Linkage Techniques

The process of identifying which records in two or more databases correspond to the same entity is an important aspect of data quality activities such as data pre-processing and data integration. Known as record linkage, data matching or entity resolution, this process has attracted interest from researchers in fields such as databases and data warehousing, data mining, information systems, and machine learning. Record linkage has various challenges, including scalability to large databases, accurate matching and classification, and privacy and confidentiality. The latter challenge arises because commonly personal identifying data, such as names, addresses and dates of birth of individuals, are used in the linkage process. When databases are linked across organizations, the issue of how to protect the privacy and confidentiality of such sensitive information is crucial to successful application of record linkage. In this paper we present an overview of techniques that allow the linking of databases between organizations while at the same time preserving the privacy of these data. Known as 'privacy-preserving record linkage' (PPRL), various such techniques have been developed. We present a taxonomy of PPRL techniques to characterize these techniques along 15 dimensions, and conduct a survey of PPRL techniques. We then highlight shortcomings of current techniques and discuss avenues for future research