DECISION SUPPORT SYSTEM USING WEIGHTING SIMILARITY MODEL FOR CONSTRUCTING GROUND-TRUTH DATA SET

This research aims to form a ground-truth dataset in the entity-matching process used to detect duplication of records in a bibliographic database. The contribution of this research is the obtained dataset which can be used as reference in measuring and evaluating the entity matching model implemented in bibliographic databases. This aim was achieved by developing a decision support system through experts who act as decision makers in the bibliographic databases field to construct ground-truth datasets. The model used in this decision support system weights similarity by comparing each attribute of the pairwise record in the dataset. An expert who understands all characteristics of the research database can use the graphical user interface to evaluate and determine the pairwise record that meets the conditions, such as duplication of records. This research produces a ground-truth dataset using the decision support system approach

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

Named Entity Resolution in Personal Knowledge Graphs

Entity Resolution (ER) is the problem of determining when two entities refer to the same underlying entity. The problem has been studied for over 50 years, and most recently, has taken on new importance in an era of large, heterogeneous 'knowledge graphs' published on the Web and used widely in domains as wide ranging as social media, e-commerce and search. This chapter will discuss the specific problem of named ER in the context of personal knowledge graphs (PKGs). We begin with a formal definition of the problem, and the components necessary for doing high-quality and efficient ER. We also discuss some challenges that are expected to arise for Web-scale data. Next, we provide a brief literature review, with a special focus on how existing techniques can potentially apply to PKGs. We conclude the chapter by covering some applications, as well as promising directions for future research.Comment: To appear as a book chapter by the same name in an upcoming (Oct. 2023) book `Personal Knowledge Graphs (PKGs): Methodology, tools and applications' edited by Tiwari et a

When in doubt ask the crowd : leveraging collective intelligence for improving event detection and machine learning

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Embedding Techniques to Solve Large-scale Entity Resolution

Entity resolution (ER) identifies and links records that belong to the same real-world entities, where an entity refer to any real-world object. It is a primary task in data integration. Accurate and efficient ER substantially impacts various commercial, security, and scientific applications. Often, there are no unique identifiers for entities in datasets/databases that would make the ER task easy. Therefore record matching depends on entity identifying attributes and approximate matching techniques. The issues of efficiently handling large-scale data remain an open research problem with the increasing volumes and velocities in modern data collections. Fast, scalable, real-time and approximate entity matching techniques that provide high-quality results are highly demanding. This thesis proposes solutions to address the challenges of lack of test datasets and the demand for fast indexing algorithms in large-scale ER. The shortage of large-scale, real-world datasets with ground truth is a primary concern in developing and testing new ER algorithms. Usually, for many datasets, there is no information on the ground truth or ‘gold standard’ data that specifies if two records correspond to the same entity or not. Moreover, obtaining test data for ER algorithms that use personal identifying keys (e.g., names, addresses) is difficult due to privacy and confidentiality issues. Towards this challenge, we proposed a numerical simulation model that produces realistic large-scale data to test new methods when suitable public datasets are unavailable. One of the important findings of this work is the approximation of vectors that represent entity identification keys and their relationships, e.g., dissimilarities and errors. Indexing techniques reduce the search space and execution time in the ER process. Based on the ideas of the approximate vectors of entity identification keys, we proposed a fast indexing technique (Em-K indexing) suitable for real-time, approximate entity matching in large-scale ER. Our Em-K indexing method provides a quick and accurate block of candidate matches for a querying record by searching an existing reference database. All our solutions are metric-based. We transform metric or non-metric spaces to a lowerdimensional Euclidean space, known as configuration space, using multidimensional scaling (MDS). This thesis discusses how to modify MDS algorithms to solve various ER problems efficiently. We proposed highly efficient and scalable approximation methods that extend the MDS algorithm for large-scale datasets. We empirically demonstrate the improvements of our proposed approaches on several datasets with various parameter settings. The outcomes show that our methods can generate large-scale testing data, perform fast real-time and approximate entity matching, and effectively scale up the mapping capacity of MDS.Thesis (Ph.D.) -- University of Adelaide, School of Mathematical Sciences, 202

Author Gender Metadata Augmentation of HathiTrust Digital Library

ABSTRACT Bibliographic metadata is essential for digital library resource description. Especially as the size and number of bibliographic entities grows, high-quality metadata enables richer forms of digital library access, search, and use. Metadata records can be enriched through automated techniques. For example, a digital humanities scholar might use the gender of a set of authors during their literature analysis. In this study, we undertook to enrich the metadata description of a large-scale digital library, the HathiTrust (HT) digital library, specifically by determining the gender of authors of the public domain portion of the collection. The results are stored to a separate Solr index accessible through the HathiTrust Research Center services. This study, which successfully resolved in 78.9% of the cases the gender of authors in the HT public domain corpus, suggests future research directions in capturing and representing the provenance of the contributing sources to enhance trust, and in machine learning to resolve the remaining names

Evaluation of unique identifiers used as keys to match identical publications in Pure and SciVal:a case study from health science

Unique identifiers (UID) are seen as an effective key to match identical publications across databases or identify duplicates in a database. The objective of the present study is to investigate how well UIDs work as match keys in the integration between Pure and SciVal, based on a case with publications from the health sciences. We evaluate the matching process based on information about coverage, precision, and characteristics of publications matched versus not matched with UIDs as the match keys. We analyze this information to detect errors, if any, in the matching process. As an example we also briefly discuss how publication sets formed by using UIDs as the match keys may affect the bibliometric indicators number of publications, number of citations, and the average number of citations per publication.  The objective is addressed in a literature review and a case study. The literature review shows that only a few studies evaluate how well UIDs work as a match key. From the literature we identify four error types: Duplicate digital object identifiers (DOI), incorrect DOIs in reference lists and databases, DOIs not registered by the database where a bibliometric analysis is performed, and erroneous optical or special character recognition. The case study explores the use of UIDs in the integration between the databases Pure and SciVal. Specifically journal publications in English are matched between the two databases. We find all error types except erroneous optical or special character recognition in our publication sets. In particular the duplicate DOIs constitute a problem for the calculation of bibliometric indicators as both keeping the duplicates to improve the reliability of citation counts and deleting them to improve the reliability of publication counts will distort the calculation of average number of citations per publication. The use of UIDs as a match key in citation linking is implemented in many settings, and the availability of UIDs may become critical for the inclusion of a publication or a database in a bibliometric analysis

Linking historical census data across time

Historical census data provide a snapshot of the era when our ancestors lived. Such data contain valuable information for the reconstruction of households and the tracking of family changes across time, which can be used for a variety of social science research projects. As valuable as they are, these data provide only snapshots of the main characteristics of the stock of a population. To capture household changes requires that we link person by person and household by household from one census to the next over a series of censuses. Once linked together, the census data are greatly enhanced in value. Development of an automatic or semi-automatic linking procedure will significantly relieve social scientists from the tedious task of manually linking individuals, families, and households, and can lead to an improvement of their productivity. In this thesis, a systematic solution is proposed for linking historical census data that integrates data cleaning and standardisation, as well as record and household linkage over consecutive censuses. This solution consists of several data pre-processing, machine learning, and data mining methods that address different aspects of the historical census data linkage problem. A common property of these methods is that they all adopt a strategy to consider a household as an entity, and use the whole of household information to improve the effectiveness of data cleaning and the accuracy of record and household linkage. We first proposal an approach for automatic cleaning and linking using domain knowledge. The core idea is to use household information in both the cleaning and linking steps, so that records that contain errors and variations can be cleaned and standardised and the number of wrongly linked records can be reduced. Second, we introduce a group linking method into household linkage, which enables tracking of the majority of members in a household over a period of time. The proposed method is based on the outcome of the record linkage step using either a similarity based method or a machine learning approach. A group linking method is then applied, aiming to reduce ambiguity of multiple household linkages. Third, we introduce a graph-based method to link households, which takes the structural relationship between household members into consideration. Based on the results of linking individual records, our method builds a graph for each household, so that the matches of household's in different census are determined by both attribute relationship and record similarities. This allows household similarities be more accurately calculated. Finally, we describe an instance classification method based on a multiple instance learning method. This allows an integrated solution to link both households and individual records at the same time. Our method treats group links as bags and individual record links as instances. We extend multiple instance learning from bag to instance classification in order to allow the reconstruction of bags from candidate instances. The classified bag and instance samples lead to a significant reduction in multiple group links, thereby improving the overall quality of linked data

Advanced Entity Resolution Techniques

Entity resolution is the task of determining which records in one or more data sets correspond to the same real-world entities. Entity resolution is an important problem with a range of applications for government agencies, commercial organisations, and research institutions. Due to the important practical applications and many open challenges, entity resolution is an active area of research and a variety of techniques have been developed for each part of the entity resolution process. This thesis is about trying to improve the viability of sophisticated entity resolution techniques for real-world entity resolution problems. Collective entity resolution techniques are a subclass of entity resolution approaches that incorporate relationships into the entity resolution process and introduce dependencies between matching decisions. Group linkage techniques match multiple related records at the same time. Temporal entity resolution techniques incorporate changing attribute values and relationships into the entity resolution process. Population reconstruction techniques match records with different entity roles and very limited information in the presence of domain constraints. Sophisticated entity resolution techniques such as these produce good results when applied to small data sets in an academic environment. However, they suffer from a number of limitations which make them harder to apply to real-world problems. In this thesis, we aim to address several of these limitations with the goal that this will enable such advanced entity resolution techniques to see more use in practical applications. One of the main limitations of existing advanced entity resolution techniques is poor scalability. We propose a novel size-constrained blocking framework, that allows the user to set minimum and maximum block-size thresholds, and then generates blocks where the number of records in each block is within the size range. This allows efficiency requirements to be met, improves parallelisation, and allows expensive techniques with poor scalability such as Markov logic networks to be used. Another significant limitation of advanced entity resolution techniques in practice is a lack of training data. Collective entity resolution techniques make use of relationship information so a bootstrapping process is required in order to generate initial relationships. Many techniques for temporal entity resolution, group linkage and population reconstruction also require training data. In this thesis we propose a novel approach for automatically generating high quality training data using a combination of domain constraints and ambiguity. We also show how we can incorporate these constraints and ambiguity measures into active learning to further improve the training data set. We also address the problem of parameter tuning and evaluation. Advanced entity resolution approaches typically have a large number of parameters that need to be tuned for good performance. We propose a novel approach using transitive closure that eliminates unsound parameter choices in the blocking and similarity calculation steps and reduces the number of iterations of the entity resolution process and the corresponding evaluation. Finally, we present a case study where we extend our training data generation approach for situations where relationships exist between records. We make use of the relationship information to validate the matches generated by our technique, and we also extend the concept of ambiguity to cover groups, allowing us to increase the size of the generated set of matches. We apply this approach to a very complex and challenging data set of population registry data and demonstrate that we can still create high quality training data when other approaches are inadequate