With the rise of linked data and knowledge graphs, the need becomes compelling to find suitable solutions to increase the coverage and correctness of datasets, to add missing knowledge and to identify and remove errors. Several approaches – mostly relying on machine learning and NLP techniques – have been proposed to address this refinement goal; they usually need a partial gold standard, i.e. some “ground truth” to train automatic models. Gold standards are manually constructed, either by involving domain experts or by adopting crowdsourcing and human computation solutions.
Data linking is rooted in the record linkage problem studied in the databases community since the 1960s @cite_26 ; for this reason, in the Semantic Web community, the term is often used to name the problem of finding equivalent resources on the Web of linked data @cite_8 ; in this meaning, data linking is the process to create links that connect subject- and object-resources from two different datasets through a property that indicate a correspondence or an equivalence (e.g. owl:sameAs ).
@cite_26 @cite_8
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By specifying that published datasets must link to other existing datasets, the 4th linked data principle ensures a Web of data and not just a set of unconnected data islands. The authors propose in this paper the term data linking to name the problem of finding equivalent resources on the Web of linked data. In order to perform data linking, many techniques were developed, finding their roots in statistics, database, natural language processing and graph theory. The authors begin this paper by providing background information and terminological clarifications related to data linking. Then a comprehensive survey over the various techniques available for data linking is provided. These techniques are classified along the three criteria of granularity, type of evidence, and source of the evidence. Finally, the authors survey eleven recent tools performing data linking and we classify them according to the surveyed techniques. The problem of record linkage focuses on determining whether two object descriptions refer to the same underlying entity. Addressing this problem effectively has many practical applications, e.g., elimination of duplicate records in databases and citation matching for scholarly articles. In this paper, we consider a new domain where the record linkage problem is manifested: Internet comparison shopping. We address the resulting linkage setting that requires learning a similarity function between record pairs from streaming data. The learned similarity function is subsequently used in clustering to determine which records are co-referent and should be linked. We present an online machine learning method for addressing this problem, where a composite similarity function based on a linear combination of basis functions is learned incrementally. We illustrate the efficacy of this approach on several real-world datasets from an Internet comparison shopping site, and show that our method is able to effectively learn various distance functions for product data with differing characteristics. We also provide experimental results that show the importance of considering multiple performance measures in record linkage evaluation. Record linkage is the process of matching records from several databases that refer to the same entities. When applied on a single database, this process is known as deduplication. Increasingly, matched data are becoming important in many application areas, because they can contain information that is not available otherwise, or that is too costly to acquire. Removing duplicate records in a single database is a crucial step in the data cleaning process, because duplicates can severely influence the outcomes of any subsequent data processing or data mining. With the increasing size of today's databases, the complexity of the matching process becomes one of the major challenges for record linkage and deduplication. In recent years, various indexing techniques have been developed for record linkage and deduplication. They are aimed at reducing the number of record pairs to be compared in the matching process by removing obvious nonmatching pairs, while at the same time maintaining high matching quality. This paper presents a survey of 12 variations of 6 indexing techniques. Their complexity is analyzed, and their performance and scalability is evaluated within an experimental framework using both synthetic and real data sets. No such detailed survey has so far been published. In the Big Data era, ever-increasing RDF data have reached a scale in billions of entities and brought challenges to the problem of entity linkage on the Semantic Web. Although millions of entities, typically denoted by URIs, have been explicitly linked with owl:sameAs, potentially coreferent ones are still numerous. Existing automatic approaches address this problem mainly from two perspectives: one is via equivalence reasoning, which infers semantically coreferent entities but probably misses many potentials; the other is by similarity computation between property-values of entities, which is not always accurate and do not scale well. In this paper, we introduce a bootstrapping approach by leveraging these two kinds of methods for entity linkage. Given an entity, our approach first infers a set of semantically coreferent entities. Then, it iteratively expands this entity set using discriminative property-value pairs. The discriminability is learned with a statistical measure, which does not only identify important property-values in the entity set, but also takes matched properties into account. Frequent property combinations are also mined to improve linkage accuracy. We develop an online entity linkage search engine, and show its superior precision and recall by comparing with representative approaches on a large-scale and two benchmark datasets.