Sorted Neighborhood for Schema-Free RDF Data

Kejriwal, Mayank; Miranker, Daniel P.

doi:10.1007/978-3-319-25639-9_38

Cited by 5 publications

(6 citation statements)

References 14 publications

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“…Note that the recall metric is computed differently in Table 2. Specifically, the number of true positives in the retrieved 500 duplicates is divided by the quantity min(500, |Ω m |), where |Ω m | is the actual number of matching entities ( Table 1), instead of |Ω m | (as with traditional recall computation 36 ). The table shows that the proposed TSG equals or outperforms the 36 The reason for bounding the denominator in this particular experiment is to prevent the recall from exceeding 100%.…”

Section: Methodsmentioning

confidence: 99%

“…In a preliminary workshop report, we demonstrated that the empirical benefits of relational DNF blocking schemes can also be realized on schema-free RDF data, if the formalism is appropriately adopted [35]. In follow-up work, we showed that the schemes can be used with numerous blocking algorithms, including an adapted version of the classic Sorted Neighborhood algorithm [36]. This article comprehensively develops DNF blocking scheme learning on RDF data, and uses it to propose a novel Set Covering-based learning algorithm with convenient theoretical properties (Section 4.5.1).…”

Section: Supervised Systemsmentioning

confidence: 99%

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An unsupervised instance matcher for schema-free RDF data

Kejriwal

Miranker

2015

Journal of Web Semantics

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Section: Methodsmentioning

confidence: 99%

Section: Supervised Systemsmentioning

confidence: 99%

An unsupervised instance matcher for schema-free RDF data

Kejriwal

Miranker

2015

Journal of Web Semantics

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“…A commonly used schema-agnostic blocking approach is Token-Blocking [34,36,37,51,52] in which individual records are split into bags of all possible tokens (individual words) and grouped by their commonly shared tokens. Figure 4.5 depicts Token-Blocking applied to the same records of Fig.…”

Section: Schema-agnostic Blocking Approachesmentioning

confidence: 99%

“…In [36], the authors note that Sorted Neighbourhood assumes that a sorting key can be applied to all records of a dataset (or datasets) as they all share the same attributes (i.e. schema).…”

Section: Sorted Neighbourhoodmentioning

confidence: 99%

A Review of Unsupervised and Semi-supervised Blocking Methods for Record Linkage

O'Hare¹,

Jurek-Loughrey²,

Campos

2018

Unsupervised and Semi-Supervised Learning

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Record linkage, referred to also as entity resolution, is a process of identifying records representing the same real-world entity (e.g. a person) across varied data sources. To reduce the computational complexity associated with record comparisons, a task referred to as blocking is commonly performed prior to the linkage process. The blocking task involves partitioning records into blocks of records and treating records from different blocks as not related to the same entity. Following this, record linkage methods are applied within each block significantly reducing the number of record comparisons. Most of the existing blocking techniques require some degree of parameter selection in order to optimise the performance for a particular dataset (e.g. attributes and blocking functions used for splitting records into blocks). Optimal parameters can be selected manually but this is expensive in terms of time and cost and assumes a domain expert to be available. Automatic supervised blocking techniques have been proposed; however, they require a set of labelled data in which the matching status of each record is known. In the majority of real-world scenarios, we do not have any information regarding the matching status of records obtained from multiple sources. Therefore, there is a demand for blocking techniques that sufficiently reduce the number of record comparisons with little to no human input or labelled data required. Given the importance of the problem, recent research efforts have seen the development of novel unsupervised and semi-supervised blocking techniques. In this chapter, we review existing blocking techniques and discuss their advantages and disadvantages. We detail other research areas that have recently arose and discuss other unresolved issues that are still to be addressed.

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“…We presented a second algorithm that works along similar lines but uses a different, lowercomplexity, matching algorithm instead [Kejriwal and Miranker 2014]. A second approach to addressing multiple types, which has been gaining traction recently, is to devise schema-free algorithms that do not need to match types before performing instance matching [Rong et al 2012], [Kejriwal and Miranker 2015b].…”

Section: Type Alignmentmentioning

confidence: 99%

Experience: Type alignment on DBpedia and Freebase

Kejriwal,

Miranker

2016

Preprint

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Linked Open Data exhibits growth in both volume and variety of published data. Due to this variety, instances of many different types (e.g. Person) can be found in published datasets. Type alignment is the problem of automatically matching types (in a possibly many-many fashion) between two such datasets. Type alignment is an important preprocessing step in instance matching. Instance matching concerns identifying pairs of instances referring to the same underlying entity. By performing type alignment a priori, only instances conforming to aligned types are processed together, leading to significant savings. This article describes a type alignment experience with two large-scale cross-domain RDF knowledge graphs, DBpedia and Freebase, that contain hundreds, or even thousands, of unique types. Specifically, we present a MapReducebased type alignment algorithm and show that there are at least three reasonable ways of evaluating type alignment within the larger context of instance matching. We comment on the consistency of those results, and note some general observations for researchers evaluating similar algorithms on cross-domain graphs.

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Sorted Neighborhood for Schema-Free RDF Data

Cited by 5 publications

References 14 publications

An unsupervised instance matcher for schema-free RDF data

An unsupervised instance matcher for schema-free RDF data

A Review of Unsupervised and Semi-supervised Blocking Methods for Record Linkage

Experience: Type alignment on DBpedia and Freebase

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