Automatic Key Selection for Data Linking

Achichi, Manel; Ellefi, Mohamed Ben; Symeonidou, Danai; Todorov, Konstantin

doi:10.1007/978-3-319-49004-5_1

Cited by 9 publications

(20 citation statements)

References 20 publications

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“…Several key extraction algorithms have been designed [7,38,47,48] which extract key candidates from RDF datasets and select the most accurate key candidate according to key quality measures. Key extraction algorithms discover either IN-keys (only made of IN-conditions) [47,1,20] or EQ-keys (only made of EQ-conditions) [7], however though they can identify entities from the same dataset, they cannot do it across datasets unless these are using a common ontology or there exists an alignment between their ontologies.…”

Section: Motivationsmentioning

confidence: 99%

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Link key candidate extraction with relational concept analysis

Atencia

Jérôme

Euzenat

et al. 2020

Discrete Applied Mathematics

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Linked data aims at publishing data expressed in RDF (Resource Description Framework) at the scale of the worldwide web. These datasets interoperate by publishing links which identify individuals across heterogeneous datasets. Such links may be found by using a generalisation of keys in databases, called link keys, which apply across datasets. They specify the pairs of properties to compare for linking individuals belonging to different classes of the datasets. Here, we show how to recast the proposed link key extraction techniques for RDF datasets in the framework of formal concept analysis. We define a formal context, where objects are pairs of resources and attributes are pairs of properties, and show that formal concepts correspond to link key candidates. We extend this characterisation to the full RDF model including non functional properties and interdependent link keys. We show how to use relational concept analysis for dealing with cyclic dependencies across classes and hence link keys. Finally, we discuss an implementation of this framework.stating that whenever an instance of the class Livre has the same values for the property auteur as an instance of the class Book has for the property creator and they share at least one value for their properties titre and title, then they denote the same entity. The notion of link key used in this paper generalises the definition introduced in [5] because the body of the rule includes two sets: the set of property pairs for which instances have to share all the values and the set of property pairs for which instances have to share at least one value.Such a link key may depend on other ones. For instance, properties auteur and creator may have values in the Écrivain and Writer classes respectively. Identifying their values will then resort to another link key:{⟨prénom,firstname⟩}{⟨nom,lastname⟩} linkkey ⟨Écrivain,Writer⟩ This situation would be even more intricate if Écrivain and Writer were instead identified from the values of their properties ouvrages and hasWritten refering to instances of Livre and Book (which have a chance to be more accurate). We would then face interdependent link keys.The problem considered here is the extraction of such link keys from RDF data. We have already proposed an algorithm for extracting some types of link keys [5]. This method may be decomposed in two distinct steps:(1) identifying link key candidates, i.e. sets of property pairs that would generate at least one link if used as a link key and that would be maximal for at least one generated link, followed by (2) selecting the best link key candidates according to quality measures. A method for discovering functional link keys in relational databases based on Formal Concept Analysis (FCA [23]) is detailed in [6].Globally extracting a set of link keys across several RDF data sources raises several issues, as link keys differ from database keys in various aspects: (a) they relax two constraints of the relational model, namely, that attributes are functional (RDF properties may have s...

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Section: Motivationsmentioning

confidence: 99%

“…The approaches proposed in [1,20] aim at using a key extraction algorithm [47] to extract pairs of keys that can be used as link specifications. They extract IN-keys that hold in both source and target datasets.…”

Section: Motivationsmentioning

confidence: 99%

Link key candidate extraction with relational concept analysis

Atencia

Jérôme

Euzenat

et al. 2020

Discrete Applied Mathematics

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“…The generated minimal mutual keys can be of large number. Thus in order to minimize the intervention of the oracle in the IM task, RANKey defines a ranking metric to identify the more appropriate mutual keys to be used in the IM mission. This metric is defined as the multiplication of the support scores for a mutual key in both KBs.…”

Section: Context‐free Approachesmentioning

confidence: 99%

“…Unlike RANKey, KeyRanker applies a combining strategy of the complementary keys to improve the results. It discards the keys that generate identity links covered by other keys.…”

Section: Context‐free Approachesmentioning

confidence: 99%

Data linking over RDF knowledge graphs: A survey

Assi

Mcheick

Dhifli³

2020

Concurrency and Computation

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Summary Instance matching (IM) is the process of matching instances across Knowledge Bases (KBs) that refer to the same real‐world object (eg, the same person in two different KBs). Several approaches in the literature were developed to perform this process using different algorithmic techniques and search strategies. In this article, we aim to provide the rationale for IM and to survey the existing algorithms for performing this task. We begin by identifying the importance of such a process and define it formally. We also provide a new classification of these approaches depending on the “source of evidence,” which can be considered as the context information integrated explicitly or implicitly in the IM process. We survey and discuss the state‐of‐the‐art IM methods regarding the context information. We, furthermore, describe and compare different state‐of‐the‐art IM approaches in relation to several criteria. Such a comprehensive comparative study constitutes an asset and a guide for future research in IM.

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“…Then, highly similar instances are grouped together by the help of a standard hierarchical clustering algorithm [11]. An RDF key discovery [12] and a key ranking [9] algorithms are applied on each pair of similar clusters (identified by comparing cluster centroids) across the two graphs, in order to identify the set of properties that best allow to discriminate between the resources contained in each cluster. A new linkset (called "sure links") results from this process and is then compared to the links produced at the matching step (called "candidate links") in order to eliminate errors and increase precision, leading to the production of the final linkset.…”

Section: Data Linkingmentioning

confidence: 99%

DOREMUS: A Graph of Linked Musical Works

Achichi¹,

Lisena²,

Todorov³

et al. 2018

Lecture Notes in Computer Science

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Three major French cultural institutions-the French National Library (BnF), Radio France and the Philharmonie de Paris-have come together in order to develop shared methods to describe semantically their catalogs of music works and events. This process comprises the construction of knowledge graphs representing the data contained in these catalogs following a novel agreed upon ontology that extends CIDOC-CRM and FRBRoo, the linking of these graphs and their open publication on the web. A number of specialized tools that allow for the reproduction of this process are developed, as well as web applications for easy access and navigation through the data. The paper presents one of the main outcomes of this project-the DOREMUS knowledge graph, consisting of three linked datasets describing classical music works and their associated events (e.g., performances in concerts). This resource fills an important gap between library content description and music metadata. We present the DOREMUS pipeline for lifting and linking the data, the tools developed for these purposes, as well as a search application allowing to explore the data.

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Automatic Key Selection for Data Linking

Cited by 9 publications

References 20 publications

Link key candidate extraction with relational concept analysis

Link key candidate extraction with relational concept analysis

Data linking over RDF knowledge graphs: A survey

DOREMUS: A Graph of Linked Musical Works

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