Type Inference on Noisy RDF Data

Paulheim, Heiko; Bizer, Christian

doi:10.1007/978-3-642-41335-3_32

Cited by 137 publications

(119 citation statements)

References 12 publications

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“…However, datatype inference has been addressed in other contexts, such as (theoretical approaches): XML Schema Definition (XSD) [6,7,14], programming languages [4,5,11,16,31], and OWL [13,19,26,28]. Moreover, we have revised some tools available on the Web for XSD inference 2.…”

Section: Related Workmentioning

confidence: 99%

“…In the context of OWL, the authors in [26] propose a rulebased method to heuristically generate datatype information by exploiting axioms in a knowledge base. They assign Hierarchical structure to infer datatypes.…”

Section: Knowledge-based Approachesmentioning

confidence: 99%

“…However, datatypes, as gYear (e.g., 1999), cannot be determined (since it is identified as an integer). Also, the other theoretical studies in the context of programming languages and OWL have focused on inferring complex datatype through axioms, assigned operations, and inference rules [11,16,26], without considering simple datatypes.…”

Section: Introductionmentioning

confidence: 99%

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RDF-F: RDF Datatype inFerring Framework

2018

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In the context of RDF document matching/integration, the datatype information, which is related to literal objects, is an important aspect to be analyzed in order to better determine similar RDF documents. In this paper, we present an RDF Datatype in Ferring Framework, called RDF-F, which provides two independent datatype inference processes: 1) a fourstep process consisting of (i) a predicate information analysis (i.e., deduce the datatype from existing range property), (ii) an analysis of the object value itself by a pattern-matching process (i.e., recognize the object lexical space), (iii) a semantic analysis of the predicate name and its context, and (iv) generalization of Numeric and Binary datatypes to ensure the integration; and 2) a non-ambiguous lexical-space-matching process, where literal values are inferred by the modification of their representation, following new lexical spaces. We evaluated the performance and the accuracy of both processes with datasets from DBpedia. Results show that the execution time of both indicators is linear and their accuracy can increase up to 97.10 and 99.30%, respectively.

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Section: Related Workmentioning

confidence: 99%

Section: Knowledge-based Approachesmentioning

confidence: 99%

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RDF-F: RDF Datatype inFerring Framework

2018

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“…The work in [29] focuses on heuristically inferring type information in noisy and incorrect RDF data by using statistical distributions. In [28] they predict properties for resources based on a statistical dataset analysis, in particular, in co-occurrence of properties.…”

Section: Related Workmentioning

confidence: 99%

Statistically-driven generation of multidimensional analytical schemas from linked data

Nebot

Berlanga

2016

Knowledge-Based Systems

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The ever-increasing Linked Data (LD) initiative has given place to open, large amounts of semi-structured and rich data published on the Web. However, effective analytical tools that aid the user in his/her analysis and go beyond browsing and querying are still lacking. To address this issue, we propose the automatic generation of multidimensional analytical stars (MDAS). The success of the multidimensional (MD) model for data analysis has been in great part due to its simplicity. Therefore, in this paper we aim at automatically discovering MD conceptual patterns that summarize LD. These patterns resemble the MD star schema typical of relational data warehousing. The underlying foundations of our method is a statistical framework that takes into account both concept and instance data. We present an implementation that makes use of the statistical framework to generate the MDAS. We have performed several experiments that assess and validate the statistical approach with two well-known and large LD sets.

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“…Their method is, however, rather simplistic: it neither utilizes the literal's context, such as the associated property and subject, nor captures the contextual meaning of the relevant words. What has been widely studied is the semantic annotation of KB entities [13,23,28] and of noun phrases outside the KB (e.g., from web tables) [18,9,4]; in such cases, however, the context is very different, and entity typing can, for example, exploit structured information such as the entity's linked Wikipedia page [13] and the domain and range of properties that the entity is associated with [23].…”

Section: Introductionmentioning

confidence: 99%

Canonicalizing Knowledge Base Literals

Chen

Jiménez-Ruiz

Horrocks

2019

Lecture Notes in Computer Science

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Ontology-based knowledge bases (KBs) like DBpedia are very valuable resources, but their usefulness and usability is limited by various quality issues. One such issue is the use of string literals instead of semantically typed entities. In this paper we study the automated canonicalization of such literals, i.e., replacing the literal with an existing entity from the KB or with a new entity that is typed using classes from the KB. We propose a framework that combines both reasoning and machine learning in order to predict the relevant entities and types, and we evaluate this framework against state-of-the-art baselines for both semantic typing and entity matching.

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Type Inference on Noisy RDF Data

Cited by 137 publications

References 12 publications

RDF-F: RDF Datatype inFerring Framework

RDF-F: RDF Datatype inFerring Framework

Statistically-driven generation of multidimensional analytical schemas from linked data

Canonicalizing Knowledge Base Literals

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