MULDER: Querying the Linked Data Web by Bridging RDF Molecule Templates

Endris, Kemele M.; Galkin, Mikhail; Lytra, Ioanna; Mami, Mohamed Nadjib; Vidal, María-Esther; Auer, Sören

doi:10.1007/978-3-319-64468-4_1

Cited by 18 publications

(6 citation statements)

References 13 publications

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“…In mediation, the response rate is lower than that of the materialization method, but there is no cost for collecting data. The third approach is the virtual method ( Mountantonakis & Tzitzikas, 2020 ), which is federated query processing featuring two categories of SPARQL endpoints ( e.g ., LODatio ( Mountantonakis & Tzitzikas, 2020 )) and LDF clients (linked data fragment) ( Endris et al, 2017 ).…”

Section: Related Workmentioning

confidence: 99%

“…Among the sources of the knowledge graph (KG), source selection methods have been proposed, regardless of its application, in the topic of data integration that is represented in three main approaches: materialization ( Papadaki, Tzitzikas & Spyratos, 2020 ; Papadaki, Spyratos & Tzitzikas, 2021 ), mediation ( Farré, Varga & Almar, 2019 ; Ekaputra et al, 2017 ), and virtual ( Endris et al, 2017 ; Algosaibi, 2021 ). The mediation approach, among these approaches, creates a balance between the cost of storage and time response.…”

Section: Introductionmentioning

confidence: 99%

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Schema and content aware classification for predicting the sources containing an answer overcorpusand knowledge graphs

Asadifar

Kahani

Shekarpour

2022

PeerJ Computer Science

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Today, several attempts to manage question answering (QA) have been made in three separate areas: (1) knowledge-based (KB), (2) text-based and (3) hybrid, which takes advantage of both prior areas in extracting the response. On the other hand, in question answering on a large number of sources, source prediction to ensure scalability is very important. In this paper, a method for source prediction is presented in hybrid QA, involving several KB sources and a text source. In a few hybrid methods for source selection, including only one KB source in addition to the textual source, prioritization or heuristics have been used that have not been evaluated so far. Most methods available in source selection services are based on general metadata or triple instances. These methods are not suitable due to the unstructured source in hybrid QA. In this research, we need data details to predict the source. In addition, unlike KB federated methods that are based on triple instances, we use the behind idea of mediated schema to ensure data integration and scalability. Results from evaluations that consider word, triple, and question level information, show that the proposed approach performs well against a few benchmarks. In addition, the comparison of the proposed method with the existing approaches in hybrid and KB source prediction and also QA tasks has shown a significant reduction in response time and increased accuracy.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Schema and content aware classification for predicting the sources containing an answer overcorpusand knowledge graphs

Asadifar

Kahani

Shekarpour

2022

PeerJ Computer Science

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“…We motivate our work using a real-world use case from the biomedical domain where data sources from clinical records and genomics data have been integrated into an RDF graph. For instance, Fig.1 depicts two RDF subgraphs or RDF molecules [7]. One RDF molecule represents a patient and his/her clinical information provided by source (S1), while the other RDF molecule models the results of liquid biopsy available in a research institute (S2).…”

Section: Motivating Examplementioning

confidence: 99%

“…This ignores the privacy policy enforced which yields no answer for the given query. The MULDER [7] federated query engine generates a bushy plan and decomposes the query by identifying matching RDF Molecule Templates (PRDF-MTs) as a subquery, as shown in Fig.2d. PRDF-MT is a template that represents a set of RDF molecules that share the same RDF type (rdf:type).…”

Section: Motivating Examplementioning

confidence: 99%

BOUNCER: Privacy-Aware Query Processing over Federations of RDF Datasets

Endris

Almhithawi

Lytra

et al. 2018

Lecture Notes in Computer Science

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Data provides the basis for emerging scientific and interdisciplinary data-centric applications with the potential of improving the quality of life for the citizens. However, effective data-centric applications demand data management techniques able to process a large volume of data which may include sensitive data, e.g., financial transactions, medical procedures, or personal data. Managing sensitive data requires the enforcement of privacy and access control regulations, particularly, during the execution of queries against datasets that include sensitive and nonsensitive data. In this paper, we tackle the problem of enforcing privacy regulations during query processing, and propose BOUNCER, a privacyaware query engine over federations of RDF datasets. BOUNCER allows for the description of RDF datasets in terms of RDF molecule templates, i.e., abstract descriptions of the properties of the entities in an RDF dataset and their privacy regulations. Furthermore, BOUNCER implements query decomposition and optimization techniques able to identify query plans over RDF datasets that not only contain the relevant entities to answer a query, but that are also regulated by policies that allow for accessing these relevant entities. We empirically evaluate the effectiveness of the BOUNCER privacy-aware techniques over state-ofthe-art benchmarks of RDF datasets. The observed results suggest that BOUNCER can effectively enforce access control regulations at different granularity without impacting the performance of query processing.

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“…In order to extract knowledge the data should be curated, integrated and semantically described. Recently, the development of Semantic Web technologies has facilitated the implementation of various semantic integration applications, e.g., Karma [5], MINTE [6], SILK [7], and Sieve [8]. The main purpose of Semantic Web Technologies is to describe the meaning of data, a machine readable fashion.…”

Section: Introductionmentioning

confidence: 99%

Data Integration for Supporting Biomedical Knowledge Graph Creation at Large-Scale

Jozashoori,

Novikova,

Vidal

2018

Preprint

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In recent years, following FAIR and open data principles, the number of available big data including biomedical data has been increased exponentially. In order to extract knowledge, these data should be curated, integrated, and semantically described. Accordingly, several semantic integration techniques have been developed; albeit effective, they may suffer from scalability in terms of different properties of big data. Even scaled-up approaches may be highly costly because tasks of semantification, curation and integration are performed independently. In order to overcome these issues, we devise ConMap, a semantic integration approach which exploits knowledge encoded in ontology in order to describe mapping rules to perform these tasks at the same time. Experimental results performed on different data sets suggest that ConMap can significantly reduce the time required for knowledge graph creation by up to 70% of the time that is consumed following a traditional approach.

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MULDER: Querying the Linked Data Web by Bridging RDF Molecule Templates

Cited by 18 publications

References 13 publications

Schema and content aware classification for predicting the sources containing an answer overcorpusand knowledge graphs

Schema and content aware classification for predicting the sources containing an answer overcorpusand knowledge graphs

BOUNCER: Privacy-Aware Query Processing over Federations of RDF Datasets

Data Integration for Supporting Biomedical Knowledge Graph Creation at Large-Scale

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