Extending SPARQL with Similarity Joins

Ferrada, Sebastián; Bustos, Benjamín; Hogan, Aidan

doi:10.1007/978-3-030-62419-4_12

Cited by 4 publications

(6 citation statements)

References 24 publications

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“…Extending the performance experimentation presented in our previous work [9], we now present similar experiments using real SPARQL queries. We use a truthy version of the Wikidata RDF dump.…”

Section: Wikidata: K-nn Self-similarity Queriesmentioning

confidence: 92%

“…Contributions This paper is an extension of our previous work, where we proposed, defined, implemented and evaluated novel types of similarity joins in SPARQL [9]. In this extended version, we discuss new use-case queries over different RDF data, and we expand the evaluation of the similarity join implementations by building a benchmark based on the query logs of Wikidata.…”

Section: Ferrada Et Al / Similarity Joins and Clustering For Sparqlmentioning

confidence: 99%

“…We use a truthy version of the Wikidata RDF dump. 9 We start with SPARQL queries from the Wikidata query logs, and apply several filters: first, at least one numeric attribute must be projected in the SELECT clause; second, these numeric attributes must not be obtained in an OPTIONAL clause; third, they produce at least 8 solution mappings; and fourth, they are executed in less than 3 hours using Jena. We obtain 2,726 SPARQL queries.…”

Section: Wikidata: K-nn Self-similarity Queriesmentioning

confidence: 99%

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Similarity joins and clustering for SPARQL

Ferrada,

Bustos,

Hogan

2024

Self Cite

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The SPARQL standard provides operators to retrieve exact matches on data, such as graph patterns, filters and grouping. This work proposes and evaluates two new algebraic operators for SPARQL 1.1 that return similarity-based results instead of exact results. First, a similarity join operator is presented, which brings together similar mappings from two sets of solution mappings. Second, a clustering solution modifier is introduced, which instead of grouping solution mappings according to exact values, brings them together by using similarity criteria. For both cases, a variety of algorithms are proposed and analysed, and use-case queries that showcase the relevance and usefulness of the novel operators are presented. For similarity joins, experimental results are provided by comparing different physical operators over a set of real world queries, as well as comparing our implementation to the closest work found in the literature, DBSimJoin, a PostgreSQL extension that supports similarity joins. For clustering, synthetic queries are designed in order to measure the performance of the different algorithms implemented.

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Section: Wikidata: K-nn Self-similarity Queriesmentioning

confidence: 92%

Section: Ferrada Et Al / Similarity Joins and Clustering For Sparqlmentioning

confidence: 99%

Section: Wikidata: K-nn Self-similarity Queriesmentioning

confidence: 99%

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Similarity joins and clustering for SPARQL

Ferrada,

Bustos,

Hogan

2024

Self Cite

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“…where 𝐻 𝑉 𝐿 𝑉 ,𝑘 ∈ R 2×𝑢 ×𝑑 𝑣 is the visual representation of object 𝑜 𝑘 in 𝐻 𝑉 𝐿 𝑉 from equation (7), and H𝑉 𝐿 𝑉 ,𝑘 is a 𝑑 𝑣 -dimensional embedding, which used as the RGB-D visual feature of the object 𝑜 𝑘 .…”

Section: Attribute-aware Textual Encodermentioning

confidence: 99%

“…Relation extraction (RE) plays a critical role in constructing knowledge graphs, and with the abundance of multimedia data resources, such as web images, many researchers have explored the ability to extract entities and predict their relations from multimodal data [20,22,34]. This approach has become increasingly relevant in building or populating knowledge graphs, especially multimodal knowledge graphs [8,13,25], which have proven useful in downstream tasks, including question answering [7,17], recommendation [16,28], and reasoning [12,35]. Recently, Zheng et al [34] introduces the concept of multimodal relation extraction (MRE), which identifies textual relations using visual clues.…”

Section: Introductionmentioning

confidence: 99%

MORE: A Multimodal Object-Entity Relation Extraction Dataset with a Benchmark Evaluation

He,

Wang,

Cao

et al. 2023

Proceedings of the 31st ACM International Conference on Multimedia

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Extracting relational facts from multimodal data is a crucial task in the field of multimedia and knowledge graphs that feeds into widespread real-world applications. The emphasis of recent studies centers on recognizing relational facts in which both entities are present in one modality and supplementary information is used from other modalities. However, such works disregard a substantial amount of multimodal relational facts that arise across different modalities, such as one entity seen in a text and another in an image. In this paper, we propose a new task, namely Multimodal Object-Entity Relation Extraction, which aims to extract "object-entity" relational facts from image and text data. To facilitate research on this task, we introduce MORE, a new dataset comprising 21 relation types and 20,264 multimodal relational facts annotated on 3,559 pairs of textual news titles and corresponding images. To show the challenges of Multimodal Object-Entity Relation Extraction, we evaluated recent state-of-the-art methods for multimodal relation extraction and conducted a comprehensive experimentation analysis on MORE. Our results demonstrate significant challenges for existing methods, underlining the need for further research on this task. Based on our experiments, we identify several promising directions for future research. The MORE dataset and code are available at https://github.com/NJUNLP/MORE.

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Querying knowledge graphs through positive and negative examples and feedback

Akritidis,

Tzitzikas

2024

J Intell Inf Syst

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Extending SPARQL with Similarity Joins

Cited by 4 publications

References 24 publications

Similarity joins and clustering for SPARQL

Similarity joins and clustering for SPARQL

MORE: A Multimodal Object-Entity Relation Extraction Dataset with a Benchmark Evaluation

Querying knowledge graphs through positive and negative examples and feedback

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