Explaining Graph Navigational Queries

Fionda, Valeria; Pirrò, Giuseppe

doi:10.1007/978-3-319-58068-5_2

Cited by 3 publications

(2 citation statements)

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“…Anyanwu et al [42], Kochut et al [43] and Fionda et al [44] tackle the problem of association discovering from a query language perspective. This work starts from a specific input pair while their from a declarative specification (i.e., a query) the results of which are unknown before evaluation.…”

Section: Discovering Semantic Associationsmentioning

confidence: 99%

Building relatedness explanations from knowledge graphs

Pirrò

2019

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Knowledge graphs (KGs) are a key ingredient to complement search results, discover entities and their relations and support several knowledge discovery tasks. We face the problem of building relatedness explanations, that is, graphs that can explain how a pair of entities is related in a KG. Explanations can be used in a variety of tasks; from exploratory search to query answering. We formalize the notion of explanation and present two algorithms. The first, E4D (Explanations from Data), assembles explanations starting from all paths interlinking the source and target entity in the data. The second algorithm E4S (Explanations from Schema) builds explanations focused on a specific relatedness perspective expressed by providing a predicate. E4S first generates candidate explanation patterns at the level of schema; then, it assembles explanations by proceeding to their verification in the data. Given a set of paths, found by E4D or E4S, we describe different criteria to build explanations based on information-theory, diversity and their combination. As a concrete use-case of relatedness explanations, we introduce relatedness-based KG querying, which revisits the query-by-example paradigm from the perspective of relatedness explanations. We implemented all machineries in the RECAP tool, which is based on RDF and SPARQL. We discuss an evaluation of the explanation building algorithms and a comparison of RECAP with related systems on real-world data.

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Section: Discovering Semantic Associationsmentioning

confidence: 99%

Building relatedness explanations from knowledge graphs

Pirrò

2019

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“…However, in terms of expressive power, several authors have noted that property paths fall short when trying to express a number of important properties related to navigating RDF documents (cf. [10,11,17,[20][21][22]40]), and that a more powerful form of recursion needs to be added to SPARQL to address this issue. Consequently, this realization has brought forward a good number of extensions of property paths that offer more expressive recursive functionalities (see e.g.…”

Section: Introductionmentioning

confidence: 99%

Recursion in SPARQL

Reutter

Soto

Vrgoč

2021

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The need for recursive queries in the Semantic Web setting is becoming more and more apparent with the emergence of datasets where different pieces of information are connected by complicated patterns. This was acknowledged by the W3C committee by the inclusion of property paths in the SPARQL standard. However, as more data becomes available, it is becoming clear that property paths alone are not enough to capture all recursive queries that the users are interested in, and the literature has already proposed several extensions to allow searching for more complex patterns. We propose a rather different, but simpler approach: add a general purpose recursion operator directly to SPARQL. In this paper we provide a formal syntax and semantics for this proposal, study its theoretical properties, and develop algorithms for evaluating it in practical scenarios. We also show how to implement this extension as a plug-in on top of existing systems, and test its performance on several synthetic and real world datasets, ranging from small graphs, up to the entire Wikidata database.

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