Knowledge graph exploration

Lissandrini, Matteo; Pedersen, Torben Bach; Hose, Katja; Mottin, Davide

doi:10.1145/3409481.3409485

Cited by 26 publications

(23 citation statements)

References 37 publications

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“…In our future work, we plan to expand our experiments to comprehensive datasets and combining RPT and PGT into a single hybrid transformation approach. Furthermore, based on our previous work, we plan to investigate how processing advanced queries over provenance-enhanced datasets can be improved [12][13][14]24], how querying heterogeneous provenance-enhanced graph data can be embedded in scalable ecosystems [29], how evolving knowledge graphs can be supported [20], and how interoperability between RDF-star and property graphs can be achieved [28].…”

Section: Discussionmentioning

confidence: 99%

Transforming RDF-star to Property Graphs: A Preliminary Analysis of Transformation Approaches -- extended version

Abuoda¹,

Dell’Aglio²,

Keen³

et al. 2022

Preprint

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RDF and property graph models have many similarities, such as using basic graph concepts like nodes and edges. However, such models differ in their modeling approach, expressivity, serialization, and the nature of applications. RDF is the de-facto standard model for knowledge graphs on the Semantic Web and supported by a rich ecosystem for inference and processing. The property graph model, in contrast, provides advantages in scalable graph analytical tasks, such as graph matching, path analysis, and graph traversal. RDF-star extends RDF and allows capturing metadata as a first-class citizen. To tap on the advantages of alternative models, the literature proposes different ways of transforming knowledge graphs between property graphs and RDF. However, most of these approaches cannot provide complete transformations for RDF-star graphs. Hence, this paper provides a step towards transforming RDF-star graphs into property graphs. In particular, we identify different cases to evaluate transformation approaches from RDF-star to property graphs. Specifically, we categorize two classes of transformation approaches and analyze them based on the test cases. The obtained insights will form the foundation for building complete transformation approaches in the future.

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Section: Discussionmentioning

confidence: 99%

Transforming RDF-star to Property Graphs: A Preliminary Analysis of Transformation Approaches -- extended version

Abuoda¹,

Dell’Aglio²,

Keen³

et al. 2022

Preprint

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“…KG Profiling focuses is on computing frequencies and statistical measures to understand the KG characteristics in terms of the structure and the contents [15]. It can help the initial KG exploratory stages in order to identify whether the dataset can satisfy the current information need or complementary resources are necessary.…”

Section: As Reference 6058mentioning

confidence: 99%

“…WD also has definitions of schemas in the form of shapes (subgraph patterns to describe a concept) using ShEx shape expressions. Some schemas, besides predicates with p: or wdt: prefixes, also uses qualifiers with pq: prefix (see p:710 and its allowed qualifiers in EntitySchema:E84 14 ) and referrers with pr: prefix (see EntitySchema:E43 15 and the pr:P854 specification) to define a well-formed concept.…”

Section: Related Workmentioning

confidence: 99%

Can you trust Wikidata?

Santos,

Schwabe,

Lifschitz

2024

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In order to use a value retrieved from a Knowledge Graph (KG) for some computation, the user should, in principle, ensure that s/he trusts the veracity of the claim, i.e., considers the statement as a fact. Crowd-sourced KGs, or KGs constructed by integrating several different information sources of varying quality, must be used via a trust layer. The veracity of each claim in the underlying KG should be evaluated, considering what is relevant to carrying out some action that motivates the information seeking. The present work aims to assess how well Wikidata (WD) supports the trust decision process implied when using its data. WD provides several mechanisms that can support this trust decision, and our KG Profiling, based on WD claims and schema, elaborates an analysis of how multiple points of view, controversies, and potentially incomplete or incongruent content are presented and represented.

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“…Due to this bottom-up and flexible definition, and to its constant evolution, it can sometimes become challenging for an (external) user to understand the knowledge graph, and to effectively reuse both the ontology and its data [5,16]. The analysis of the content of a KG may have as goals (i) to understand the structure of the KG and its main contents, (ii) to determine whether the KG answers possible questions from the user, and (iii) to find the subset of triples that are pertinent to the user's use case [13]. In this context, it would be useful to exploit the KG's data to derive axioms or constraints as building blocks of a background ontology.…”

Section: Carriero Et Al / Empirical Ontology Design Patterns and Shap...mentioning

confidence: 99%

Empirical ontology design patterns and shapes from Wikidata

Carriero,

Groth,

Presutti

2024

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The ontology underlying the Wikidata knowledge graph (KG) has not been formalized. Instead, its semantics emerges bottom-up from the use of its classes and properties. Flexible guidelines and rules have been defined by the Wikidata project for the use of its ontology, however, it is still often difficult to reuse the ontology’s constructs. Based on the assumption that identifying ontology design patterns from a knowledge graph contributes to making its (possibly) implicit ontology emerge, in this paper we present a method for extracting what we term empirical ontology design patterns (EODPs) from a knowledge graph. This method takes as input a knowledge graph and extracts EODPs as sets of axioms/constraints involving the classes instantiated in the KG. These EODPs include data about the probability of such axioms/constraints happening. We apply our method on two domain-specific portions of Wikidata, addressing the music and art, architecture, and archaeology domains, and we compare the empirical ontology design patterns we extract with the current support present in Wikidata. We show how these patterns can provide guidance for the use of the Wikidata ontology and its potential improvement, and can give insight into the content of (domain-specific portions of) the Wikidata knowledge graph.

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Knowledge graph exploration

Cited by 26 publications

References 37 publications

Transforming RDF-star to Property Graphs: A Preliminary Analysis of Transformation Approaches -- extended version

Transforming RDF-star to Property Graphs: A Preliminary Analysis of Transformation Approaches -- extended version

Can you trust Wikidata?

Empirical ontology design patterns and shapes from Wikidata

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