11 research outputs found
Assessing the quality of Wikidata referencing
Wikidata is a versatile and broad-based Knowledge Graph (KG) that leverages the
power of collaborative contributions via an open wiki, augmented by bot accounts,
to curate the content. Wikidata represents over 102 million interlinked data entities,
accompanied by over 1.4 billion statements about the items, accessible to the public
via a SPARQL endpoint and diverse dump formats. The Wikidata data model enables assigning references to every single statement. While the quality of Wikidata
statements has been assessed, the quality of references in this knowledge graph is
not well covered in the literature. To cover the gap, we develop and implement
a comprehensive referencing quality assessment framework based on Linked Data
quality dimensions and criteria. We implement the objective metrics of the assessment framework as the Referencing Quality Scoring System - RQSS. RQSS provides
quantified scores by which the referencing quality can be analyzed and compared.
Due to the scale of Wikidata, we developed a subsetting approach to creating
a comparison platform that systematically samples Wikidata. We have used both
well-defined subsets and random samples to evaluate the quality of references in
Wikidata using RQSS. Based on RQSS, the overall referencing quality in Wikidata
subsets is 0.58 out of 1. Random subsets (representative of Wikidata) have higher
overall scores than topical subsets by 0.05, with Gene Wiki having the highest scores
amongst topical subsets. Regarding referencing quality dimensions, all subsets have
high scores in accuracy, availability, security, and understandability, but have weaker
scores in completeness, verifiability, objectivity, and versatility. RQSS scripts can
be reused to monitor the referencing quality over time. The evaluation shows that
RQSS is practical and provides valuable information, which can be used by Wikidata contributors and WikiProject owners to identify the referencing quality gaps.
Although RQSS is developed based on the Wikidata RDF model, its referencing
quality assessment framework can be generalized to any RDF KG.James Watt Scholarship fundin
RQSS: Referencing quality scoring system for Wikidata
Wikidata is a collaborative multi-purpose Knowledge Graph (KG) with the unique feature of adding provenance data to the statements of items as a reference. More than 73% of Wikidata statements have provenance metadata; however, few studies exist on the referencing quality in this KG, focusing only on the relevancy and trustworthiness of external sources. While there are existing frameworks to assess the quality of Linked Data, and in some aspects their metrics investigate provenance, there are none focused on reference quality. We define a comprehensive referencing quality assessment framework based on Linked Data quality dimensions, such as completeness and understandability. We implement the objective metrics of the assessment framework as the Referencing Quality Scoring System – RQSS. The system provides quantified scores by which the referencing quality can be analyzed and compared. RQSS scripts can also be reused to monitor the referencing quality regularly. Due to the scale of Wikidata, we have used well-defined subsets to evaluate the quality of references in Wikidata using RQSS. We evaluate RQSS over three topical subsets: Gene Wiki, Music, and Ships, corresponding to three Wikidata WikiProjects, along with four random subsets of various sizes. The evaluation shows that RQSS is practical and provides valuable information, which can be used by Wikidata contributors and project holders to identify the quality gaps. Based on RQSS, the average referencing quality in Wikidata subsets is 0.58 out of 1. Random subsets (representative of Wikidata) have higher overall scores than topical subsets by 0.05, with Gene Wiki having the highest scores amongst topical subsets. Regarding referencing quality dimensions, all subsets have high scores in accuracy, availability, security, and understandability, but have weaker scores in completeness, verifiability, objectivity, and versatility. Although RQSS is developed based on the Wikidata RDF model, its referencing quality assessment framework can be applied to KGs in general
Broadcast Complexity and Adaptive Adversaries in Verifiable Secret Sharing
Verifiable secret sharing (VSS) is one of the basic problems in the theory of distributed cryptography and has an important role in secure multiparty computation. In this case, it is tried to share a confidential data as secret, between multiple nodes in a distributed system, in the presence of an active adversary that can destroy some nodes, such that the secret can be reconstructed with the participation of certain size of honest nodes. A dynamic adversary can change its corrupted nodes among the protocol. So far, there is not a formal definition and there are no protocols of dynamic adversaries in VSS context. Also, another important question is, would there exist a protocol to share a secret with a static adversary with at most 1 broadcast round? In this paper, we provide a formal definition of the dynamic adversary. The simulation results prove the efficiency of the proposed protocol in terms of the runtime, the memory usage, and the number of message exchanges. We show that the change period of the dynamic adversary could not happen in less than 4 rounds in order to have a perfectly secure VSS, and then we establish a protocol to deal with this type of adversary. Also, we prove that the lower bound of broadcast complexity for the static adversary is (2,0)-broadcast rounds
Creating, maintaining and updating Shape Expressions as EntitySchemas in the Wikimedia ecosystem
Shape Expressions are formal – machine readable – descriptions of data shapes/schemas. They provide the means to validate expectations by both data and use-case providers. In 2019 the Wikidata introduced the EntitySchema namespace that allows storing Shape Expressions in and Wikibase extensions. Next to Wikidata, this EntitySchema namespace is also available to local wikibase installs and on cloud installation such as wbstack.com. In this tutorial we will shortly introduce Shape Expressions after which we will guide the audience through the EntitySchema namespace in both Wikidata and Wikibase. We will also introduce Wikishape (https://wikishape.weso.es/) as a Shape Expression platform provided by Weso. After this tutorial the participants will be able to write simple Shape Expressions and maintain that on either Wikidata or a local Wikibase