Knowledge graphs have successfully been adopted by academia, governement and industry to represent large scale knowledge bases. Open and collaborative knowledge graphs such as Wikidata capture knowledge from different domains and harmonize them under a common format, making it easier for researchers to access the data while also supporting Open Science.Wikidata keeps getting bigger and better, which subsumes integration use cases. Having a large amount of data such as the one presented in a scopeless Wikidata offers some advantages, e.g., unique access point and common format, but also poses some challenges, e.g., performance.Regular wikidata users are not unfamiliar with running into frequent timeouts of submitted queries. Due to its popularity, limits have been imposed to allow for fair access to many.However this suppreses many interesting and complex queries that require more computational power and resources. Replicating Wikidata on one's own infrastructure can be a solution which also offers a snapshot of the contents of wikidata at some given point in time. There is no need to replicate Wikidata in full, it is possible to work with subsets targeting, for instance, a particular domain. Creating those subsets has emerged as an alternative to reduce the amount and spectrum of data offered by Wikidata. Less data makes more complex queries possible while still keeping the compatibility with the whole Wikidata as the model is kept. In this paper we report the tasks done as part of a Wikidata subsetting project during the Virtual BioHackathon Europe 2020 and SWAT4(HC)LS 2021, which had already started at NBDC/DBCLS BioHackathon 2019 in Japan, SWAT4(HC)LS hackathon 2019, and Virtual COVID-19 BioHackathon 2019. We describe some of approaches we identified to create subsets and some susbsets from the Life Sciences domain as well as other use cases we also discussed.

Knowledge Graphs (KGs) such as Wikidata act as a hub of information from multiple domains and disciplines, and is crowdsourced by multiple stakeholders. The vast amount of available information makes it difficult for researchers to manage the entire KG, which is also continually being edited. It is necessary to develop tools that extract subsets for domains of interest. These subsets will help researchers to reduce costs and time, making data of interest more accessible. In the last two BioHackathons (BH20, BH21), we have created prototypes to extract subsets easily applicable to Wikidata, as well as to define a map of the different approaches used to tackle this problem. Building on those outcomes, we aim to enhance subsetting in both definitions using Entity schemas based on Shape Expressions (ShEx) and extraction algorithms, with a special focus on the biomedical domain. Our first aim is to develop complex subsetting patterns based on qualifiers and references for enhancing credibility of datasets. Our second aim is to establish a faster subsetting extraction platform applying new algorithms based on Apache Spark and new tools like a document-oriented DBMS platform.

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.

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