Reproducible Domain-Specific Knowledge Graphs in the Life Sciences: a Systematic Literature Review

Knowledge graphs (KGs) are widely used for representing and organizing structured knowledge in diverse domains. However, the creation and upkeep of KGs pose substantial challenges. Developing a KG demands extensive expertise in data modeling, ontology design, and data curation. Furthermore, KGs are dynamic, requiring continuous updates and quality control to ensure accuracy and relevance. These intricacies contribute to the considerable effort required for their development and maintenance. One critical dimension of KGs that warrants attention is reproducibility. The ability to replicate and validate KGs is fundamental for ensuring the trustworthiness and sustainability of the knowledge they represent. Reproducible KGs not only support open science by allowing others to build upon existing knowledge but also enhance transparency and reliability in disseminating information. Despite the growing number of domain-specific KGs, a comprehensive analysis concerning their reproducibility has been lacking. This paper addresses this gap by offering a general overview of domain-specific KGs and comparing them based on various reproducibility criteria. Our study over 19 different domains shows only eight out of 250 domain-specific KGs (3.2%) provide publicly available source code. Among these, only one system could successfully pass our reproducibility assessment (14.3%). These findings highlight the challenges and gaps in achieving reproducibility across domain-specific KGs. Our finding that only 0.4% of published domain-specific KGs are reproducible shows a clear need for further research and a shift in cultural practices

How and Why is An Answer (Still) Correct? Maintaining Provenance in Dynamic Knowledge Graphs

Knowledge graphs (KGs) have increasingly become the backbone of many critical knowledge-centric applications. Most large-scale KGs used in practice are automatically constructed based on an ensemble of extraction techniques applied over diverse data sources. Therefore, it is important to establish the provenance of results for a query to determine how these were computed. Provenance is shown to be useful for assigning confidence scores to the results, for debugging the KG generation itself, and for providing answer explanations. In many such applications, certain queries are registered as standing queries since their answers are needed often. However, KGs keep continuously changing due to reasons such as changes in the source data, improvements to the extraction techniques, refinement/enrichment of information, and so on. This brings us to the issue of efficiently maintaining the provenance polynomials of complex graph pattern queries for dynamic and large KGs instead of having to recompute them from scratch each time the KG is updated. Addressing these issues, we present HUKA which uses provenance polynomials for tracking the derivation of query results over knowledge graphs by encoding the edges involved in generating the answer. More importantly, HUKA also maintains these provenance polynomials in the face of updates---insertions as well as deletions of facts---to the underlying KG. Experimental results over large real-world KGs such as YAGO and DBpedia with various benchmark SPARQL query workloads reveals that HUKA can be almost 50 times faster than existing systems for provenance computation on dynamic KGs

Provenance-aware knowledge representation: A survey of data models and contextualized knowledge graphs

Expressing machine-interpretable statements in the form of subject-predicate-object triples is a well-established practice for capturing semantics of structured data. However, the standard used for representing these triples, RDF, inherently lacks the mechanism to attach provenance data, which would be crucial to make automatically generated and/or processed data authoritative. This paper is a critical review of data models, annotation frameworks, knowledge organization systems, serialization syntaxes, and algebras that enable provenance-aware RDF statements. The various approaches are assessed in terms of standard compliance, formal semantics, tuple type, vocabulary term usage, blank nodes, provenance granularity, and scalability. This can be used to advance existing solutions and help implementers to select the most suitable approach (or a combination of approaches) for their applications. Moreover, the analysis of the mechanisms and their limitations highlighted in this paper can serve as the basis for novel approaches in RDF-powered applications with increasing provenance needs