Benchmarking ’Expressing Without Asserting’ approaches on a large Wikidata dump

Semantic Web technologies provide the means to express the knowledge in a formal and standardized manner, enabling machines to automatically derive meaning from the data. Often this knowledge is uncertain or different degrees of certainty may be assigned to the same statements. This is the case in many fields of study such as in Digital Humanities, Science and Arts. The challenge relies on the fact that our knowledge about the surrounding world is dynamic and may evolve based on new data coming from the latest discoveries. Furthermore we should be able to express conflicting, debated or disputed statements in an efficient, effective and consistent way without the need of asserting them. We call this approach 'Expressing Without Asserting' (EWA). In this work we identify all existing methods that are compatible with actual Semantic Web standards and enable us to express EWA. In our research we were able to prove that existing reification methods such as Named Graphs, Singleton Properties, Wikidata Statements and RDF-Star are the most suitable methods to represent in a reliable way EWA. Next we compare these methods with our own method, namely Conjectures from a quantitative perspective. Our main objective was to put Conjectures into stress tests leveraging enormous datasets created ad hoc using art-related Wikidata dumps and measure the performance in various triplestores in relation with similar concurrent methods. Our experiments show that Conjectures are a formidable tool to express efficiently and effectively EWA. In some cases, Conjectures outperform state of the art methods such as singleton and Rdf-Star exposing their great potential. Is our firm belief that Conjectures represent a suitable solution to EWA issues. Conjectures in their weak form are fully compatible with Semantic Web standards, especially with RDF and SPARQL. Furthermore Conjectures benefit from comprehensive syntax and intuitive semantics that make them easy to learn and adapt.