## 5 Research Challenges  
Table 3 attempts to summarize the current state of the four elements that make up this paper, the current version of the Web (Web 2.0), Blockchain and the Semantic Web (Web 3.0) and the metaverse (enabled by Web 3.0). Each area of research touched upon by this paper was graded from Not Important (NI), to Important (I) and Very Important (VI). The table offers an overview of what we, the authors of this article, believe are the key challenges to implementing metaverse-specific applications from a research perspective. The question of programming language was thoroughly addressed throughout Sections 3 and 4. The rest of these sections will address the other issues of importance such as performance, architecture, quality of data, and security. \begin{table}
\begin{tabular}{c c} \hline \hline
**Features** & **Description** \\ \hline RDF and OWL Support & Comprehensive support for RDF and OWL ontologies, including parsing, querying, and manipulation. \\ RDF Model & Provides a flexible data model for representing RDF triples and graphs. Supports various serialization formats. \\ SPARQL Query & Powerful query language and engine for querying RDF data using the SPARQL query language. \\ Ontology Support & Supports working with ontologies, including creating, manipulating, and reasoning with OWL ontologies. \\ Reasoning & Offers support for ontology reasoning, including inferencing and consistency checking. \\ Rule-Based Reasoning & Provides support for rule-based reasoning with rule engines like the Jena rules engine. \\ Inferencing & Supports semantic inferencing using rule engines and custom inference rules. \\ RDF Serialization & Supports various RDF serialization formats, such as RDF/XML, Turtle, N-Triples, and JSON-LD. \\ Integration & Integrates well with other Java frameworks and libraries. \\ SPARQL Update & Supports SPARQL Update, allowing updates to RDF data using SPARQL queries. \\ RDF Storage & Supports storing and retrieving RDF data in various storage systems, including in-memory and databases. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Jena features and limitations overview. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Area of Research** & **Web 2.0** & **Blockchain** & **Semantic Web** & **Metaverse** \\ \hline Programming language & NI & I & I & I \\ Performance & I & VI & I & VI \\ Architecture & I & I & VI & I \\ Data quality & I & VI & Vi & I \\ Security & VI & VI & VI & VI \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Research challenges in key areas related to the metaverse. Perhaps one of the most important aspects of any network of computers, of any kind, is the performance of said network. However, while some comparisons exist between Corda and Ethereum, from a feature perspective or a generic approach to smart contracts and blockchain, performance comparisons are scant. One of the more complete performance evaluations comes from [PERSON] et al. [12]. The article compares privately provisioned blockchain platforms to one another. The platforms are deployed in Azure cloud and their performances are compared using various benchmarking tools such as Caliper, Blockbench, or Corda's own enterprise test suite [12]. In terms of performance, Ethereum takes a significant lead in terms of throughput, but does worse in terms of latency [12]. While this article does serve to highlight some of the key performance differences in blockchain processing by several providers, an updated benchmark would be required to come to a more concrete conclusion about performance. Additionally, it is important to note that, in the benchmarks run by [PERSON] et al., all blockchains were run on private networks, and this in itself is one of the key differences between Corda and Ethereum. As [PERSON], [PERSON] and [PERSON] highlight, one of the key differences between Ethereum and Corda is the platform itself [13].