## 3 Interconnecting Interdisciplinary Emotional Knowledge
### Keeping Track of Provenance Metadata
To enhance clarity, we provide rdfs:label and rdfs:comment to explicitly describe concepts in natural language and even provide the source of the knowledge using the PROV-O ontology (as explained in Section 3.6). To ensure **deferencable URI**, we tested the ontology with **Vapour**[93] to test the availability of the ontology on servers (e.g., deferencable URI). Deferencing Entity URI (without content negotiation) passed (Figure 9). However, we can enhance availability by

Figure 8: Ontology metadata of our emotion ontology under the Protege Ontology Editor. Figure 7: Subset of the emotion ontology (visualization under GraphDB). providing other sources such as HTML, JSON-LD, RDF/XML, and TURTLE. Moreover, [[http://sensormeasurement.appspot.com/ont/m3/emotion.owl](http://sensormeasurement.appspot.com/ont/m3/emotion.owl)]([http://sensormeasurement.appspot.com/ont/m3/emotion.owl](http://sensormeasurement.appspot.com/ont/m3/emotion.owl)) (accessed on 17 September 2022) can be downloaded, but the server configuration must be improved. We are using Google Application Engine to host the application online, with web services running, and obtain the DNS name. ### LOV4 IoT-Emotion Ontology Catalog: Page View Statistics

The LOV4 IoT-Emotion web page has been visited 5511 times (3987 as unique views) between January 2018, and March 2022 according to Google Analytics (Figure 10). Visitors return to this dataset which demonstrates its usefulness. To make the GUI more appealing, there is still a need for front-end developers. The results are encouraging to update the dataset with additional domains and ontologies. **AIOTI IoT Ontology Landscape**: LOV4 IoT is considered with the AIOTI (The Alliance for the Internet of Things Innovation) IoT ontology landscape survey form ([[https://ec.europa.eu/eusurvey/runner/OntologyLandscapeTemplate](https://ec.europa.eu/eusurvey/runner/OntologyLandscapeTemplate)]([https://ec.europa.eu/eusurvey/runner/OntologyLandscapeTemplate](https://ec.europa.eu/eusurvey/runner/OntologyLandscapeTemplate)) accessed on 17 September 2022) that references and classifies ontologies ([[https://aiotic.eu/wp-content/uploads/2022/AIOTI-Ontology-Landscape-Report-R1-Published-1.0.1.pdf](https://aiotic.eu/wp-content/uploads/2022/AIOTI-Ontology-Landscape-Report-R1-Published-1.0.1.pdf)]([https://aiotic.eu/wp-content/uploads/2022/AIOTI-Ontology-Landscape-Report-R1-Published-1.0.1.pdf](https://aiotic.eu/wp-content/uploads/2022/AIOTI-Ontology-Landscape-Report-R1-Published-1.0.1.pdf)) accessed on 17 September 2022), which is led by the Standard WG-Semantic Interoperability Expert Group. The group helps industrial practitioners and non-experts find which ontologies are relevant in a certain domain, where to find them, how to choose the most suitable, and ontology maintenance-related questions. **StandICT.eu 2023 Standardized Ontology Landscape**: Another similar action to collect standardized ontologies is under development within the StandICT.eu 2023 project. A landscape on standardized ontologies should be released before the end of 2022. ### Emotional Knowledge Graph Evaluated with Emotion Scenarios

A subset of emotional-related scenarios has been introduced in Section 4.4. More and more scenarios are integrated. Figure 10: LOV4 IoT Google Analytics per IoT domain (e.g., emotion). Figure 9: Deferencable URI evaluated with Vapour. Rules are implemented and employed within the emotion scenarios (see Table 6 for demonstrator's URLs such as sensor discovery, rule discovery, emotion ontology, etc.). **Technical Evaluation** We summarize 16 rules that encourage ontology best practices in Table 7, so researchers will enhance the reusability and better interoperability of their emotion knowledge [91]. Each rule is associated with bad practices and best practice examples to ease the task of beginners in their learning journey (Step-by-step tutorial to improve the ontology quality, dissemination, reuse, etc.