## 1 Introduction
To reuse past expertise and follow FAIR principles [14], the EmoKG emotional knowledge graph is mapped to existing knowledge bases found on the BioPortal biomedical ontology catalog such as SNOMEDCT, FMA, RXNORM, MedDRA, and also from emotion ontologies (when available online). We design the LOV4 IoT-Emotion ontology catalog that encourages researchers to apply FAIR principles [15] by releasing online their (emotion) ontologies, datasets, rules, etc. The set of emotion ontology codes shared online can be semi-automatically processed; if the ontology code is not available yet, the scientific publications describing the emotion ontologies are semi-automatically processed with Natural Language Processing (NLP) techniques to feed the emotion reasoning engine and build the naturopathy recommender system. We address the following **research questions (RQ)**:  
* RQ1: Can we reuse the domain expertise, and what are the shortcomings of past ontology-based emotion IoT projects? * RQ2: What are the sensors used in the emotion domain? Are there standardized sensor emotion dictionaries? Is there emotion ontology supported by standards? * RQ3: What are the AI technologies (e.g., rule-based inference engine) to deduce meaningful information from emotion sensor data so developers can design faster IoT-based emotion services? * RQ4: How to prove the veracity of the reasoning engine? The key **contributions** of our research are:  
* C1: Ontology-based emotion projects shared though the LOV4 IoT-Emotion ontology catalog tool (Table 1); it addresses RQ1 in Section 3.3. * C2: The emotion sensor dictionary is aligned with the ETSI SmartM2M SAREF for eHealth Aging Well (SAREFEHAW) standard ontology; it addresses RQ2 in Section 4.2. * C3: Retrieval of emotional knowledge by the reasoner, used in emotion scenarios; it addresses RQ4 in Section 4. * C4: Provenance that keeps track of the knowledge designed by domain experts which is explicitly encoded in the ontology catalog and rule datasets to prove the veracity of the reasoning engine; it addresses RQ4 in Section 3.6. * C5: Standardization-complancy: Lessons learned from semantic interoperability are disseminated within the **ISO/IEC 21823-3 IoT semantic interoperability**[16], and the Alliance for the Internet of Things Innovation (AIOTI) Standardization WG ([[https://aioti.eu/aioti-wg03-reports-on-iot-standards/](https://aioti.eu/aioti-wg03-reports-on-iot-standards/)]([https://aioti.eu/aioti-wg03-reports-on-iot-standards/](https://aioti.eu/aioti-wg03-reports-on-iot-standards/)), accessed on 17 September 2022), which includes the Semantic Interoperability Expert Group [17,18] where the rule-based inference engine is taken as a baseline [19]. SAREF designers are also members of AIOTI Standard WG. AIOTI has other subgroups such as as **AIOTI Health WG** and **AIOTI Urban Living WG**. Furthermore, to implement the emotional knowledge graph, we employ semantic web technologies (RDF, RDFS, OWL, SPARQL), which are **W3C standards**. **Structure of the paper**: Related work on emotion ontologies and IoT-based emotional projects are described in Section 2. Interconnecting interdisciplinary emotional knowledge is described in Section 3, which includes the set of competency questions and the ontology catalog for emotions. The emotional recommender system is described in Section 4, which includes the sensor dictionary for emotion, its reasoner and emotion scenarios. Evaluation is included in Section 5. The paper concludes and envisions future work in Section 6. Figure 1: Overview: Integrating emotional knowledge from heterogeneous communities: Artificial Intelligence (e.g., knowledge engineering), Affective Sciences, Internet of Things (IoT), Psychology, Neurosciences, Physiology, etc.