**7. Future Work**

The future work for Fuzzy Natural Logic involves expanding the *fundamental trichotomous expressions and LSVs*. For such a task, it will be necessary to either explore computational techniques which can automatically extract the LSV, or to perform a systematisation of surveys in which a significant number of native speakers with linguistic knowledge of their language perform classifications of words. Regardless of the method, the final objective should be the creation of an ontology of the evaluative meaning based on the feature descriptions that this work pointed out.

Figure 9 shows an ontology made *ad hoc* as a guidance for a future design of a computational one. On the top, at the same level, we find both the *Prime Sentiment*, and *Prime semantics*, which will typically share polarities; that is, *vL* will be prototypically negative. At the same time, *vR* will be prototypically positive. From the most abstract notion of prime, we will find the next level, at which we find the *fundamental trichotomous expressions* with their *LSVs*. The closer we ge<sup>t</sup> to the bottom, the more metaphorical evaluations will be found. The prototypical relations are marked with a straight line, while fractured lines denote the borderline relations. The lines will state the semantic distribution between the prime and the related words if they have a relationship to *vL*, *vS*, or *vR*. For example, *Einstein* has a borderline relationship of being a metaphorical evaluation with a semantic polarity of *vR* in the LSV prime of *intelligence*, which means that it will be included in the fuzzy set of the *TE* − *EH* of *intelligent*. The ontology must be able to define evaluative words as a combination of several *fundamental trichotomous expressions*, such as the case of *cute*. Typically, something *cute* is referred for something closer to be *pretty*, but at the same time, it shares traits with something being *quite small*. We would not say that a *big truck* is *cute*, but a *cartooney small toy truck* could be *cute*. Independently of how right is this semantic analysis, the main advantage of such an ontology is that it provides 'explainability', and the particular cases can be changed and improved. Thus, if a semantic relation is inaccurate, it could be fixed. Some words might have a lot of semantic relationships. It is a case of *do-it-yourself*. This lexical unit operates as an evaluation, providing a lot of different borderline meanings.

Creating several ontologies would help linguistics evaluate complexity of the syntax and semantics across languages for future experiments and linguistic applications in explainable AI. That is, computing with constraints how similar the meanings between languages are, elaborate ontologies of the history of language through the evolution of importance, and the evaluation of the linguistic universality in the semantic domain, among others.

**Figure 9.** Ontology of evaluative expressions.

**Author Contributions:** All authors have contributed equally. Conceptualization, A.T.-U., V.N. and M.D.J.-L.; Formal analysis, A.T.-U., V.N. and M.D.J.-L.; Writing—original draft, A.T.-U., V.N. and M.D.J.-L.; Writing—review & editing, A.T.-U., V.N. and M.D.J.-L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This paper has been supported by the project CZ.02.2.69/0.0/0.0/18\_053/0017856 "Strengthening scientific capacities OU II".

**Data Availability Statement:** https://github.com/sfu-discourse-lab/SO-CAL (accessed on 10 January 2022).

**Acknowledgments:** We also want to thank Maite Taboada for her collaboration and support during this research and Eduard Mir Neira for his work with the Spanish Lexicon LSV scales.

**Conflicts of Interest:** The authors declare no conflict of interest.
