## 6 Discussion and Future Work  
With the aim of assuring human workers' wellbeing when performing their everyday tasks, this work presents KIDE4I (Knowledge-drIven Dialogue framEwork for Industry), a task-oriented dialogue system framework for industrial scenarios, with ontologies--for domain modelling and dialogue management--as its core. The main characteristics of this system are that (i) it allows a natural communication between workers and industrial assets, reducing the cognitive demand to do so, (ii) it does not need large amounts of training data to be constructed, and (iii) its architecture is generic enough to adapt it to new use cases with a reduced amount of effort. This paper also reports the methodology to adapt KIDE4I for its use with different applications and, to validate it, the adaptation process for 4 different use cases, all relevant in industrial contexts, has been described. This adaptation process shows that other adaptations benefit from the developments carried out for a base use case, especially in the ontology modelling and instantiation phase, in which more than a 90% of the classes and more than a 70% of the instances needed can be reused or obtained automatically. To evaluate and validate KIDE4I, two user studies have been carried out, one for the KIDE4 Guide adaptation (for a guide/logistics robot) and another for the KIDE4 BinPicking adaptation (for a bin-picking robot). The results of these user studies have been reported in terms of qualitative and quantitative evaluation. Qualitative evaluation has been carried out through SASSI, a standardized questionnaire to evaluate spoken dialogue systems. The results on SASSI report very high scores in general (between 5 and 6 out of 6) for all the evaluation aspects considered and have revealed that the system is accurate, fast, useful, efficient, and, most importantly, not demanding cognitively and that users consider that using it would improve their productivity and their security. As for quantitative evaluation, in more than an 80% of the dialogues in both use cases, the interaction goal was fulfilled, and the number of steps necessary to do so is around 3. Also, the dialogues that did not fulfil the interaction goal were analysed and classified according to the type of errors that caused these dialogues to be unsuccessful. For the KIDE4 Guide adaptation, most errors were related to ontology modelling, and for KIDE4 BinPicking, to the syntactic analysis performed in the key element extraction component. Once these errors were detected, they were solved. For the errors depending on third-party tools, such as the ASR technology or the libraries used for the polarity component, other options will be explored as future work in order to reduce the number of errors caused by these modules. Finally, the average time of response has been obtained for both use cases, and it has been observed that the more complex the use case, the more average time it takes to obtain an interpretation, although the differences between complex use cases (KIDE4 Guide) and simpler ones (KIDE4 BinPicking) is practically imperceptible to users (half a second of difference in average). Furthermore, and to wrap up, the quantitative results obtained totally endorse the participants' experience reflected in the questionnaires. Future work includes user studies for the remaining two KIDE4I adaptations and the improvements on functionalities provided by third-party tools mentioned above. In the long run, so as the system is able to learn from new interactions, a component that obtains feedback from users will be developed and implemented. Methodology, C.A., I.F., A.S.; software, C.A. ; validation, C.A. ; formal analysis, C.A., I.F., A.S.; investigation, C.A. ; resources, C.A. ; writing--original draft preparation, C.A. ; writing--review and editing, C.A., I.F., A.S.; supervision, I.F., A.S.; experimental design, C.A., I.F., A.S.; experimentation supervision, C.A. All authors have read and agreed to the published version of the manuscript.