## 6 Use Cases
### Semantic Labeling  
Labeling is the process of creating descriptions of the content of some data. For images or other sensorial data, labels are typically spatio-temporal entities that determine the presence of objects or actions in the reality captured by the sensors [1]. With the emergence of DL, labeling has become a major activity for automakers and providers of electronics. With DL, a sufficiently large and rich dataset containing sensorial data and labels can be used to train models that learn from the dataset and predict labels on previously unseen data. This ability has triggered the creation of many ADAS and AD functions.  
As a consequence, datasets have become a critical asset for players in the automotive market. Labels are frequently defined using function-specific or customized taxonomies and lacking a global or universal hierarchy. Datasets are usually non-compatible and difficult to merge due to semantic inconsistencies between the used terms.  
In this sense, AGO, as a domain ontology, can serve as the core of a data translation function that maps relations between (otherwise non-interoperable) datasets. During the development of the work, all the datasets were represented graphically, and it was verified that the labels of the elements can be mapped to their respective synonyms in the ontology. This translation function requires the definition of advanced search queries. The result may be used to feed training models that need to be adapted for new tests that use different datasets from those used for training. A diagram of the process is presented in Figure 7.  
Figure 7: Using AGO ontology for translating of heterogeneous labels (e.g., from different datasets).  
As an example of the above-mentioned process, Figure 8 depicts a graphical representation of the classes defined in the \"Waymo\" dataset mapped to their equivalent AGO classes. The equivalence among elements is defined by the \"owl:sameAs\" relationship, which means that one can be replaced by the other without altering the meaning and vice versa.  
This type of application allows identifying the relations among different datasets and translating the terms to the needed terminology. The translation can be performed automatically or manually, with user interfaces showing a graph visualization and navigation capabilities.  
Furthermore, the ontology can be part of a knowledge management system and used before the labels are created, at the annotation or labeling stage (see Figure 9). Specifically, the ontology can serve as a database with a formal definition of a unified understanding of the terminology related with the use case. In this manner, the database can be used for generating structure or configuration files that can guide a labeling application or process to produce labels not only in the expected format (e.g., OpenLABEL), but also semantically compatible with the ontology as depicted in Figure 9. Also related with these files, both the structure and the terms defined using natural language can be validated with a content-checker application. Thus, even if there are different users working with the tool or defining several use cases, the terms are chosen from the ontology and remain  
Figure 8: Waymo dataset mapping to AGO example. Matched classes represent the common terms among the different datasets and, thus, the translation can be undertaken by identifying these equivalences.  
the same for every use case. For instance, in the case of web-based annotation tools [25], its functionality could be boosted by including relations among the annotated objects. In addition, the previous annotations and configuration files may be checked according to the vocabulary defined in the ontology, enhancing interoperability by ensuring a common understanding of the domain concepts.