Knowledge Graphs integrate data from multiple, heterogeneous sources, using ontologies to facilitate data interoperability. Ontology development is a resource-consuming task that requires the collaborative work of domain experts and ontology engineers. Therefore, companies invest considerable resources in order to generate and maintain Enterprise Knowledge Graphs and ontologies from large and complex datasets, most of which can be unfamiliar for ontology engineers. In this work, we study the use of Large Language Models to aid in the development of ontologies from datasets, ultimately increasing the automation of the generation of ontology-based Knowledge Graphs. As a result we have developed a structured workflow that leverages Large Language Models to enhance ontology engineering through data pre-processing, ontology planning, building, and entity improvement. Our method is also able to generate mappings and RDF data, but in this work we focus on the ontologies. The pipeline has been implemented in the OntoGenix tool. In this work we show the results of the application of OntoGenix to six datasets related to commercial activities. The findings indicate that the ontologies produced exhibit patterns of coherent modeling, and features that closely resemble those created by humans, although the most complex situations are better reflected by the ontologies developed by humans. • OntoGenix is an LLM-powered pipeline for automating ontology development. • Ontologies developed by OntoGenix are similar to human-generated ontologies for most quality metrics applied. • Ontologies developed by OntoGenix are limited for capturing complex modeling situations. • Our experiments on e-commerce datasets show that OntoGenix modeling is consistent.

Enterprise knowledge graphs combine data from multiple sources, and the structure and granularity of the resulting graphs can vary widely. The data in knowledge graphs is typically structured by schemas, mainly ontologies. Finding the alignments between content from different graphs allows to reduce redundancy, detect inconsistencies, and improve the use of the data. Recently, many graph alignment methods have been proposed, usually tested on general knowledge bases, so there is very little knowledge about the effectiveness of the methods in specific domains. Moreover, we are also interested in studying how these methods capture the semantic similarity between the schemas of the knowledge graphs. In this article, we study both aspects using 25 graph alignment methods on datasets related to e-commerce activities of organizations, such as product sales or customer satisfaction. We have developed a pipeline for generating and evaluating the results. The results show that the alignment methods can be used as a semantic similarity system between the ontology-dataset pairs that generate the knowledge graphs, that AttrE and BootEA are the most effective and robust methods across the different datasets, that the complexity of the structure of the ontology has a clear impact on the effectiveness of the methods, and that the results of the alignment experiments generate information about the similarity of the ontologies and reveal which parts of them should be better modeled to increase the performance of the methods. • Methodological framework for systematic analysis of entity alignment experiments. • Evaluation of 25 methods for supporting semantic similarity analysis. • AttrE and BootEA are the best performing methods based on standardized metrics. • The structure of the ontology influences the results of the graph alignment methods. • Not all class attributes or relations have equal influence on the alignment results.

The adoption of Knowledge Graphs (KGs) by public and private organizations to integrate and publish data has increased in recent years. Ontologies play a crucial role in providing the structure for KGs, but are usually disregarded when designing Application Programming Interfaces (APIs) to enable browsing KGs in a developer-friendly manner. In this paper we provide a systematic review of the state of the art on existing approaches to ease access to ontology-based KG data by application developers. We propose two comparison frameworks to understand specifications, technologies and tools responsible for providing APIs for KGs. Our results reveal several limitations on existing API-based specifications, technologies and tools for KG consumption, which outline exciting research challenges including automatic API generation, API resource path prediction, ontology-based API versioning, and API validation and testing.