Maintaining the consistency of large-scale knowledge graphs (KGs) like Wikidata requires both automated methods and human expertise. In this paper, we address the task of recommending users best suited to repair a given inconsistency in a KG. Our approach leverages textual entity abstracts to compute sentence embeddings, which are clustered to identify semantically coherent regions of the KG. We introduce a framework that combines unsupervised clustering with 10-fold evaluation to test user recommendation strategies. Repair histories are linked to users, and test inconsistencies are assigned to clusters using approximate prediction. We evaluate two strategies: (i) frequency-based assignment, which recommends users based on how often they have edited entities in the predicted cluster, and (ii) embedding-based similarity, which compares the test inconsistency to past user-edited items via cosine similarity. Preliminary results show a cluster silhouette ≥ 0.5, membership hit rate of 80%, with the frequency-based approach achieving a Hits@3 of 60%. Our findings suggest that lightweight unsupervised methods can effectively recommend users, showing promise for semi-automated KG maintenance.

Knowledge Base Question Answering systems (KBQA) aim to find answers to natural language questions over a knowledge base. This work presents a template matching approach for Complex KBQA systems (C-KBQA) using the combination of Semantic Parsing and Neural Networks techniques to classify natural language questions into answer templates. An attention mechanism was created to assist a Tree-LSTM in selecting the most important information. The approach was evaluated on the LC-Quad 1, LC-Quad 2, ComplexWebQuestion, and WebQuestionsSP datasets, and the results show that our approach outperforms other approaches on three datasets.

Abstract Every year, new ontology matching approaches have been published to address the heterogeneity problem in ontologies. It is well known that no one is able to stand out from others in all aspects. An ontology meta-matcher combines different alignment techniques to explore various aspects of heterogeneity to avoid the alignment performance being restricted to some ontology characteristics. The meta-matching process consists of several stages of execution, and sometimes the contribution/cost of each algorithm is not clear when evaluating an approach. This article presents the evaluation of solutions commonly used in the literature in order to provide more knowledge about the ontology meta-matching problem. Results showed that the more characteristics of the entities that can be captured by similarity measures set, the greater the accuracy of the model. It was also possible to observe the good performance and accuracy of local search-based meta-heuristics when compared to global optimization meta-heuristics. Experiments with different objective functions have shown that semi-supervised methods can shorten the execution time of the experiment but, on the other hand, bring more instability to the result.

Ontology alignment has impact in how ontologies will be treated by actors in semantic web and stills an unsolved issue. To contribute in this issue, we present an algorithm developed in the GNoSIS system. This solution uses both syntactic and semantic techniques in a structural approach. The algorithm uses distinct resemblance functions and calculates the degree of similarity between the concepts in a recursive manner, calculating the resemblance function result between concepts based on the total similarity degree between concepts with a close kinship. A validation of this approach is presented in this article.

In project teams, having an unique vocabulary and a common understanding about terms is essential to the success of the project. This issue especially disturbs a design project, which has a multidisciplinary team and must consist of people with specific and different knowledge, from diverse domains, to execute special activities. Negotiation arises from this context as a process for the construction of consensus. The goal of this work is to present a model of negotiation to obtain the consensus of meanings, based on models of business negotiation, and consequently, deal with conflicts and the multiplicity of understandings of a concept, making this negotiation a way for creating value for all agents involved.

With the grown availability of large and specialized online ontologies, the questions about the integration of independently developed ontologies have become even more important. To facilitate the ontology integration process, this paper presents an ontology integration support module, that promotes the creation of new ontologies by reusing others. The hypothesis is that the ontology designer achieves a reduction in the time dedicated to create a new ontology, as well as obtain ontologies with better quality. The experimental use of the prototype developed showed evidence that the hypothesis can be confirmed.