Daniel Faria

Marta Contreiras Silva, Daniel Faria, Catia Pesquita

Constructing comprehensive knowledge graphs requires the use of multiple ontologies in order to fully contextualize data into a domain. Ontology matching finds equivalences between concepts interconnecting ontologies and creating a cohesive semantic layer. While the simple pairwise state of the art is well established, simple equivalence mappings cannot provide full semantic integration of related but disjoint ontologies. Complex multi-ontology matching (CMOM) aligns one source entity to composite logical expressions of multiple target entities, establishing more nuanced equivalences and provenance along the ontological hierarchy. We present CMOMgen, the first end-to-end CMOM strategy that generates complete and semantically sound mappings, without establishing any restrictions on the number of target ontologies or entities. Retrieval-Augmented Generation selects relevant classes to compose the mapping and filters matching reference mappings to serve as examples, enhancing In-Context Learning. The strategy was evaluated in three biomedical tasks with partial reference alignments. CMOMgen outperforms baselines in class selection, demonstrating the impact of having a dedicated strategy. Our strategy also achieves a minimum of 63% in F1-score, outperforming all baselines and ablated versions in two out of three tasks and placing second in the third. Furthermore, a manual evaluation of non-reference mappings showed that 46% of the mappings achieve the maximum score, further substantiating its ability to construct semantically sound mappings.

Emanuel Santos, Daniel Faria, Cátia Pesquita et al.

Ontology Matching aims to find a set of semantic correspondences, called an alignment, between related ontologies. In recent years, there has been a growing interest in efficient and effective matching methods for large ontologies. However, most of the alignments produced for large ontologies are logically incoherent. It was only recently that the use of repair techniques to improve the quality of ontology alignments has been explored. In this paper we present a novel technique for detecting incoherent concepts based on ontology modularization, and a new repair algorithm that minimizes the incoherence of the resulting alignment and the number of matches removed from the input alignment. An implementation was done as part of a lightweight version of AgreementMaker system, a successful ontology matching platform, and evaluated using a set of four benchmark biomedical ontology matching tasks. Our results show that our implementation is efficient and produces better alignments with respect to their coherence and f-measure than the state of the art repairing tools. They also show that our implementation is a better alternative for producing coherent silver standard alignments.

Francisco M Couto, Daniel Faria, Bruno Tavares et al.

The increasing use of productivity and impact metrics for evaluation and comparison, not only of individual researchers but also of institutions, universities and even countries, has prompted the development of bibliometrics. Currently, metrics are becoming widely accepted as an easy and balanced way to assist the peer review and evaluation of scientists and/or research units, provided they have adequate precision and recall. This paper presents a benchmarking study of a selected list of representative Portuguese research units, based on a fairly complete set of parameters: bibliometric parameters, number of competitive projects and number of PhDs produced. The study aimed at collecting productivity and impact data from the selected research units in comparable conditions i.e., using objective metrics based on public information, retrievable on-line and/or from official sources and thus verifiable and repeatable. The study has thus focused on the activity of the 2003-06 period, where such data was available from the latest official evaluation. The main advantage of our study was the application of automatic tools, achieving relevant results at a reduced cost. Moreover, the results over the selected units suggest that this kind of analyses will be very useful to benchmark scientific productivity and impact, and assist peer review.