The Supervised Semantic Similarity Toolkit

Sousa, Rita; Silva, Sara; Pesquita, Cátia

doi:10.1007/978-3-031-11609-4_8

Cited by 2 publications

(2 citation statements)

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“…We have developed a novel approach 1 [36] to learn the similarity between entities represented in KGs (Definition 1) optimized towards a specific objective similarity. This tailoring is achieved by considering the similarities for different semantic aspects (Definition 2), as opposed to the static SSMs (Definition 5).…”

Section: Methodsmentioning

confidence: 99%

Supervised Biomedical Semantic Similarity

2023

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Semantic similarity between concepts in knowledge graphs is essential for several bioinformatics applications, including the prediction of protein-protein interactions and the discovery of associations between diseases and genes. Although knowledge graphs describe entities in terms of several perspectives (or semantic aspects), state-of-the-art semantic similarity measures are general-purpose. This can represent a challenge since different use cases for the application of semantic similarity may need different similarity perspectives and ultimately depend on expert knowledge for manual fine-tuning. We present a new approach that uses supervised machine learning to tailor aspect-oriented semantic similarity measures to fit a particular view on biological similarity or relatedness. We implement and evaluate it using different combinations of representative semantic similarity measures and machine learning methods with four biological similarity views: protein-protein interaction, protein function similarity, protein sequence similarity and phenotype-based gene similarity. The results demonstrate that our approach outperforms non-supervised methods, producing semantic similarity models that fit different biological perspectives significantly better than the commonly used manual combinations of semantic aspects.INDEX TERMS Semantic similarity, ontology, knowledge graph, supervised learning.• We design a comparative evaluation that includes five KG-based similarity measures based on embeddings or taxonomic semantic similarity and eight ML methods.• We report extensive experimental results demonstrating that our approach can produce a supervised semantic similarity that outperforms static semantic similarity for 21 benchmark biomedical datasets.

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Section: Methodsmentioning

confidence: 99%

Supervised Biomedical Semantic Similarity

2023

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“…Machine learning-based methods for ontology alignment [16] and ontology matching [7] can be applied to automatically map concepts and terms from one ontology or vocabulary to another. These algorithms use techniques such as semantic similarity measures [25], graph-based methods [24], and deep learning models [4,10,11] to identify correspondences between concepts in different ontologies or vocabularies. The goal is to produce a mapping that enables data exchange between systems using different ontologies or vocabularies while preserving the meaning of the data.…”

Section: Ontology and Vocabulary Alignmentmentioning

confidence: 99%

Enhancing Data Space Semantic Interoperability through Machine Learning: a Visionary Perspective

Boukhers

Lange

Beyan

2023

Companion Proceedings of the ACM Web Conference 2023

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Our vision paper outlines a plan to improve the future of semantic interoperability in data spaces through the application of machine learning. The use of data spaces, where data is exchanged among members in a self-regulated environment, is becoming increasingly popular. However, the current manual practices of managing metadata and vocabularies in these spaces are time-consuming, prone to errors, and may not meet the needs of all stakeholders. By leveraging the power of machine learning, we believe that semantic interoperability in data spaces can be significantly improved. This involves automatically generating and updating metadata, which results in a more flexible vocabulary that can accommodate the diverse terminologies used by different sub-communities. Our vision for the future of data spaces addresses the limitations of conventional data exchange and makes data more accessible and valuable for all members of the community. CCS CONCEPTS• Information systems → Data exchange; Data access methods; Semantic web description languages.

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The Supervised Semantic Similarity Toolkit

Cited by 2 publications

References 3 publications

Supervised Biomedical Semantic Similarity

Supervised Biomedical Semantic Similarity

Enhancing Data Space Semantic Interoperability through Machine Learning: a Visionary Perspective

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