A Collection of Benchmark Data Sets for Knowledge Graph-Based Similarity in the Biomedical Domain

Cardoso, Carlota; Sousa, Rita; Köhler, Sebastian; Pesquita, Cátia

doi:10.1007/978-3-030-62327-2_9

Cited by 4 publications

(8 citation statements)

References 12 publications

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“…CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted February 16, 2021. ; https://doi.org/10.1101/2021.02.16.431402 doi: bioRxiv preprint results are in agreement with conclusions in [55].…”

Section: /23supporting

confidence: 88%

“…The 21 benchmark datasets are presented in [34] and are available online 2 (dated June 2020). These datasets explore four proxy similarities based on protein and gene properties.…”

Section: Methodsmentioning

confidence: 99%

“…The 21 benchmark datasets are presented in [18] and are available online [2] (dated June 2020). These The protein datasets, described in Table 2 shows the similarity approaches employed.…”

Section: Benchmark Datasetsmentioning

confidence: 99%

“…We evaluate the proposed approach in a set of 21 benchmark datasets [34] that have varying sizes with different semantic annotation characteristics and include data from two biomedical ontologies, GO and Human Phenotype Ontology (HPO). These datasets contain four proxies for biomedical entity similarity calculated based on protein sequence similarity, protein function family similarity, protein-protein interactions and phenotype-based gene similarity.…”

Section: Introductionmentioning

confidence: 99%

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Supervised Semantic Similarity

Sousa

Silva

Pesquita

2021

Preprint

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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. Moreover, although black-box machine learning models produce the best results, approaches such as genetic programming and linear regression still produce improved results while generating models that are interpretable.

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Section: /23supporting

confidence: 88%

“…The 21 benchmark datasets are presented in [34] and are available online 2 (dated June 2020). These datasets explore four proxy similarities based on protein and gene properties.…”

Section: Methodsmentioning

confidence: 99%

“…The 21 benchmark datasets are presented in [18] and are available online [2] (dated June 2020). These The protein datasets, described in Table 2 shows the similarity approaches employed.…”

Section: Benchmark Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Supervised Semantic Similarity

Sousa

Silva

Pesquita

2021

Preprint

Self Cite

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“…Then we apply OSR2Vec to generate dense vector representation of ontology as annotations so as for semantic-based tasks. And we evaluate our method on the kgsim dataset [27], a publically available dataset, which provides a collection of benchmark datasets for researchers to use to evaluate the similarity measures of proteins with three different proxies.…”

Section: Introductionmentioning

confidence: 99%

OSR2Vec: an Ontology Semantic Representation Method Based Pre-training Natural Language Model

Liu

Sun

Wang

et al. 2022

Preprint

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Background: Ontology, as a formal description and organization for domain-specific knowledge, is served as a significant approach of information retrieval and biological discovery in biomedical research, attracting more and more attention in computational biomedical and bioinformatics. It is quantified entities using semantic similarity based on ontology terms when using ontologies for description or annotation. More recently, machine learning approaches have enabled ontologies to describe concepts in ”computable” semantics. However, ontology semantics representation is not yet well-explored by existing learning-based methods. Results: We propose OSR2Vec, an ontology semantic representation approach that includes transforming structured ontology information into natural language and applying a pre-trained natural language model to vectorize semantics. OSR2Vec converts directly linked ontology words into sentence sequences, which are then embedded to semantics representation using the ouBioBERT model and the TSDAE method. We evaluate our method on a protein semantic similarities task using the kgsim dataset. We demonstrate the advantages of OSR2Vec by constructing sentence sequence to attain more specific semantics and confirm that performance on the protein semantic similarities task is favorable when using a combination of ouBioBERT and TSDAE according to our method. Conclusions: OSR2Vec is a method to represent ontology semantics using pre-training natural language model embedding sentences. With increasing the quality of representation, OSR2Vec provides an effective method in the ontology-based semantic task.

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Supervised Biomedical Semantic Similarity

2023

Self Cite

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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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A Collection of Benchmark Data Sets for Knowledge Graph-Based Similarity in the Biomedical Domain

Cited by 4 publications

References 12 publications

Supervised Semantic Similarity

Supervised Semantic Similarity

OSR2Vec: an Ontology Semantic Representation Method Based Pre-training Natural Language Model

Supervised Biomedical Semantic Similarity

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