A modern semantic similarity method using multiple resources for enhancing influenza detection

Review Biochemical Research Methods

Semantic similarity and machine learning with ontologies

Maxat Kulmanov et al.

Summary: Ontologies have been widely used in the life sciences to formalize domain knowledge. Recently, there has been an increasing trend in using ontologies to provide background knowledge in similarity-based analysis and machine learning models. The methods of combining ontologies with machine learning are still novel and actively being developed.

BRIEFINGS IN BIOINFORMATICS (2021)

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Article Multidisciplinary Sciences

Improving the state-of-the-art in Thai semantic similarity using distributional semantics and ontological information

Ponrudee Netisopakul et al.

Summary: This study aims to enhance baseline evaluations of Thai semantic similarity and address challenges of unsegmented Asian languages. By applying various strategies such as traditional word embeddings and ontological or hybrid sources, significant improvements in state-of-the-art metrics were achieved across multiple datasets.

PLOS ONE (2021)

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Article Computer Science, Information Systems

Integrating Sensor Ontologies with Global and Local Alignment Extractions

Xingsi Xue et al.

Summary: This paper proposes a method of extracting sensor ontology alignment from various alignments determined by different matchers using debate mechanism, calculating factors such as correctness factor and support strength, and assessing the performance with the evaluation of the Ontology Alignment Evaluation Initiative and real sensor ontologies. The results demonstrate the robustness and effectiveness of the approach compared to advanced ontology matching techniques.

WIRELESS COMMUNICATIONS & MOBILE COMPUTING (2021)

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Review Computer Science, Information Systems

Ontology learning: Grand tour and challenges

Ahlem Cherifa Khadir et al.

Summary: This paper presents the state of the art of ontology learning field, covering different classes of approaches and relevant solutions. It emphasizes the challenge of evaluating ontologies and identifies some unresolved issues.

COMPUTER SCIENCE REVIEW (2021)

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Article Computer Science, Information Systems

Semantic Similarity Computing Model Based on Multi Model Fine-Grained Nonlinear Fusion

Peiying Zhang et al.

Summary: This paper proposes a sentence similarity calculation method based on multi-model nonlinear fusion, combining traditional statistical methods and deep learning models. Experimental results show that the matching rate of this method is 84%, with an F1 value of 75%.

IEEE ACCESS (2021)

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Article Engineering, Electrical & Electronic