101. Finding most informative common ancestor in cross-ontological semantic similarity assessment: An intrinsic information content-based approach.
- Author
-
Adhikari, Abhijit, Dutta, Biswanath, and Dutta, Animesh
- Subjects
- *
MEDICAL terminology , *ARTIFICIAL intelligence , *COGNITIVE science , *INFORMATION retrieval , *ONTOLOGIES (Information retrieval) , *MICA , *PATTERN matching - Abstract
Semantic Similarity (SS) has become a long-standing research domain in artificial intelligence and cognitive science for measuring the strength of the semantic relationship between entities (e.g., words, documents). Several ontology-based SS measures have been proposed in the recent time due to their ability of mimicking the cognitive process of humans. Among them, intrinsic information content (IC) based approaches have shown a significant correlation with human assessment. The design principle of the existing intrinsic IC-based SS measures constrain themselves to be applicable in a single ontology. However, such SS measures can be leveraged within two ontologies with the help of identifying the most informative common ancestors (MICA) across the ontologies. Existing IC-based MICA identification algorithms follow string matching of the labels of the concepts. In this paper, we propose a novel intrinsic IC-based MICA finding algorithm that exploits two domain-ontologies for finding SS without using string matching of the labels. The proposed approach has been evaluated using a widely used benchmark dataset of medical terms. The experimental results show that the proposed IC-based approach can be a stepping stone to a new direction in the process of finding MICA over two ontologies. • An algorithm for finding Cross-Ontology Semantic Similarity between concepts. • Avoiding strict string matching for finding the most informative common ancestor. • Intrinsic Information Theoretic Knowledge-based paradigm followed. • Correlation Coefficient with human cognition has been measured. • Information retrieval becomes more effective. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF