Semantic Annotator for Knowledge Graph Exploration : Pattern-Based NLP Technique
Authors
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Associate Professor, DRTC, Indian Statistical Institute, Bangalore - 560059, Karnataka
Biswanath Dutta -
DRTC, Indian Statistical Institute, Bangalore – 560059, KarnatakaPuranjani Das
DOI:
https://doi.org/10.17821/srels/2023/v60i1/170889Keywords:
Application, Automated Annotation, Entity Annotation, Knowledge Graph Exploration, NLP, Semantic Annotation, Semantic Annotation Platform, Thing Annotation, Thing Spotting.Abstract
Semantic Annotator for knowledge Graph Exploration, abbreviated as SAGE is a “Thing” annotation system. Here, “Thing” refers to any concept, named individuals (aka entities), entity relations, and attributes. The system is primarily built based on the idea of “string to thing” where the “string” is any given text (e.g., abstract of an article) as input by the user. For annotation, the system utilises knowledge graph(s). SAGE can be used by anyone for annotating Things and for their exploitation on the Web. The annotation of things is done through exact and partial matches. For exact matches, the system makes explicit the name of the knowledge graphs it is sourced from. It also shows the type hierarchies for the matched named entities. In the current work, we describe the SAGE annotation system, designed on pattern-based NLP techniques, along with its features and various usage, and the experimental results.
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