Text Entailment Based Evaluation Approach for Generating Labelled Knowledge Graph from Textual Sources

The increase in textual data across various domains has prompted the need for structured formats to take advantage of the knowledge contained in these sources. Knowledge Graphs serve as an effective tool for organizing and extracting insights efficiently. However, constructing a Knowledge Graph from text presents formidable challenges due to the unstructured nature of textual data, which requires advanced Natural Language Processing techniques to parse and infer semantic relationships accurately. To evaluate the structure of the Knowledge Graph proposed in this paper, an approach based on Textual Entailment is introduced to assess the consistency and precision of Knowledge Graphs. This method leverages Textual Entailments to determine whether the textual data support the inferred relationships within the Knowledge Graph, thereby ensuring that the constructed graph faithfully represents the underlying knowledge.