This article, part 5 of a series, emphasizes the importance of maintaining the quality of knowledge graphs (KG) for their credibility. It outlines a dual-stage process for entity resolution, highlighting the use of ChemDataExtractor to extract chemical formulas and establish accurate name-acronym relationships. The authors apply word embedding techniques on the derived entities to enhance the precision of core labels. This approach aims to correct any discrepancies in the representation of materials by ensuring a diligent comparison of entity similarities to uphold the integrity of the information presented in knowledge graphs.
To ensure the credibility of knowledge graphs (KG), itâs paramount to verify and, if necessary, correct inference results prior to their construction.
The process of entity resolution is critical; it involves standardization and the correction of core labels, particularly focusing on the relationships between names and acronyms.
Employing ChemDataExtractor allows us to derive chemical formulas and facilitate accurate name-acronym pairings through advanced algorithms like word embedding.
The comparison of similarities between entities extracted by ChemDataExtractor and other models is essential for maintaining the accuracy and reliability of material information.
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