DLCLJan 20, 2021

HIVE-4-MAT: Advancing the Ontology Infrastructure for Materials Science

arXiv:2101.07960v16 citations
Originality Synthesis-oriented
AI Analysis

This work addresses the need for better shared ontology infrastructures in materials science, though it appears incremental as it builds on existing knowledge extraction methods.

The paper tackles the problem of improving ontology infrastructure for materials science by introducing HIVE-4-MAT, an automatic linked data ontology application that enables vocabulary browsing, term search, and knowledge extraction, with plans to integrate named entity recognition and relation extraction.

Introduces HIVE-4-MAT - Helping Interdisciplinary Vocabulary Engineering for Materials Science, an automatic linked data ontology application. Covers contextual background for materials science, shared ontology infrastructures, and reviews the knowledge extraction and indexing process. HIVE-4-MAT's vocabulary browsing, term search and selection, and knowledge extraction and indexing are reviewed, and plans to integrate named entity recognition. Conclusion highlights next steps with relation extraction to support better ontologies.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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