# Extracting Domain-specific Concepts from Large-scale Linked Open Data

arXiv: https://arxiv.org/abs/2112.03102 · HTML: https://mlsanity.com/paper/2112.03102/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 46 references that are in the map.
- First posted on arXiv 22 Nov 2021.

## Builds on (most cited first, 4 of 4)

- [The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures](https://mlsanity.com/paper/1905.06939/) (2019)
- [RDF 1.1: Knowledge Representation and Data Integration Language for the Web](https://mlsanity.com/paper/2001.00432/) (2020)
- [Semi-Automatic Construction of a Domain Ontology for Wind Energy Using Wikipedia Articles](https://mlsanity.com/paper/1410.8581/) (2014)
- [Development of Semantic Web-based Imaging Database for Biological Morphome](https://mlsanity.com/paper/2110.12058/) (2021)

## Abstract (arXiv)

We propose a methodology for extracting concepts for a target domain from large-scale linked open data (LOD) to support the construction of domain ontologies providing field-specific knowledge and definitions. The proposed method defines search entities by linking the LOD vocabulary with technical terms related to the target domain. The search entities are then used as a starting point for obtaining upper-level concepts in the LOD, and the occurrences of common upper-level entities and the chain-of-path relationships are examined to determine the range of conceptual connections in the target domain. A technical dictionary index and natural language processing are used to evaluate whether the extracted concepts cover the domain. As an example of extracting a class hierarchy from LOD, we used Wikidata to construct a domain ontology for polymer materials and physical properties. The proposed method can be applied to general datasets with class hierarchies, and it allows ontology developers to create an initial model of the domain ontology for their own purposes.