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GRAPH: A Domain Ontology-driven Semantic Graph Auto Extraction System |
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PP: 9S-16S |
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Author(s) |
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Chunying Zhou,
Huajun Chen,
Jinhuo Tao,
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Abstract |
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| This paper presents sGRAPH – a domain ontology-driven semantic graph auto
extraction system used to discover knowledge from text publications in traditional
Chinese medicine. The traditional Chinese medicine language system (TCMLs),
composed of an ontology schema and a knowledge base containing 153,692 words
and 304,114 relations, is used as the domain ontology. The sGRAPH comprises two
components: a user interface that interacts with users and the domain ontology-based
semantic graph extraction algorithm. This algorithm is divided into five steps: text
processing, semantic graph extraction, graph identification, keyword-based semantic
graph search and the selectable enrichment to the knowledge base. When the
knowledge base of TCMLs is used, the domain-specific words are extracted from
sentences more accurately; and the hierarchical structure of the ontology can also be
used to help identify the extracted graphs. The algorithm not only can extract
relations between words that have already been annotated by relations in the
knowledge base but also can predict the relations between words that have never been
annotated by relations. The sGRAPH was developed and evaluated by extracting
semantic graphs from 2000 publications which predicted 6778 relations that have
never been found. |
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