用通用数据源构建专用领域知识图谱，提升实体消歧与链接效率。
Domain specific ontologies from Linked Open Data (LOD)
- 基于通用数据流自动构建IT领域本体
- 通过领域术语词典扩展知识覆盖范围
- 适合需要定制化知识的行业应用
逻辑与概率推理任务正越来越多依赖维基数据、DBpedia等通用本体。但实体消歧与链接等任务可从领域专用知识图谱中获益，这类图谱更易消费且便于集成私有内容。本文分享了利用无领域依赖流程自举构建IT领域本体的经验，并通过领域特定术语表进行扩展。
原文摘要 · Abstract (English)
Logical and probabilistic reasoning tasks that require a deeper knowledge of semantics are increasingly relying on general purpose ontologies such as Wikidata and DBpedia. However, tasks such as entity disambiguation and linking may benefit from domain specific knowledge graphs, which make it more efficient to consume the knowledge and easier to extend with proprietary content. We discuss our experience bootstrapping one such ontology for IT with a domain-agnostic pipeline, and extending it using domain-specific glossaries.
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