在過去十年中，本體論的出現對生命科學領域的生物標定有非常深遠的影響。MeSH本體論是一個用來標定生物醫學文獻的字典，進而輔助使用者在PubMed的文獻搜尋。由於近年來資訊爆炸性成長，有非常多的字詞與概念陸續推陳出新。自動化擴展既有字詞的技術可以輔助生物標定專家以較有系統的機制來維護、更新本體論。然而，大多數的相關技術都是基於一個裝載文字的容器中以詞彙樣式規則來進行字詞擴展，這使得本體論中偏狹義的字詞難於達到好的擴展效果。因此，在這篇研究中，我們設計了一個以族譜為基礎的本體論自我擴展演算法。基於一個字詞在本體論族譜中的廣度與深度，我們提出一個同輩關係字詞衍生法與一個修剪過濾方法。實驗結果顯示，我們的各類別平均精確度為0.50，而其中在有機體學本體論類別中有最好的精確度0.83表現，與我們的基準方法比較下有明顯改善。此外，我們也進一步發現利用其他領域本體論來進行字詞擴展會有不錯的效果。

During the last decade, the advent of Ontologies used for biomedical annotation has had a deep impact on life science. MeSH is a well-known Ontology for the purpose of indexing journal articles in PubMed, improving literature searching on multi-domain topics. Since the explosion of data growth in recent years, there are new terms, concepts that weed through the old and bring forth the new. Automatically extending sets of existing terms will enable bio-curators to systematically improve text-based ontologies level by level. However, most of the related techniques which apply symbolic patterns based on a literature corpus tend to focus on more general but not specific parts of the ontology. Therefore, in this work, we present a novel method for utilizing genealogical information from Ontology itself to find suitable siblings for ontology extension. Based on the breadth and depth dimensions, the sibling generation stage and pruning strategy are proposed in our approach. As a result, on the average, the precision of the genealogical-based method achieved 0.5, with the best 0.83 performance of category “Organisms”. We also achieve average precision 0.69 of 229 new terms in MeSH 2013 version. Furthermore, we found that there is an opportunity for extending Ontology by multiple domains, with employing the knowledge from Ontologies of different domains.

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