學習將自然語言敘述映射為知識圖譜表示形式以利知識庫之建立

Abstract

透過資訊擷取系統以自動化方式建立知識庫，仍面臨許多挑戰。其中，對應知識在自然語言與知識庫中的字詞表示形式差異仍是一項問題。本篇論文以機器自動學習的方法，將知識三元組中的關係片語從自然語言形式的關係樣式，轉換為知識庫格式的關係謂語。我們在向量空間上訓練一個字詞表示模型，並藉此建立連結語意相等的關係樣式與關係謂語。相較於前人們的研究，我們所建立的關係片語對應表，不僅準確而且達到可觀的高覆蓋率。

Directly adding the knowledge triples obtained from open information extraction systems into a knowledge base is often impractical due to a vocabulary gap between natural language expressions and knowledge base representation. This thesis aims at learning to map relational phrases in triples from natural-language-like statement to knowledge base predicate format. We train a word representation model on a vector space and link each natural language relational pattern to semantically equivalent knowledge base predicate. Our mapping result shows not only high quality, but also promising coverage on relational phrases compared to previous researches.