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First year of the UQ sustainable energy micromasters series: Evaluation of participation and achievement
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Leveraging knowledge graph embeddings for natural language question answering
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Abstract:
A promising pathway for natural language question answering over knowledge graphs (KG-QA) is to translate natural language questions into graph-structured queries. During the translation, a vital process is to map entity/relation phrases of natural language questions to the vertices/edges of underlying knowledge graphs which can be used to construct target graph-structured queries. However, due to linguistic flexibility and ambiguity of natural language, the mapping process is challenging and has been a bottleneck of KG-QA models. In this paper, we propose a novel framework, called KemQA, which stands on recent advances in relation phrase dictionaries and knowledge graph embedding techniques to address the mapping problem and construct graph-structured queries of natural language questions. Extensive experiments were conducted on question answering benchmark datasets. The results demonstrate that our framework outperforms state-of-the-art baseline models in terms of effectiveness and efficiency.
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Keyword:
1700 Computer Science; 2614 Theoretical Computer Science; Knowledge graph; Knowledge graph embedding; Natural language question answering
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URL: https://espace.library.uq.edu.au/view/UQ:8ad10b5
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Using LIP to gloss over faces in single-stage face detection networks
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Vertical and sequential sentiment analysis of micro-blog topic
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DAVE: extracting domain attributes and values from text corpus
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Structural operational semantics through context-dependent behaviour
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A Correlation Analysis on LSA and HAL Semantic Space Models
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Fuzzy K-means clustering on a high dimensional semantic space
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A natural language interpreter for the construction of conceptual schemas
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