Mapping from sentence phrases to knowledge graph resources is an important step for
applications such as search engines, automatic question answering systems based on
acknowledge base and knowledge graphs. The existing solution maps a simple phrase
to a knowledge graph resource strictly or approximately from the text. However, it is
difficult to detect phrases and map the composite semantic resource. This paper proposes a longest matching resource mapping scheme to solve this problem, namely, to
find the longest substring in a sentence that can match the knowledge base resource.
Based on this scheme, we propose an optimization algorithm based on state compression dynamic programming. Furthermore, we improve the operating efficiency by removing invalid states. Experimental results show that our proposed optimization algorithm considerably improves the efficiency of the benchmark algorithm in terms of
running time.
Cite This Article
APA Style
Min, Z., Haibin, T., Ming, J., Tao, W., Jingfan, T. (2019). A longest matching resource mapping algorithm with state compression dynamic programming optimization. Intelligent Automation & Soft Computing, 25(3), 625-635. https://doi.org/10.31209/2019.100000117
Vancouver Style
Min Z, Haibin T, Ming J, Tao W, Jingfan T. A longest matching resource mapping algorithm with state compression dynamic programming optimization. Intell Automat Soft Comput . 2019;25(3):625-635 https://doi.org/10.31209/2019.100000117
IEEE Style
Z. Min, T. Haibin, J. Ming, W. Tao, and T. Jingfan "A Longest Matching Resource Mapping Algorithm with State Compression Dynamic Programming Optimization," Intell. Automat. Soft Comput. , vol. 25, no. 3, pp. 625-635. 2019. https://doi.org/10.31209/2019.100000117