论文标题
知识检索，以功能为导向的网络
Knowledge Retrieval With Functional Object-Oriented Networks
论文作者
论文摘要
在本实验中，实现了三种不同的搜索算法，目的是从大型知识图中提取任务树（称为功能对象的网络（FOON））。使用通用的Foon，其中包含通过注释在线烹饪视频的注释知识，并且可以检索一个任务树。使用迭代加深搜索和贪婪的最佳搜索搜索搜索通用FOON以进行任务树检索的过程，并具有两个不同的启发式功能。分析并比较了这三种算法的性能。实验的结果表明，迭代加深的总体表现强劲。但是，事实证明，在知情搜索中，不同的启发式方法对某些情况有益。
In this experiment, three different search algorithms are implemented for the purpose of extracting a task tree from a large knowledge graph, known as the Functional Object-Oriented Network (FOON). Using a universal FOON, which contains knowledge extracted by annotating online cooking videos, and a desired goal, a task tree can be retrieved. The process of searching the universal FOON for task tree retrieval is tested using iterative deepening search and greedy best-first search with two different heuristic functions. The performance of these three algorithms is analyzed and compared. The results of the experiment show that iterative deepening performs strongly overall. However, different heuristics in an informed search proved to be beneficial for certain situations.
