Mapping instances to the Linked Open Data (LOD) cloud plays an important role for enriching information of instances, since the LOD cloud contains abundant amounts of interlinked instances describing the instances. Consequently, many techniques have been introduced for mapping instances to a LOD data set; however, most of them merely focus on tackling with the problem of heterogeneity. Unfortunately, the problem of the large number of LOD data sets has yet to be addressed. Owing to the number of LOD data sets, mapping an instance to a LOD data set is not sufficient because an identical instance might not exist in that data set. In this article, we therefore introduce a heuristic expansion based framework for mapping instances to LOD data sets. The key idea of the framework is to gradually expand the search space from one data set to another data set in order to discover identical instances. In experiments, the framework could successfully map instances to the LOD data sets by increasing the coverage to 90.36%. Experimental results also indicate that the heuristic function in the framework could efficiently limit the expansion space to a reasonable space. Based upon the limited expansion space, the framework could effectively reduce the number of candidate pairs to 9.73% of the baseline without affecting any performances.

The traditional reinforcement learning (RL) methods can solve Markov Decision Processes (MDPs) online, but these learning methods cannot effectively use a priori knowledge to guide the learning process. The exploration of the optimal policy is time-consuming and does not employ the information about specific issues. To tackle the problem, this paper proposes heuristic function negotiation (HFN) as an online learning framework. The HFN framework extends MDPs and introduces heuristic functions. HFN changes the state-action dual layer structure of traditional RL to the triple layer structure, in which multiple heuristic functions can be set to meet the needs required to solve the problem. The HFN framework can use different algorithms to let the functions negotiate to determine the appropriate action, and adjust the impact of each function according to the rewards. The HFN framework introduces domain knowledge by setting heuristic functions and thus speeds up the problem solving of MDPs. Furthermore, user preferences can be reflected in the learning process, which improves the flexibility of RL. The experiments show that, by setting reasonable heuristic functions, the learning results of the HFN framework are more efficient than traditional RL. We also apply HFN to the air combat simulation of unmanned aerial vehicles (UAVs), which shows that different function settings lead to different combat behaviors.

Automatic alignment of bilingual texts is useful to example-based machine translation by facilitating the creation of example pairs of translation for the machine. Two main approaches to automatic alignment have been reported in the literature. They are lexical approach and statistical approach. The former looks for relationships between lexical contents of the bilingual texts in order to find alignment pairs, while the latter uses statistical correlation between sentence lengths of the bilingual texts as the basis of matching. This paper describes a combination of the two approaches in aligning Japanese-Cinese bilingual texts by allowing kanji contents and sentence lengths in the texts to work together in achieving an alignment process. Because of the sentential structure differences between Japanese and Chinese, matching at the sentence level may result in frequent matching between a number of sentences en masses. In view of this, the current work also attempts to create shorter alignment pairs by permitting sentences to be matched with clauses or phrases of the other text if possible. While such matching is more difficult and error-prone, the reliance on kanji contents has proven to be very useful in minimizing the errors. The current research has thus found solutions to problems that are unique to the present work.