Knowledge base‐based intelligent question‐answering systems have insufficient understanding of the questions. In the early stages of research, it is effective in most cases that the existing natural language question‐understanding methods can answer questions by connecting entities and relationships when ignoring the identification of focus words. However, as research deepens, ignoring focus words has become a shortcoming. To address this, we propose identifying focus words, enabling more precise understanding of user focus. We define focus itemset, frequent focus itemset, focus association rule, and strong focus association rule to express focus‐related information better. Given the unique nature of focus association rules, we propose a prefix tree structure and an algorithm for mining association rules aimed at identifying focus words. We also introduce an inverted index specifically designed for focus association rules and propose an efficient algorithm for identifying focus words based on this index. Experiments verify the effectiveness of our algorithm and the efficiency of the inverted index, with a focus word identification rate exceeding 90%.

Machine understanding and thinking require prior knowledge consisting of explicit and implicit knowledge. The current knowledge base contains various explicit knowledge but not implicit knowledge. As part of implicit knowledge, the typical characteristics of the things referred to by the concept are available by concept cognition for knowledge graphs. Therefore, this paper attempts to realize concept cognition for knowledge graphs from the perspective of mining multigranularity decision rules. Specifically, (1) we propose a novel multigranularity three-way decision model that merges the ideas of multigranularity (i.e., from coarse granularity to fine granularity) and three-way decision (i.e., acceptance, rejection, and deferred decision). (2) Based on the multigranularity three-way decision model, an algorithm for mining multigranularity decision rules is proposed. (3) The monotonicity of positive or negative granule space ensured that the positive (or negative) granule space from coarser granularity does not need to participate in the three-classification process at a finer granularity, which accelerates the process of mining multigranularity decision rules. Moreover, the experimental results show that the multigranularity decision rule is better than the two-way decision rule, frequent decision rule and single granularity decision rule, and the monotonicity of positive or negative granule space can accelerate the process of mining multigranularity decision rules.

A spatial colocation pattern represents a subset of spatial features with instances that are prevalently located together in a geographic space. Although many algorithms for mining spatial colocation patterns have been proposed, the following problems still remain. these methods miss certain meaningful patterns (e.g., {Ganoderma_lucidumnew, maple_treedead} and {water_hyacinthnew(increase), algaedead(decrease)}) and obtain a wrong conclusion if the instances of two or more features increase/decrease (i.e., new/dead) in the same/approximate proportion, which has no effect on the prevalent patterns; and the efficiency of existing methods is low in mining prevalent spatial colocation patterns, because the number of prevalent spatial colocation patterns is quite large. Therefore, we first propose the concept of a dynamic spatial colocation pattern that can reflect the dynamic relationships among spatial features. Second, we mine a small number of prevalent maximal dynamic spatial colocation patterns that can derive all prevalent dynamic spatial colocation patterns, which can improve the efficiency of obtaining all prevalent dynamic spatial colocation patterns. Third, we propose an algorithm for mining prevalent maximal dynamic spatial colocation patterns and two pruning strategies. Finally, the effectiveness and efficiency of the proposed method and the pruning strategies are verified by extensive experiments over real/synthetic data sets.