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%.

The advancement of intelligent systems, particularly in domains such as natural language processing and autonomous driving, has been primarily driven by deep neural networks (DNNs). However, these systems exhibit vulnerability to adversarial attacks that can be both subtle and imperceptible to humans, resulting in arbitrary and erroneous decisions. This susceptibility arises from the hierarchical layer‐by‐layer learning structure of DNNs, where small distortions can be exponentially amplified. While several defense methods have been proposed, they often necessitate prior knowledge of adversarial attacks to design specific defense strategies. This requirement is often unfeasible in real‐world attack scenarios. In this paper, we introduce a novel learning model, termed “immune” learning, known as adversarial‐resilient deep symbolic tree (ARDST), from a neurosymbolic perspective. The ARDST model is semiparametric and takes the form of a tree, with logic operators serving as nodes and learned parameters as weights of edges. This model provides a transparent reasoning path for decision‐making, offering fine granularity, and has the capacity to withstand various types of adversarial attacks, all while maintaining a significantly smaller parameter space compared to DNNs. Our extensive experiments, conducted on three benchmark datasets, reveal that ARDST exhibits a representation learning capability similar to DNNs in perceptual tasks and demonstrates resilience against state‐of‐the‐art adversarial attacks.

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.