The neural-network-based charge prediction, which is to predict the defendants’ charges from the criminal case documents via neural network, has been a development-potential affair in artificial intelligence (AI) based legal assistant system and made some achievements. Neural network is playing important role to capture deep information in current work. However, charge prediction suffers from serious data imbalance in real-world situation. Only high-frequency charges are easy to be predicted whereas plenty of low-frequency ones are hard to be hold. Furthermore, the presence of confusing charges makes prediction worse. Here, we propose a novel model of charge prediction via the judicial interpretation of crimes (CPJIC) to provide more accurate charge prediction. The concept of crime interpretation is introduced into CPJIC, which alleviates the problems resulted from data imbalance and confusing charges. With the technique of embedding, both fact description and crime interpretation are embedded into a low-dimensional vector space as well as a neural network, delivering implemented computable charge prediction. The experimental results demonstrate that CPJIC can identify the low-frequency and confusing charges better than previous work.

Knowledge graph embedding aims to transform the entities and relations of triplets into the low-dimensional vectors. Previous methods are oriented towards the static knowledge graphs, in which all entities and relations are assumed to be known and only some unknown triplets need to be predicted. However, the real-world knowledge graphs can grow dynamically, and some new knowledge are often added. To embed the new knowledge into the space of original knowledge graph, the classic models have to perform the entire re-embedding with including the new and original knowledge. This causes heavy computational burden for embedding. To address this problem, this study proposes a new model of anchors-based incremental embedding (ABIE) to implement the dynamical embedding for the growing knowledge graph. According to ABIE, every knowledge graph has some key entities, called anchors, which can fix the embedding space of knowledge graph. When some new knowledge is added into the graph, only a few updated entities and relations are embedded into the embedding space with the help of anchors, and the entire re-embedding on the whole graph is not necessary. By this way, the computational burden of embedding caused by the growth of knowledge graph is reduced significantly.

Role mining has been extensively used to automatically generate roles for role-based access control. Nevertheless, the two core problems in role mining, role minimization and edge concentration, are both NP-hard. While many approximate algorithms have been developed to solve the problems, experimental tests disclose that no algorithm clearly outperforms the others in both role minimization and edge concentration. The performance results highly depend on the data set under study. To determine the right role mining algorithm, a trial-and-error approach is time consuming due to the computational overhead in mining large data set. We tackle the problem from a fresh angle. Instead of developing fast role mining algorithms, we adopt a data-centric approach that quickly estimates the bounds on optimal role mining results without actually running any role mining algorithm. Based on the inherent features of the data set, the approach can also determine whether it is easy to achieve both role minimization and edge concentration, and if not, which direction, role minimization or edge concentration, that role mining could move toward further.

Access control is one of the most basic techniques to ensure the security of the information system. The traditional access controls of information systems are usually performed based on the traversals or queries of rules. However, with the increasing complexity of information systems, massive data, and open environments bring great workload and risk for the traditional methods. This study proposes a model of embedding-based computable access control (ECAC), by employing the idea of representation learning in artificial intelligence. According to ECAC, access control rules can be embedded into a Euclidean vector space, and the security of arbitrary behavior can be computed by numerical vector operations, without any traditional querying or traversing of rules, and thus the workload of access control is reduced. Furthermore, by the embedding-based computation, the security of unknown behaviors can be predicted. Potentially, due to the use of numerical vectors instead of traditional semantic symbols, the risk of privacy leakage via semantics can be reduced. Finally, as the first embedding-based access control model, the effectiveness of ECAC is evaluated and concluded by the experiment-based analyses and discussions.