In this paper, we study the problem of learning probabilistic logical rules for inductive and interpretable link prediction. Despite the importance of inductive link prediction, most previous works focused on transductive link prediction and cannot manage previously unseen entities. Moreover, they are black-box models that are not easily explainable for humans. We propose DRUM, a scalable and differentiable approach for mining first-order logical rules from knowledge graphs that resolves these problems. We motivate our method by making a connection between learning confidence scores for each rule and low-rank tensor approximation.

Mapping structural brain connectomes for living human brains typically requires expert analysis and rule-based models on diffusion-weighted magnetic resonance imaging. A data-driven approach, however, could overcome limitations in such rule-based approaches and improve precision mappings for individuals. In this work, we explore a framework that facilitates applying learning algorithms to automatically extract brain connectomes. Using a tensor encoding, we design an objective with a group-regularizer that prefers biologically plausible fascicle structure. We show that the objective is convex and has unique solutions, ensuring identifiable connectomes for an individual.

Knowledge graph reasoning, which aims at predicting missing facts through reasoning with observed facts, is critical for many applications. Such a problem has been widely explored by traditional logic rule-based approaches and recent knowledge graph embedding methods. A principled logic rule-based approach is the Markov Logic Network (MLN), which is able to leverage domain knowledge with first-order logic and meanwhile handle uncertainty. However, the inference in MLNs is usually very difficult due to the complicated graph structures. TransE, DistMult) learn effective entity and relation embeddings for reasoning, which are much more effective and efficient.

It helps organizations to meet their most critical challenge of managing high false positives and provides a holistic view of investigating an alert. AMLOCK Analytics uses various statistical methods and machine learning algorithms to derive analyses and predictions based on institution specific historical data. One of the important features of 3i Infotech's AMLOCK Analytics is the reduction of false positives using risk profiling, through predictive analytics that identifies potential risk and thereby enhances decision making. The solution also provides an insight on the trends followed by customers, based on seasonality and identifies the anomalies based on deviation from these trends, where machine learning helps in customer segmentation. This enables users to investigate effectively by working closely on those groups which are risky or deemed outliers. Ravikanth Sama, Global Head- AML Practice, 3i Infotech said, "AMLOCK Analytics blends both the traditional rule-based system and the power of Analytics to bring better efficiency & risk focus.

With the rapid advances in computing and information technologies, traditional access control models have become inadequate in terms of capturing fine-grained, and expressive security requirements of newly emerging applications. An attribute-based access control (ABAC) model provides a more flexible approach for addressing the authorization needs of complex and dynamic systems. While organizations are interested in employing newer authorization models, migrating to such models pose as a significant challenge. Many large-scale businesses need to grant authorization to their user populations that are potentially distributed across disparate and heterogeneous computing environments. Each of these computing environments may have its own access control model. The manual development of a single policy framework for an entire organization is tedious, costly, and error-prone. In this paper, we present a methodology for automatically learning ABAC policy rules from access logs of a system to simplify the policy development process. The proposed approach employs an unsupervised learning-based algorithm for detecting patterns in access logs and extracting ABAC authorization rules from these patterns. In addition, we present two policy improvement algorithms, including rule pruning and policy refinement algorithms to generate a higher quality mined policy. Finally, we implement a prototype of the proposed approach to demonstrate its feasibility.

In the active research area of employing embedding models for knowledge graph completion, particularly for the task of link prediction, most prior studies used two benchmark datasets FB15k and WN18 in evaluating such models. Most triples in these and other datasets in such studies belong to reverse and duplicate relations which exhibit high data redundancy due to semantic duplication, correlation or data incompleteness. This is a case of excessive data leakage---a model is trained using features that otherwise would not be available when the model needs to be applied for real prediction. There are also Cartesian product relations for which every triple formed by the Cartesian product of applicable subjects and objects is a true fact. Link prediction on the aforementioned relations is easy and can be achieved with even better accuracy using straightforward rules instead of sophisticated embedding models. A more fundamental defect of these models is that the link prediction scenario, given such data, is non-existent in the real-world. This paper is the first systematic study with the main objective of assessing the true effectiveness of embedding models when the unrealistic triples are removed. Our experiment results show these models are much less accurate than what we used to perceive. Their poor accuracy renders link prediction a task without truly effective automated solution. Hence, we call for re-investigation of possible effective approaches.

Explainability has been an important goal since the early days of Artificial Intelligence. Several approaches for producing explanations have been developed. However, many of these approaches were tightly coupled with the capabilities of the artificial intelligence systems at the time. With the proliferation of AI-enabled systems in sometimes critical settings, there is a need for them to be explainable to end-users and decision-makers. We present a historical overview of explainable artificial intelligence systems, with a focus on knowledge-enabled systems, spanning the expert systems, cognitive assistants, semantic applications, and machine learning domains. Additionally, borrowing from the strengths of past approaches and identifying gaps needed to make explanations user- and context-focused, we propose new definitions for explanations and explainable knowledge-enabled systems.

The next question is, why do we even bother to explain something that is just a guess (but a very good one). If it works why do we even care? Actually the problem lies in us, in humans. Nowadays we don't trust anything that we cannot understand. The interesting part is that we still have trust in specialist (if it's a human specialist and not a robot), even if the error rate of a human specialist is much higher than of a specialized Artificial Neural Network.

This enables banks and financial institutions to identify complex and hidden AML patterns. It helps organizations to meet their most critical challenge of managing high false positives and provides a holistic view of investigating an alert. AMLOCK Analytics uses various statistical methods and machine learning algorithms to derive analyses and predictions based on institution specific historical data. Ravikanth Sama, Global Head- AML Practice, 3i Infotech said, "AMLOCK Analytics blends both the traditional rule-based system and the power of Analytics to bring better efficiency & risk focus. It can be hosted both on-premise and on cloud infrastructure. The solution provides a probability score indicating the chances of closing an alert based on the past actions taken by the users on similar alerts. AMLOCK Analytics improves the conversion rate of Suspicious Transaction Report (STR) or Suspicious Activity Report (SAR), as it dynamically correlates between the alerts in which suspicious transaction reports have been generated and those that have been tagged as false positives by the investigators."

The logic-based methods that learn first-order rules from knowledge graphs (KGs) for knowledge graph completion (KGC) task are desirable in that the learnt models are inductive, interpretable and transferable. The challenge in such rule learners is that the expressive rules are often buried in vast rule space, and the procedure of identifying expressive rules by measuring rule quality is costly to execute. Therefore, optimizations on rule generation and evaluation are in need. In this work, we propose a novel bottom-up probabilistic rule learner that features: 1.) a two-stage procedure for optimized rule generation where the system first generalizes paths sampled from a KG into template rules that contain no constants until a certain degree of template saturation is achieved and then specializes template rules into instantiated rules that contain constants; 2.) a grouping technique for optimized rule evaluation where structurally similar instantiated rules derived from the same template rules are put into the same groups and evaluated collectively over the groundings of the deriving template rules. Through extensive experiments over large benchmark datasets on KGC task, our algorithm demonstrates consistent and substantial performance improvements over all of the state-of-the-art baselines.