Knowledge base completion (KBC) aims to automatically infer missing facts by exploiting information already present in a knowledge base (KB). A promising approach for KBC is to embed knowledge into latent spaces and make predictions from learned embeddings. However, existing embedding models are subject to at least one of the following limitations: (1) theoretical inexpressivity, (2) lack of support for prominent inference patterns (e.g., hierarchies), (3) lack of support for KBC over higher-arity relations, and (4) lack of support for incorporating logical rules. Here, we propose a spatio-translational embedding model, called BoxE, that simultaneously addresses all these limitations. BoxE embeds entities as points, and relations as a set of hyper-rectangles (or boxes), which spatially characterize basic logical properties. This seemingly simple abstraction yields a fully expressive model offering a natural encoding for many desired logical properties. BoxE can both capture and inject rules from rich classes of rule languages, going well beyond individual inference patterns. By design, BoxE naturally applies to higher-arity KBs. We conduct a detailed experimental analysis, and show that BoxE achieves state-of-the-art performance, both on benchmark knowledge graphs and on more general KBs, and we empirically show the power of integrating logical rules.

Amid a global pandemic, economic recession and simmering racial tensions around the world, Israel's threat to formally annex parts of occupied Palestinian territory presents yet another international crisis in the making. This is because, with this outrageous move, the Israeli government threatens to unravel the rules-based system of international relations. Today's international law regime was established in the first half of the 20th century not only to regulate relations between states but also to assist the movements for self-determination across the world and oversee the end of colonialism. The looming Israeli annexation of Palestinian land and the global inaction on it evidence the failure of this regime to help end colonialism and put its very raison d'etre in question. Much of the narrative in international diplomatic circles around the issue of annexation has revolved around deterrence, with the rationale being the threat of tangible consequences to annexation will lead to a reconsideration of the move. Yet this narrative fails to acknowledge that we have reached a point, where Israel will annex yet another chunk of Palestinian territory precisely because deterrence has not worked.

It wasn't too long ago that talking to a computer and having it not only understand, but speak back, was confined to the realm of science fiction, like that of the shipboard computers of Star Trek. The technology of the 24th century's Starship Enterprise is reality in the 21st century thanks to natural language processing (NLP), a machine learning-driven discipline that gives computers the ability to understand, process, and respond to spoken words and written text. Make no mistake: NLP is a complicated field that one can spend years studying. This guide contains the basics about NLP, details how it can benefit businesses, and explains where to get started with its implementation. Natural language processing (NLP) is a cross-discipline approach to making computers hear, process, understand, and duplicate human language.

Associations are the specific measurable constraints on interestingness used in association rule learning. Regardless of the rules being employed to classify new data, the associations need to be defined by constraints to determine what is both interesting and relevant. Support – How frequently the pattern/items occur in the dataset. Confidence – How often the rule being used has been true (conditional probability). Lift – Actual success rate of the target model (rule) over the expected success from random chance.

The rule extraction literature contains the notion of a fidelity-accuracy dilemma: when building an interpretable model of a black box function, optimising for fidelity is likely to reduce performance on the underlying task, and vice versa. I reassert the relevance of this dilemma for the modern field of explainable artificial intelligence, and highlight how it is compounded when the black box is an agent interacting with a dynamic environment. I then discuss two independent research directions - building white box agents and interpreting black box agents - which are both coherent and worthy of attention, but must not be conflated by researchers embarking on projects in the domain of agent interpretability.

This research is about the development a fuzzy decision support system for the diagnosis of coronary artery disease based on evidence. The coronary artery disease data sets taken from University California Irvine (UCI) are used. The knowledge base of fuzzy decision support system is taken by using rules extraction method based on Rough Set Theory. The rules then are selected and fuzzified based on information from discretization of numerical attributes. Fuzzy rules weight is proposed using the information from support of extracted rules. UCI heart disease data sets collected from U.S., Switzerland and Hungary, data from Ipoh Specialist Hospital Malaysia are used to verify the proposed system. The results show that the system is able to give the percentage of coronary artery blocking better than cardiologists and angiography. The results of the proposed system were verified and validated by three expert cardiologists and are considered to be more efficient and useful.

AI is a broad term covering multiple fields. For AML professionals, perhaps the most relevant subfield of AI is machine learning, which refers to the use of algorithms to continually improve a task, without the need for human intervention. Machine learning algorithms search for patterns within a given data set. Repeated recognition of patterns allows an algorithm to make ever more swift and accurate predictions. According to a survey of 296 UK-based AML professionals conducted by The Economist Intelligence Unit, the areas where respondents believe AI and advanced analytics can best be applied to combat money laundering are suspicious activity reporting (45%) and transaction monitoring (43%).

Artificial Intelligence (AI) environment has risen from data scientists to reach the boardroom as a pre-curser to digital transformation. Clayton Christensen, author of The Innovator's Dilemma, a disruptive technology adds to the premise writing AI "enables new markets to emerge" to disrupts an existing market status-quo. The adoption path of AI needs a well thought out strategy to evolve in response to the dynamically changing technology parlance. These changes are well equated to the waves in an ocean, where either CIOs need to learn how to ride the wave or be overpowered by its force. Artificial Intelligence defined in business parlance are algorithms that imitate human thinking applying to compute systems using logic, decision trees and if-then rules.

With the huge success of deep learning, other machine learning paradigms have had to take back seat. Yet other models, particularly rule-based, are more readable and explainable and can even be competitive when labelled data is not abundant. However, most of the existing rule-based classifiers suffer from the production of a large number of classification rules, affecting the model readability. This hampers the classification accuracy as noisy rules might not add any useful informationfor classification and also lead to longer classification time. In this study, we propose SigD2 which uses a novel, two-stage pruning strategy which prunes most of the noisy, redundant and uninteresting rules and makes the classification model more accurate and readable. To make SigDirect more competitive with the most prevalent but uninterpretable machine learning-based classifiers like neural networks and support vector machines, we propose bagging and boosting on the ensemble of the SigDirect classifier. The results of the proposed algorithms are quite promising and we are able to obtain a minimal set of statistically significant rules for classification without jeopardizing the classification accuracy. We use 15 UCI datasets and compare our approach with eight existing systems.The SigD2 and boosted SigDirect (ACboost) ensemble model outperform various state-of-the-art classifiers not only in terms of classification accuracy but also in terms of the number of rules.

Mining association rules from data streams is a challenging task due to the (typically) limited resources available vs. the large size of the result. Frequent closed itemsets (FCI) enable an efficient first step, yet current FCI stream miners are not optimal on resource consumption, e.g. they store a large number of extra itemsets at an additional cost. In a search for a better storage-efficiency trade-off, we designed Ciclad,an intersection-based sliding-window FCI miner. Leveraging in-depth insights into FCI evolution, it combines minimal storage with quick access. Experimental results indicate Ciclad's memory imprint is much lower and its performances globally better than competitor methods.