In this paper we focus on the opacity issue of sub-symbolic machine learning predictors by promoting two complementary activities, namely, symbolic knowledge extraction (SKE) and injection (SKI) from and into sub-symbolic predictors. We consider as symbolic any language being intelligible and interpretable for both humans and computers. Accordingly, we propose general meta-models for both SKE and SKI, along with two taxonomies for the classification of SKE and SKI methods. By adopting an explainable artificial intelligence (XAI) perspective, we highlight how such methods can be exploited to mitigate the aforementioned opacity issue. Our taxonomies are attained by surveying and classifying existing methods from the literature, following a systematic approach, and by generalising the results of previous surveys targeting specific sub-topics of either SKE or SKI alone. More precisely, we analyse 132 methods for SKE and 117 methods for SKI, and we categorise them according to their purpose, operation, expected input/output data and predictor types. For each method, we also indicate the presence/lack of runnable software implementations. Our work may be of interest for data scientists aiming at selecting the most adequate SKE/SKI method for their needs, and also work as suggestions for researchers interested in filling the gaps of the current state of the art, as well as for developers willing to implement SKE/SKI-based technologies.

Biomedical knowledge graphs (BKGs) have emerged as powerful tools for organizing and leveraging the vast and complex data found across the biomedical field. Yet, current reviews of BKGs often limit their scope to specific domains or methods, overlooking the broader landscape and the rapid technological progress reshaping it. In this survey, we address this gap by offering a systematic review of BKGs from three core perspectives: domains, tasks, and applications. We begin by examining how BKGs are constructed from diverse data sources, including molecular interactions, pharmacological datasets, and clinical records. Next, we discuss the essential tasks enabled by BKGs, focusing on knowledge management, retrieval, reasoning, and interpretation. Finally, we highlight real-world applications in precision medicine, drug discovery, and scientific research, illustrating the translational impact of BKGs across multiple sectors. By synthesizing these perspectives into a unified framework, this survey not only clarifies the current state of BKG research but also establishes a foundation for future exploration, enabling both innovative methodological advances and practical implementations.

This paper solves the task of knowledge base completion i.e. filling the missing relations between two entities by combining Statistical Relational Model like Markov Logic, and knowledge graph embedding method like TransE. Authors define a set of rules to be used in MLNs and then define a joint probability distribution over the observed and hidden triplets. Similarly, they define a joint probability distribution using KGE approaches (specifically they chose transE model). Then they employ the variational EM algorithm to learn the MLN weights and finally predicting the probabilities of hidden triplets. Originality: I really liked the paper, and enjoyed thoroughly reading it.

Authors propose DRUM, an end-to-end differentiable rule-based inference method which can be used for mining rules via backprop, and extracting rules from data. Their approach is quite interesting - it can be trained from positive examples only, without negative sampling (this is currently a burden for representation learning algorithms targeting knowledge graphs). In DRUM, paths in a knowledge graph are represented by a chain of matrix multiplications (this idea is not especially novel - see [1]). For mining rules, authors start from a formulation of the problem where each rule is associated with a confidence weight, and try to maximise the likelihood of training triples by optimising an end-to-end differentiable objective. However, the space of possible rules (and thus the number of parameters as confidence scores) is massive, so authors propose a way of efficiently approximating the rule scores tensor using with another having a lower rank (Eq.

This paper proposes an interesting approach for differentiable, interpretable rule mining given a knowledge base. The major pro of the approach is its in an inductive setting without the need for negative examples, which excited the reviewers. Initially the paper lacked important comparisons to many related works, but the author did a good job in rebuttal. Please include the comparison results in the final version and the results on other datasets pointed out by the reviewers. I would like to recommend an acceptance to NeurIPS.

Freshly returned to the White House, U.S. President Donald Trump signaled Monday that his administration would not focus on maintaining the rules-based global order or cultivating the American network of alliances. Instead, he vowed that the United States would be a dominating force that would take whatever steps necessary to advance American interests -- including flexing its economic muscle and, if necessary, employing its military strength. "We will be the envy of every nation, and we will not allow ourselves to be taken advantage of any longer," Trump said in a speech that mainly focused on domestic issues following his swearing-in. "During every single day of the Trump administration, I will, very simply, put America first."

Many security applications require unsupervised anomaly detection, as malicious data are extremely rare and often only unlabeled normal data are available for training (i.e., zero-positive). However, security operators are concerned about the high stakes of trusting black-box models due to their lack of interpretability. In this paper, we propose a post-hoc method to globally explain a black-box unsupervised anomaly detection model via rule extraction.First, we propose the concept of distribution decomposition rules that decompose the complex distribution of normal data into multiple compositional distributions. To find such rules, we design an unsupervised Interior Clustering Tree that incorporates the model prediction into the splitting criteria. Then, we propose the Compositional Boundary Exploration (CBE) algorithm to obtain the boundary inference rules that estimate the decision boundary of the original model on each compositional distribution.

Rule-based models, e.g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity. However, rule-based models are hard to optimize, especially on large data sets, due to their discrete parameters and structures. Ensemble methods and fuzzy/soft rules are commonly used to improve performance, but they sacrifice the model interpretability. To obtain both good scalability and interpretability, we propose a new classifier, named Rule-based Representation Learner (RRL), that automatically learns interpretable non-fuzzy rules for data representation and classification. To train the non-differentiable RRL effectively, we project it to a continuous space and propose a novel training method, called Gradient Grafting, that can directly optimize the discrete model using gradient descent.

Rule sets are highly interpretable logical models in which the predicates for decision are expressed in disjunctive normal form (DNF, OR-of-ANDs), or, equivalently, the overall model comprises an unordered collection of if-then decision rules. In this paper, we consider a submodular optimization based approach for learning rule sets. The learning problem is framed as a subset selection task in which a subset of all possible rules needs to be selected to form an accurate and interpretable rule set. We employ an objective function that exhibits submodularity and thus is amenable to submodular optimization techniques. To overcome the difficulty arose from dealing with the exponential-sized ground set of rules, the subproblem of searching a rule is casted as another subset selection task that asks for a subset of features.

Optical Chemical Structure Recognition (OCSR) deals with the translation from chemical images to molecular structures, this being the main way chemical compounds are depicted in scientific documents. Traditionally, rule-based methods have followed a framework based on the detection of chemical entities, such as atoms and bonds, followed by a compound structure reconstruction step. Recently, neural architectures analog to image captioning have been explored to solve this task, yet they still show to be data inefficient, using millions of examples just to show performances comparable with traditional methods. Looking to motivate and benchmark new approaches based on atomic-level entities detection and graph reconstruction, we present CEDe, a unique collection of chemical entity bounding boxes manually curated by experts for scientific literature datasets. These annotations combine to more than 700,000 chemical entity bounding boxes with the necessary information for structure reconstruction.