Variational Deep Logic Network for Joint Inference of Entities and Relations

Nowadays, deep learning models have been widely adopted and achieved promising results on various application domains. Despite of their intriguing performance, most deep learning models function as black-boxes, lacking explicit reasoning capabilities and explanations, which are usually essential for complex problems. Take joint inference in information extraction as an example. This task requires the identification of multiple structured knowledge from texts, which is inter-correlated, including entities, events and the relationships between them. Various deep neural networks have been proposed to jointly perform entity extraction and relation prediction, which only propagate information implicitly via representation learning. However, they fail to encode the intensive correlations between entity types and relations to enforce their co-existence. On the other hand, some approaches adopt rules to explicitly constrain certain relational facts. However, the separation of rules with representation learning usually restrains the approaches with error propagation. Moreover, the pre-defined rules are inflexible and might bring negative effects when data is noisy. To address these limitations, we propose a variational deep logic network that incorporates both representation learning and relational reasoning via the variational EM algorithm. The model consists of a deep neural network to learn high-level features with implicit interactions via the self-attention mechanism and a relational logic network to explicitly exploit target interactions. These two components are trained interactively to bring the best of both worlds. We conduct extensive experiments ranging from fine-grained sentiment terms extraction, end-to-end relation prediction to end-to-end event extraction to demonstrate the effectiveness of our proposed method.

A Markov logic network method for reconstructing association rule-mining tasks in library book recommendation

Purpose This study aims to overcome the problem of traditional association rules relying almost entirely on expert experience to set relevant interest indexes in mining. Second, this project can effectively solve the problem of four types of rules being present in the database at the same time. The traditional association algorithm can only mine one or two types of rules and cannot fully explore the database knowledge in the decision-making process for library recommendation. Design/methodology/approach The authors proposed a Markov logic network method to reconstruct association rule-mining tasks for library recommendation and compared the method proposed in this paper to traditional Apriori, FP-Growth, Inverse, Sporadic and UserBasedCF algorithms on two history library data sets and the Chess and Accident data sets. Findings The method used in this project had two major advantages. First, the authors were able to mine four types of rules in an integrated manner without having to set interest measures. In addition, because it represents the relevance of mining in the network, decision-makers can use network visualization tools to fully understand the results of mining in library recommendation and data sets from other fields. Research limitations/implications The time cost of the project is still high for large data sets. The authors will solve this problem by mapping books, items, or attributes to higher granularity to reduce the computational complexity in the future. Originality/value The authors believed that knowledge of complex real-world problems can be well captured from a network perspective. This study can help researchers to avoid setting interest metrics and to comprehensively extract frequent, rare, positive, and negative rules in an integrated manner.

Numerical Markov Logic Network: A Scalable Probabilistic Framework for Hybrid Knowledge Inference

In recent years, the Markov Logic Network (MLN) has emerged as a powerful tool for knowledge-based inference due to its ability to combine first-order logic inference and probabilistic reasoning. Unfortunately, current MLN solutions cannot efficiently support knowledge inference involving arithmetic expressions, which is required to model the interaction between logic relations and numerical values in many real applications. In this paper, we propose a probabilistic inference framework, called the Numerical Markov Logic Network (NMLN), to enable efficient inference of hybrid knowledge involving both logic and arithmetic expressions. We first introduce the hybrid knowledge rules, then define an inference model, and finally, present a technique based on convex optimization for efficient inference. Built on decomposable exp-loss function, the proposed inference model can process hybrid knowledge rules more effectively and efficiently than the existing MLN approaches. Finally, we empirically evaluate the performance of the proposed approach on real data. Our experiments show that compared to the state-of-the-art MLN solution, it can achieve better prediction accuracy while significantly reducing inference time.

ON LOGIC NETWORKS ALLOWING SHORT SINGLE FAULT DETECTION TESTS UNDER ARBITRARY FAULTS OF GATES

It is proved that one can implement any non-constant Boolean function in n variables by an irredundant logic network in the basis {&, ⊕, ¬}, containing not more than one dummy input variable and allowing a single fault detection test with length not more than 2n + 3 regarding arbitrary faults of logic gates.

A versatile molecular logic system based on Eu(iii) coordination polymer film electrodes combined with multiple properties of NADH

A 4-input/10-output logic network and various logic devices were established based on the multi-responsive Eu(iii)–PMAG film electrodes.