Knowledge graphs (KGs) typically contain temporal facts indicating relationships among entities at different times. Due to their incompleteness, several approaches have been proposed to infer new facts for a KG based on the existing ones-a problem known as KG completion. KG embedding approaches have proved effective for KG completion, however, they have been developed mostly for static KGs. Developing temporal KG embedding models is an increasingly important problem. In this paper, we build novel models for temporal KG completion through equipping static models with a diachronic entity embedding function which provides the characteristics of entities at any point in time. This is in contrast to the existing temporal KG embedding approaches where only static entity features are provided. The proposed embedding function is model-agnostic and can be potentially combined with any static model. We prove that combining it with SimplE, a recent model for static KG embedding, results in a fully expressive model for temporal KG completion. Our experiments indicate the superiority of our proposal compared to existing baselines.
Statistical relational AI (StaRAI) @cite_9 @cite_13 approaches are mainly based on soft (hanf-crafted or learned) rules @cite_1 @cite_26 @cite_57 @cite_62 where the probability of a world is typically proportional to the number of rules that are satisfied violated in that world and the confidence for each rule. A line of work in this area combines a stack of soft rules with embeddings for property prediction @cite_30 @cite_2 . Another line of work extends the soft rules to temporal KGs @cite_32 @cite_45 @cite_33 @cite_37 @cite_19 @cite_14 . The approaches based on soft rules have been generally shown to perform subpar to KG embedding models @cite_7 .
@cite_30 @cite_37 @cite_26 @cite_62 @cite_33 @cite_14 @cite_7 @cite_9 @cite_1 @cite_32 @cite_57 @cite_19 @cite_45 @cite_2 @cite_13
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We propose a method combining relational-logic representations with neural network learning. A general lifted architecture, possibly reflecting some background domain knowledge, is described through relational rules which may be handcrafted or learned. The relational rule-set serves as a template for unfolding possibly deep neural networks whose structures also reflect the structures of given training or testing relational examples. Different networks corresponding to different examples share their weights, which co-evolve during training by stochastic gradient descent algorithm. The framework allows for hierarchical relational modeling constructs and learning of latent relational concepts through shared hidden layers weights corresponding to the rules. Discovery of notable relational concepts and experiments on 78 relational learning benchmarks demonstrate favorable performance of the method. A probabilistic temporal knowledge base contains facts that are annotated with a time interval and a confidence score. The interval defines the time span for which it can be assumed that the fact is true with a probability that is expressed by the confidence score. Given a probabilistic temporal knowledge base, we propose the use of Markov Logic in combination with Allen’s interval calculus to select the most probable consistent subset of facts by computing the MAP state. We apply our approach on a specific domain of DBpedia, namely the domain of academics. We simulate a scenario of extending a knowledge base automatically in an open setting by adding erroneous facts to the facts stated in DBpedia. Our results in- dicate that we can eliminate a large fraction of these errors without removing too many correctly stated facts. We introduce ProbLog, a probabilistic extension of Prolog. A ProbLog program defines a distribution over logic programs by specifying for each clause the probability that it belongs to a randomly sampled program, and these probabilities are mutually independent. The semantics of ProbLog is then defined by the success probability of a query, which corresponds to the probability that the query succeeds in a randomly sampled program. The key contribution of this paper is the introduction of an effective solver for computing success probabilities. It essentially combines SLD-resolution with methods for computing the probability of Boolean formulae. Our implementation further employs an approximation algorithm that combines iterative deepening with binary decision diagrams. We report on experiments in the context of discovering links in real biological networks, a demonstration of the practical usefulness of the approach. Logistic regression is a commonly used representation for aggregators in Bayesian belief networks when a child has multiple parents. In this paper we consider extending logistic regression to relational models, where we want to model varying populations and interactions among parents. In this paper, we first examine the representational problems caused by population variation. We show how these problems arise even in simple cases with a single parametrized parent, and propose a linear relational logistic regression which we show can represent arbitrary linear (in population size) decision thresholds, whereas the traditional logistic regression cannot. Then we examine representing interactions among the parents of a child node, and representing non-linear dependency on population size. We propose a multi-parent relational logistic regression which can represent interactions among parents and arbitrary polynomial decision thresholds. Finally, we show how other well-known aggregators can be represented using this relational logistic regression. Temporal annotations of facts are a key component both for building a high-accuracy knowledge base and for answering queries over the resulting temporal knowledge base with high precision and recall. In this paper, we present a temporal-probabilistic database model for cleaning uncertain temporal facts obtained from information extraction methods. Specifically, we consider a combination of temporal deduction rules, temporal consistency constraints and probabilistic inference based on the common possible-worlds semantics with data lineage, and we study the theoretical properties of this data model. We further develop a query engine which is capable of scaling to very large temporal knowledge bases, with nearly interactive query response times over millions of uncertain facts and hundreds of thousands of grounded rules. Our experiments over two real-world datasets demonstrate the increased robustness of our approach compared to related techniques based on constraint solving via Integer Linear Programming (ILP) and probabilistic inference via Markov Logic Networks (MLNs). We are also able to show that our runtime performance is more than competitive to current ILP solvers and the fastest available, probabilistic but non-temporal, database engines. Time-wise knowledge is relevant in knowledge graphs as the majority facts are true in some time period, for instance, (Barack Obama, president of, USA, 2009, 2017). Consequently, temporal information extraction and temporal scoping of facts in knowledge graphs have been a focus of recent research. Due to this, a number of temporal knowledge graphs have become available such as YAGO and Wikidata. In addition, since the temporal facts are obtained from open text, they can be weighted, i.e., the extraction tools assign each fact with a confidence score indicating how likely that fact is to be true. Temporal facts coupled with confidence scores result in a probabilistic temporal knowledge graph. In such a graph, probabilistic query evaluation (marginal inference) and computing most probable explanations (MPE inference) are fundamental problems. In addition, in these problems temporal coalescing, an important research in temporal databases, is very challenging. In this work, we study these problems by using probabilistic programming. We report experimental results comparing the efficiency of several state of the art systems. Relational machine learning studies methods for the statistical analysis of relational, or graph-structured, data. In this paper, we provide a review of how such statistical models can be “trained” on large knowledge graphs, and then used to predict new facts about the world (which is equivalent to predicting new edges in the graph). In particular, we discuss two fundamentally different kinds of statistical relational models, both of which can scale to massive data sets. The first is based on latent feature models such as tensor factorization and multiway neural networks. The second is based on mining observable patterns in the graph. We also show how to combine these latent and observable models to get improved modeling power at decreased computational cost. Finally, we discuss how such statistical models of graphs can be combined with text-based information extraction methods for automatically constructing knowledge graphs from the Web. To this end, we also discuss Google's knowledge vault project as an example of such combination. An intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with uncertainty. Uncertainty has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations. The book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extension of Bayesian networks. We propose a simple approach to combining first-order logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a first-order knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects in the domain, it specifies a ground Markov network containing one feature for each possible grounding of a first-order formula in the KB, with the corresponding weight. Inference in MLNs is performed by MCMC over the minimal subset of the ground network required for answering the query. Weights are efficiently learned from relational databases by iteratively optimizing a pseudo-likelihood measure. Optionally, additional clauses are learned using inductive logic programming techniques. Experiments with a real-world database and knowledge base in a university domain illustrate the promise of this approach. Recent research has shown that surprisingly rich models of human behavior can be learned from GPS (positional) data. However, most research to date has concentrated on modeling single individuals or aggregate statistical properties of groups of people. Given noisy real-world GPS data, we—in contrast—consider the problem of modeling and recognizing activities that involve multiple related individuals playing a variety of roles. Our test domain is the game of capture the flag—an outdoor game that involves many distinct cooperative and competitive joint activities. We model the domain using Markov logic, a statistical relational language, and learn a theory that jointly denoises the data and infers occurrences of high-level activities, such as capturing a player. Our model combines constraints imposed by the geometry of the game area, the motion model of the players, and by the rules and dynamics of the game in a probabilistically and logically sound fashion. We show that while it may be impossible to directly detect a multi-agent activity due to sensor noise or malfunction, the occurrence of the activity can still be inferred by considering both its impact on the future behaviors of the people involved as well as the events that could have preceded it. We compare our unified approach with three alternatives (both probabilistic and nonprobabilistic) where either the denoising of the GPS data and the detection of the high-level activities are strictly separated, or the states of the players are not considered, or both. We show that the unified approach with the time window spanning the entire game, although more computationally costly, is significantly more accurate. Probabilistic soft logic (PSL) is a framework for collective, probabilistic reasoning in relational domains. PSL uses first order logic rules as a template language for graphical models over random variables with soft truth values from the interval [0, 1]. Inference in this setting is a continuous optimization task, which can be solved efficiently. This paper provides an overview of the PSL language and its techniques for inference and weight learning. An implementation of PSL is available at http: psl.umiacs.umd.edu . The management of uncertainty is crucial when harvesting structured content from unstructured and noisy sources. Knowledge Graphs ( KGs ) are a prominent example. KGs maintain both numerical and non-numerical facts, with the support of an underlying schema. These facts are usually accompanied by a confidence score that witnesses how likely is for them to hold. Despite their popularity, most of existing KGs focus on static data thus impeding the availabilityof timewise knowledge. What is missing is a comprehensive solution for the management of uncertain and temporal data in KGs . The goal of this paper is to fill this gap. We rely on two main ingredients. The first is a numerical extension of Markov Logic Networks (MLNs) that provide the necessary underpinning to formalize the syntax and semantics of uncertain temporal KGs . The second is a set of Datalog constraints with inequalities that extend the underlying schema of the KGs and help to detect inconsistencies. From a theoretical point of view, we discuss the complexity of two important classes of queries for uncertain temporal KGs: maximuma-posteriori and conditional probability inference. Due to the hardness of these problems and the fact that MLN solvers do not scale well, we also explore the usage of Probabilistic Soft Logics (PSL) as a practical tool to support our reasoning tasks. We report on an experimental evaluation comparing the MLN and PSL approaches. Markov logic is a widely used tool in statistical relational learning, which uses a weighted first-order logic knowledge base to specify a Markov random field (MRF) or a conditional random field (CRF). In many applications, a Markov logic network (MLN) is trained in one domain, but used in a different one. This paper focuses on dynamic Markov logic networks, where the size of the discretized time-domain typically varies between training and testing. It has been previously pointed out that the marginal probabilities of truth assignments to ground atoms can change if one extends or reduces the domains of predicates in an MLN. We show that in addition to this problem, the standard way of unrolling a Markov logic theory into a MRF may result in time-inhomogeneity of the underlying Markov chain. Furthermore, even if these representational problems are not significant for a given domain, we show that the more practical problem of generating samples in a sequential conditional random field for the next slice relying on the samples from the previous slice has high computational cost in the general case, due to the need to estimate a normalization factor for each sample. We propose a new discriminative model, slice normalized dynamic Markov logic networks (SN-DMLN), that suffers from none of these issues. It supports efficient online inference, and can directly model influences between variables within a time slice that do not have a causal direction, in contrast with fully directed models (e.g., DBNs). Experimental results show an improvement in accuracy over previous approaches to online inference in dynamic Markov logic networks.