Relational learning on temporal knowledge graphs

Over the last decade, there has been an increasing interest in relational machine learning (RML), which studies methods for the statistical analysis of relational or graph-structured data. Relational data arise naturally in many real-world applications, including social networks, recommender systems, and computational finance. Such data can be represented in the form of a graph consisting of nodes (entities) and labeled edges (relationships between entities). While traditional machine learning techniques are based on feature vectors, RML takes relations into account and permits inference among entities. Recently, performing prediction and learning tasks on knowledge graphs has become a main topic in RML. Knowledge graphs (KGs) are widely used resources for studying multi-relational data in the form of a directed graph, where each labeled edge describes a factual statement, such as (Munich, locatedIn, Germany). Traditionally, knowledge graphs are considered to represent stationary relationships, which do not change over time. In contrast, event-based multi-relational data exhibits complex temporal dynamics in addition to its multi-relational nature. For example, the political relationship between two countries would intensify because of trade fights; the president of a country may change after an election. To represent the temporal aspect, temporal knowledge graphs (tKGs) were introduced that store a temporal event as a quadruple by extending the static triple with a timestamp describing when this event occurred, i.e. (Barack Obama, visit, India, 2010-11-06). Thus, each edge in the graph has temporal information associated with it and may recur or evolve over time. Among various learning paradigms on KGs, knowledge representation learning (KRL), also known as knowledge graph embedding, has achieved great success. KRL maps entities and relations into low-dimensional vector spaces while capturing semantic meanings. However, KRL approaches have mostly been done for static KGs and lack the ability to utilize rich temporal dynamics available on tKGs. In this thesis, we study state-of-the-art representation learning techniques for temporal knowledge graphs that can capture temporal dependencies across entities in addition to their relational dependencies. We discover representations for two inference tasks, i.e., tKG forecasting and completion. The former is to forecast future events using historical observations up to the present time, while the latter predicts missing links at observed timestamps. For tKG forecasting, we show how to make the reasoning process interpretable while maintaining performance by employing a sequential reasoning process over local subgraphs. Besides, we propose a continuous-depth multi-relational graph neural network with a novel graph neural ordinary differential equation. It allows for learning continuous-time representations of tKGs, especially in cases with observations in irregular time intervals, as encountered in online analysis. For tKG completion, we systematically review multiple benchmark models. We thoroughly investigate the significance of the proposed temporal encoding technique in each model and provide the first unified open-source framework, which gathers the implementations of well-known tKG completion models. Finally, we discuss the power of geometric learning and show that learning evolving entity representations in a product of Riemannian manifolds can better reflect geometric structures on tKGs and achieve better performances than Euclidean embeddings while requiring significantly fewer model parameters

Transforming Graph Representations for Statistical Relational Learning

Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of statistical relational learning (SRL) algorithms to these domains. In this article, we examine a range of representation issues for graph-based relational data. Since the choice of relational data representation for the nodes, links, and features can dramatically affect the capabilities of SRL algorithms, we survey approaches and opportunities for relational representation transformation designed to improve the performance of these algorithms. This leads us to introduce an intuitive taxonomy for data representation transformations in relational domains that incorporates link transformation and node transformation as symmetric representation tasks. In particular, the transformation tasks for both nodes and links include (i) predicting their existence, (ii) predicting their label or type, (iii) estimating their weight or importance, and (iv) systematically constructing their relevant features. We motivate our taxonomy through detailed examples and use it to survey and compare competing approaches for each of these tasks. We also discuss general conditions for transforming links, nodes, and features. Finally, we highlight challenges that remain to be addressed

Representation Independent Analytics Over Structured Data

Database analytics algorithms leverage quantifiable structural properties of the data to predict interesting concepts and relationships. The same information, however, can be represented using many different structures and the structural properties observed over particular representations do not necessarily hold for alternative structures. Thus, there is no guarantee that current database analytics algorithms will still provide the correct insights, no matter what structures are chosen to organize the database. Because these algorithms tend to be highly effective over some choices of structure, such as that of the databases used to validate them, but not so effective with others, database analytics has largely remained the province of experts who can find the desired forms for these algorithms. We argue that in order to make database analytics usable, we should use or develop algorithms that are effective over a wide range of choices of structural organizations. We introduce the notion of representation independence, study its fundamental properties for a wide range of data analytics algorithms, and empirically analyze the amount of representation independence of some popular database analytics algorithms. Our results indicate that most algorithms are not generally representation independent and find the characteristics of more representation independent heuristics under certain representational shifts

Relational Representations in Reinforcement Learning: Review and Open Problems

This paper is about representation in RL.We discuss some of the concepts in representation and generalization in reinforcement learning and argue for higher-order representations, instead of the commonly used propositional representations. The paper contains a small review of current reinforcement learning systems using higher-order representations, followed by a brief discussion. The paper ends with research directions and open problems.\u

Semantics representation in a sentence with concept relational model (CRM)

The current way of representing semantics or meaning in a sentence is by using the conceptual graphs. Conceptual graphs define concepts and conceptual relations loosely. This causes ambiguity because a word can be classified as a concept or relation. Ambiguity disrupts the process of recognizing graphs similarity, rendering difficulty to multiple graphs interaction. Relational flow is also altered in conceptual graphs when additional linguistic information is input. Inconsistency of relational flow is caused by the bipartite structure of conceptual graphs that only allows the representation of connection between concept and relations but never between relations per se. To overcome the problem of ambiguity, the concept relational model (CRM) described in this article strictly organizes word classes into three main categories; concept, relation and attribute. To do so, CRM begins by tagging the words in text and proceeds by classifying them according to a predefi ned mapping. In addition, CRM maintains the consistency of the relational flow by allowing connection between multiple relations as well. CRM then uses a set of canonical graphs to be worked on these newly classified components for the representation of semantics. The overall result is better accuracy in text engineering related task like relation extraction

kLog: A Language for Logical and Relational Learning with Kernels

We introduce kLog, a novel approach to statistical relational learning. Unlike standard approaches, kLog does not represent a probability distribution directly. It is rather a language to perform kernel-based learning on expressive logical and relational representations. kLog allows users to specify learning problems declaratively. It builds on simple but powerful concepts: learning from interpretations, entity/relationship data modeling, logic programming, and deductive databases. Access by the kernel to the rich representation is mediated by a technique we call graphicalization: the relational representation is first transformed into a graph --- in particular, a grounded entity/relationship diagram. Subsequently, a choice of graph kernel defines the feature space. kLog supports mixed numerical and symbolic data, as well as background knowledge in the form of Prolog or Datalog programs as in inductive logic programming systems. The kLog framework can be applied to tackle the same range of tasks that has made statistical relational learning so popular, including classification, regression, multitask learning, and collective classification. We also report about empirical comparisons, showing that kLog can be either more accurate, or much faster at the same level of accuracy, than Tilde and Alchemy. kLog is GPLv3 licensed and is available at http://klog.dinfo.unifi.it along with tutorials