2015 AReviewofRelationalMachineLearn

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Subject Headings: Google Knowledge Vault.


  • Pre-published as arXiv:1503.00759 Journal.

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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 different kinds of statistical relational models, both of which can scale to massive datasets. The first is based on tensor factorization methods and related latent variable models. 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. In particular, we discuss Google's Knowledge Vault project.

1. Introduction

Machine learning typically works with a data matrix, where each row represents an object characterized by a feature vector of attributes (which might be numeric or categorical), and where the main tasks are to learn a mapping from this feature vector to an output prediction of some form, or to perform unsupervised learning like clustering or factor analysis. In Statistical Relational Learning (SRL), the representation of an object can contain its relationships to other objects. Thus the data is in the form of a graph, consisting of nodes (entities) and labelled edges (relationships between entities). The main goals of SRL include prediction of missing edges, prediction of properties of the nodes, and clustering the nodes based on their connectivity patterns. These tasks arise in many settings such as analysisof social networks and biological pathways. For further information on SRL see [1, 2, 3].

In this article, we review a variety of techniques from the SRL community and explain how they can be applied to large-scale knowledge graphs (KGs), i.e, graph structured knowledge bases (KBs) which store factual information in form of relationships between entities. Recently, a large number of knowledge graphs have been created, including YAGO [4], DBpedia [5], NELL [6], Freebase [ 7], and the Google Knowledge Graph [8]. As we discuss in Section II, these graphs contain millions of nodes and billions of edges. This causes us to focus on scalable SRL techniques, which take time that is linear in the size of the graph.

In addition to typical applications of statistical relational learning, we will also discuss how SRL can aid information extraction methods to “grow” a KG automatically. In particular, we will show how SRL can be used to train a “prior” model based on an existing KG, and then combine its predictions with “noisy” facts that are automatically extracted from the web using machine reading methods (see e.g., [9, 10]). This is the approach adopted in Google’s Knowledge Vault project, as we explain in Section VIII.

The remainder of this paper is structured as follows. In Section II we introduce knowledge graphs and some of their properties. Section III discusses SRL and how it can be applied to knowledge graphs. There are two main classes of SRL techniques: those that capture the correlation between the nodes / edges using latent variables, and those that capture the correlation directly using statistical models based on the observable properties of the graph. We discuss these two families in Section IV and Section V, respectively. Section VI describes approaches for combining these two approaches, to get the best of both worlds. In Section VII we discuss relational learning using Markov Random Fields. In Section VIII we describe how SRL can be used in automated knowledge base construction projects. In Section IX we discuss extensions of the presented methods, and Section X presents our conclusions.


In this section, we discuss knowledge graphs: how they are represented, how they are created, and how they are used.

A. Knowledge representation

Relational knowledge representation as used in KGs has a long history in logic and artificial intelligence [11]. More recently, it has been used in the Semantic Web to represent information in machine-readable form, in order to enable intelligent agents operating on a “web of data” [12]. While the original vision of the Semantic Web remains to be fully realized, parts of it have been achieved. In particular, the concept of linked data [13, 14] has gained traction, as it facilitates publishing and interlinking data on the Web in relational form using the W3C Resource Description Framework (RDF) [15, 16].

In this article, we will loosely follow the RDF standard and represent facts in the form of binary relationships, in particular (subject, predicate, object) (SPO) triples, where subject and object are entities and predicate is the type of a relation. (We discuss how to represent higher-order relations in Section IX-A.) The existence of a particular SPO triple indicates an existing fact, i.e., that the respective entities are in a relationship of the respective type. For instance, the information Leonard Nimoy was an actor who played the character Spock in the science-fiction movie Star Trek can be expressed via the following set of SPO triples:

subject predicate object 
(LeonardNimoy, profession, Actor) 
(LeonardNimoy, starredIn, StarTrek) 
(LeonardNimoy, played, Spock) 
(Spock, characterIn, StarTrek) 
(StarTrek, genre, ScienceFiction) 

We can combine all the SPO triples together to form a multigraph, where nodes represent entities (all subjects and objects), and directed edges represent relationships. The direction of an edge indicates whether entities occur as subjects or objects, i.e., an edge points from the subject to the object. The different relation types are represented via different types of edges (also called edge labels). This construction is called a knowledge graph (KG), or sometimes a heterogeneous information network [17].) See Figure 1 for an example.

In addition to a collection of facts, knowledge graphs often provide type hierarchies (Leonard Nimoy is an actor, which is a person, which is a living thing) and type constraints (e.g., a person can only marry another person, not a thing).

B. Open vs closed world assumption

While existing triples always encode true relationships (facts), there are different paradigms for the interpretation of nonexisting triples:

For example, the fact that in Figure 1 there is no starredIn edge from Leonard



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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2015 AReviewofRelationalMachineLearnMaximilian Nickel
Kevin P. Murphy
Volker Tresp
Evgeniy Gabrilovich
A Review of Relational Machine Learning for Knowledge Graphs: From Multi-Relational Link Prediction to Automated Knowledge Graph Construction2015
AuthorMaximilian Nickel +, Kevin Murphy +, Volker Tresp + and Evgeniy Gabrilovich +
titleA Review of Relational Machine Learning for Knowledge Graphs: From Multi-Relational Link Prediction to Automated Knowledge Graph Construction +
year2015 +