Estimating the confidence for a link is a critical task for Knowledge Graph construction. Link prediction, or predicting the likelihood of a link in a knowledge graph based on prior state is a key research direction within this area. We propose a Latent Feature Embedding based link recommendation model for prediction task and utilize Bayesian Personalized Ranking based optimization technique for learning models for each predicate. Experimental results on large-scale knowledge bases such as YAGO2 show that our approach achieves substantially higher performance than several state-of-art approaches. Furthermore, we also study the performance of the link prediction algorithm in terms of topological properties of the Knowledge Graph and present a linear regression model to reason about its expected level of accuracy.
It has been demonstrated @cite_16 that no single approach emerges as a clear winner. Instead, the merits of factorization models and graph feature models are often complementary with each other. Thus combining the advantages of different approaches for learning knowledge graph is a promising option. For instance, @cite_24 proposes to use additive model, which is a linear combination between RESCAL and PRA. The combination results in not only decrease the training time but also increase the accuracy. @cite_7 combines a latent feature model with an additive term to learn from latent and neighborhood-based information on multi-relational data. @cite_4 fuses the outputs of PRA and neural network model as features for training a binary classifier. Our work strongly aligns with this combination approach. In this work, we build matrix factorization based techniques that have been proved successful for recommender systems and plan to incorporate graph based features in future work.
@cite_24 @cite_16 @cite_4 @cite_7
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Tensor factorization has become a popular method for learning from multi-relational data. In this context, the rank of the factorization is an important parameter that determines runtime as well as generalization ability. To identify conditions under which factorization is an efficient approach for learning from relational data, we derive upper and lower bounds on the rank required to recover adjacency tensors. Based on our findings, we propose a novel additive tensor factorization model to learn from latent and observable patterns on multi-relational data and present a scalable algorithm for computing the factorization. We show experimentally both that the proposed additive model does improve the predictive performance over pure latent variable methods and that it also reduces the required rank — and therefore runtime and memory complexity — significantly. Recent years have witnessed a proliferation of large-scale knowledge graphs, from purely academic projects such as YAGO to major commercial projects such as Google's Knowledge Graph and Microsoft's Satori. Whereas there is a large body of research on mining homogeneous graphs, this new generation of information networks are highly heterogeneous, with thousands of entity and relation types and billions of instances of those types (graph vertices and edges). In this tutorial, we present the state of the art in constructing, mining, and growing knowledge graphs. The purpose of the tutorial is to equip newcomers to this exciting field with an understanding of the basic concepts, tools and methodologies, open research challenges, as well as pointers to available datasets and relevant literature. Knowledge graphs have become an enabling resource for a plethora of new knowledge-rich applications. Consequently, the tutorial will also discuss the role of knowledge bases in empowering a range of web applications, from web search to social networks to digital assistants. A publicly available knowledge base (Freebase) will be used throughout the tutorial to exemplify the different techniques. Recent years have witnessed a proliferation of large-scale knowledge bases, including Wikipedia, Freebase, YAGO, Microsoft's Satori, and Google's Knowledge Graph. To increase the scale even further, we need to explore automatic methods for constructing knowledge bases. Previous approaches have primarily focused on text-based extraction, which can be very noisy. Here we introduce Knowledge Vault, a Web-scale probabilistic knowledge base that combines extractions from Web content (obtained via analysis of text, tabular data, page structure, and human annotations) with prior knowledge derived from existing knowledge repositories. We employ supervised machine learning methods for fusing these distinct information sources. The Knowledge Vault is substantially bigger than any previously published structured knowledge repository, and features a probabilistic inference system that computes calibrated probabilities of fact correctness. We report the results of multiple studies that explore the relative utility of the different information sources and extraction methods. We present a general and novel framework for predicting links in multirelational graphs using a set of matrices describing the various instantiated relations in the knowledge base. We construct matrices that add information further remote in the knowledge graph by join operations and we describe how unstructured information can be integrated in the model. We show that efficient learning can be achieved using an alternating least squares approach exploiting sparse matrix algebra and low-rank approximations. We discuss the relevance of modeling nonlinear interactions and add corresponding model components. We also discuss a kernel solution which is of interest when it is easy to define sensible kernels. We discuss the relevance of feature selection for the interaction terms and apply a random search strategy to tune the hyperparameters in the model. We validate our approach using data sets from the Linked Open Data (LOD) cloud and from other sources.