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Learning Convolutional Neural Networks for Graphs
TLDR
This work proposes a framework for learning convolutional neural networks for arbitrary graphs that operate on locally connected regions of the input and demonstrates that the learned feature representations are competitive with state of the art graph kernels and that their computation is highly efficient. Expand
Learning Sequence Encoders for Temporal Knowledge Graph Completion
TLDR
This work utilizes recurrent neural networks to learn time-aware representations of relation types which can be used in conjunction with existing latent factorization methods to incorporate temporal information. Expand
CODI: Combinatorial Optimization for Data Integration: results for OAEI 2011
TLDR
The system provides a declarative framework for the alignment of individuals, concepts, and properties of two heterogeneous ontologies that leverages both logical schema information and lexical similarity measures with a well-defined semantics for A-Box and T-Box matching. Expand
Learning Discrete Structures for Graph Neural Networks
TLDR
This work proposes to jointly learn the graph structure and the parameters of graph convolutional networks (GCNs) by approximately solving a bilevel program that learns a discrete probability distribution on the edges of the graph. Expand
Statistical Schema Induction
TLDR
This paper presents a statistical approach to the induction of expressive schemas from large RDF repositories and describes in detail the implementation and report on an evaluation that was conducted using several data sets including DBpedia. Expand
Learning Graph Representations with Embedding Propagation
TLDR
Embedding Propagation is an unsupervised learning framework for graph-structured data with significantly fewer parameters and hyperparameters that is competitive with and often outperforms state of the art unsuper supervised and semi-supervisedLearning methods on a range of benchmark data sets. Expand
RockIt: Exploiting Parallelism and Symmetry for MAP Inference in Statistical Relational Models
TLDR
Extensive experiments with Markov logic network (MLN) benchmarks showing that ROCKIT outperforms the state-of-the-art systems ALCHEMY, MARKOV THEBEAST, and TUFFY both in terms of efficiency and quality of results. Expand
Fine-Grained Sentiment Analysis with Structural Features
TLDR
A fully automatic framework for fine-grained sentiment analysis on the subsentence level combining multiple sentiment lexicons and neighborhood as well as discourse relations to overcome the problem of uncertainty in polarity predictions is presented. Expand
KBlrn: End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical Features
TLDR
KBLRN is the first approach that learns representations of knowledge bases by integrating latent, relational, and numerical features, and it is shown that instances of KBLRN outperform existing methods on a range of knowledge base completion tasks. Expand
A Probabilistic-Logical Framework for Ontology Matching
TLDR
A novel probabilistic-logical framework for ontology matching based on Markov logic is presented that has several advantages over existing methods such as ease of experimentation, incoherence mitigation during the alignment process, and the incorporation of a-priori confidence values. Expand
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