[PDF] Top 20 Feature Rich Networks for Knowledge Base Completion

... Knowledge Bases (KB) are an important resource for many applications such as question answer- ing (Reddy et al., 2014), relation extraction (Mintz et al., 2009) and named entity recognition (Ling and Weld, 2012). ... See full document

... Feature computation. Once path features are selected, the next step is to compute their values. Given an entity pair (h, t) and a path π, PRA com- putes the feature value as a random walk proba- bility ... See full document

... Early work on this problem focused on learn- ing symbolic rules. For example, Schoenmack- ers et al. (2010) learns Horn clauses predictive of new binary relations by exhausitively exploring re- lational paths of ... See full document

... using networks to model real-world complex phenomena, it is easy to incur in sit- uations where the existence of the relationship between two entities is ...biological networks representing protein and gene ... See full document

... Previous work has focused on using path in- formation in knowledge graphs for KBC known as path-based inference (Lao et al., 2011; Gard- ner et al., 2014; Neelakantan et al., 2015; Das et al., 2017b), in which a ... See full document

... Recently, convolutional neural networks (CNNs), originally designed for computer vision (LeCun et al., 1998), have significantly received research attention in natural language processing (Collobert et al., 2011; ... See full document

... RTE is a challenging NLP task where the objective is to judge whether a hypothesis H logically follows from premise(s) P . Advances in RTE have positive implications in other areas such as information retrieval, question ... See full document

... Visual feature extraction such as shape, texture, color, face to retrieve the image as consider either global or local ...single feature consideration, (3) Semantic based extraction is based on if semantic ... See full document

... built knowledge base completion models based on vector ...a rich body of work on sense embedding, which allows one surface form of a word to have sense-specific vectors (Neelakantan et ...our ... See full document

... such networks are the connectionist foundation for Harmonic Gram- mar (HG) and Optimality Theory (OT) in Lin- guistics (Smolensky and Legendre, 2006), where the dynamics of a neural network perform opti- mization ... See full document

... The most recent KG embedding models are ConvE (Dettmers et al. 2017) and ConvKB (Nguyen et al. 2017). ConvE was the first model using 2D convolutions over em- beddings of different embedding dimensions, with the hope of ... See full document

... Free- base) or descriptions acquired by using a simple entity linking ...on-the-fly knowledge base completion. Knowledge base completion for commonsense triples In ... See full document

... domain knowledge in the form of labeled features, which encode affinities between features and classes, to constrain a log-linear model on unlabeled data using generalized expectation criteria ...domain ... See full document

... Our methods are similar to past work on KBC (Mintz et al., 2009; Nickel et al., 2011; Lao et al., 2011; Nickel et al., 2012; Riedel et al., 2013; Gardner et al., 2014; West et al., 2014), particu- larly methods based on ... See full document

... these are two-sided, unconstrained random walks: the walks from sources and targets can be joined on intermediate nodes to get a larger set of paths that connect the source and target nodes. Once connectivity statistics ... See full document

... To understand what the model has learned in the shared memory in the KBC tasks, in Table 5, we visualize the shared memory in an IRN trained from FB15k. We compute the average attention scores of each relation type on ... See full document

... radio networks (CRNs) are composed of cognitive devices capable of changing their configurations on Real time, based on the spectrum ...spectrum knowledge base, and local spectrum knowledge ... See full document

... To overcome this drawback, we construct our train and test set by considering two snapshots of the knowledge base. The train snapshot is taken from an earlier time without special treatment. The test ... See full document

... Matrix completion aims to recover a large matrix of which only a small fraction of entries are ...abundant feature information is ...matrix completion task should be greatly facilitated by such ... See full document

... commonsense knowledge base completion (Li et ...edge base, evaluating the model’s performance on a held-out test set from the same ...sense knowledge, to train and validate their mod- ... See full document