Knowledge Base (KB) completion, which aims to determine missing relation between entities, has raised increasing attention in recent years. Most existing methods either focus on the positional relationship between entity pair and single relation (1-hop path) in semantic space or concentrate on the joint probability of Random Walks on multi-hop paths among entities. However, they do not fully consider the intrinsic relationships of all the links among entities. By observing that the single relation and multi-hop paths between the same entity pair generally contain shared similar semantic information, this paper proposes a novel method to capture the shared features between them as the basis for inferring missing relations. To capture the shared features jointly, we develop Hierarchical Attention Networks (HANs) to automatically encode the inputs into low-dimensional vectors, and exploit two partial parameter-shared components, one for feature source discrimination and the other for determining missing relations. By joint Adversarial Training (AT) the entire model, our method minimizes the classification error of missing relations, and ensures the source of shared features are difficult to discriminate in the meantime. The AT mechanism encourages our model to extract features that are both discriminative for missing relation prediction and shareable between single relation and multi-hop paths. We extensively evaluate our method on several large-scale KBs for relation completion. Experimental results show that our method consistently outperforms the baseline approaches. In addition, the hierarchical attention mechanism and the feature extractor in our model can be well interpreted and utilized in the related downstream tasks.
@cite_13: Much work in recent years has gone into the construction of large knowledge bases (KBs), such as Freebase, DBPedia, NELL, and YAGO. While these KBs are very large, they are still very incomplete, necessitating the use of inference to fill in gaps. Prior work has shown how to make use of a large text corpus to augment random walk inference over KBs. We present two improvements to the use of such large corpora to augment KB inference. First, we present a new technique for combining KB relations and surface text into a single graph representation that is much more compact than graphs used in prior work. Second, we describe how to incorporate vector space similarity into random walk inference over KBs, reducing the feature sparsity inherent in using surface text. This allows us to combine distributional similarity with symbolic logical inference in novel and effective ways. With experiments on many relations from two separate KBs, we show that our methods significantly outperform prior work on KB inference, both in the size of problem our methods can handle and in the quality of predictions made.
@cite_23: Knowledge base (KB) completion aims to infer missing facts from existing ones in a KB. Among various approaches, path ranking (PR) algorithms have received increasing attention in recent years. PR algorithms enumerate paths between entity pairs in a KB and use those paths as features to train a model for missing fact prediction. Due to their good performances and high model interpretability, several methods have been proposed. However, most existing methods suffer from scalability (high RAM consumption) and feature explosion (trains on an exponentially large number of features) problems. This paper proposes a Context-aware Path Ranking (C-PR) algorithm to solve these problems by introducing a selective path exploration strategy. C-PR learns global semantics of entities in the KB using word embedding and leverages the knowledge of entity semantics to enumerate contextually relevant paths using bidirectional random walk. Experimental results on three large KBs show that the path features (fewer in number) discovered by C-PR not only improve predictive performance but also are more interpretable than existing baselines.
@cite_6: The path ranking algorithm (PRA) has been recently proposed to address relational classification and retrieval tasks at large scale. We describe Cor-PRA, an enhanced system that can model a larger space of relational rules, including longer relational rules and a class of first order rules with constants, while maintaining scalability. We describe and test faster algorithms for searching for these features. A key contribution is to leverage backward random walks to efficiently discover these types of rules. An empirical study is conducted on the tasks of graph-based knowledge base inference, and person named entity extraction from parsed text. Our results show that learning paths with constants improves performance on both tasks, and that modeling longer paths dramatically improves performance for the named entity extraction task.
@cite_20: Scientific literature with rich metadata can be represented as a labeled directed graph. This graph representation enables a number of scientific tasks such as ad hoc retrieval or named entity recognition (NER) to be formulated as typed proximity queries in the graph. One popular proximity measure is called Random Walk with Restart (RWR), and much work has been done on the supervised learning of RWR measures by associating each edge label with a parameter. In this paper, we describe a novel learnable proximity measure which instead uses one weight per edge label sequence: proximity is defined by a weighted combination of simple "path experts", each corresponding to following a particular sequence of labeled edges. Experiments on eight tasks in two subdomains of biology show that the new learning method significantly outperforms the RWR model (both trained and untrained). We also extend the method to support two additional types of experts to model intrinsic properties of entities: query-independent experts, which generalize the PageRank measure, and popular entity experts which allow rankings to be adjusted for particular entities that are especially important.
Different from knowledge representation methods, PR is a RW based inference technique. Initially, PR is proposed by Lao2010ML to determine the missing edges by planning the optimal RW path between entities and calculating the joint probabilities of selected paths @cite_20 . The innovation points in the follow-up variant methods can be mainly summarized as the contextual information feature extraction of path @cite_23 , the planning of RW @cite_6 , and the optimization of path similarity calculation @cite_13 .