Datadriven construction of SPARQL queries by approximate question graph alignment in question answering over knowledge graphs Abstract   As increasingly more semantic realworld data is stored in knowledge graphs, providing intuitive and effective query methods for endusers is a fundamental and challenging task. Since there is a gap between the plain natural language question (NLQ) and structured data, most RDF questionanswering (QA) systems construct SPARQL queries from NLQs and obtain precise answers from knowledge graphs. A major challenge is how to disambiguate the mapping of phrases and relations in a question to the dataset items, especially in complex questions. In this paper, we propose a novel datadriven graph similarity framework for RDF QA to extract the query graph patterns directly from the knowledge graph instead of constructing them with semantically mapped items. An uncertain question graph is presented to model the interpretations of an NLQ, based on which our problem is reduced to a graph alignment problem. In formulating the alignment, both the lexical and structural similarity of graphs are considered, hence, the target RDF subgraph is used as a query graph pattern to construct the final query. We create a pruned entity graph dynamically based on the complexity of an input question to reduce the search space on the knowledge graph. Moreover, to reduce the calculating cost of the graph similarity, we compute the similarity scores only for samedistance graph elements and equip the process with an edge associationaware surface form extraction method. Empirical studies over real datasets indicate that our proposed approach is flexible and effective as it outperforms stateoftheart methods significantly.

Knowledge Graph Fact Prediction Via KnowledgeEnriched Tensor Factorization Abstract   We present a family of novel methods for embedding knowledge graphs into realvalued tensors. These tensorbased embeddings capture the ordered relations that are typical in the knowledge graphs represented by semantic web languages like RDF. Unlike many previous models, our methods can easily use prior background knowledge provided by users or extracted automatically from existing knowledge graphs. In addition to providing more robust methods for knowledge graph embedding, we provide a provablyconvergent, linear tensor factorization algorithm. We demonstrate the efficacy of our models for the task of predicting new facts across eight different knowledge graphs, achieving between 5% and 50% relative improvement over existing stateoftheart knowledge graph embedding techniques. Our empirical evaluation shows that all of the tensor decomposition models perform well when the average degree of an entity in a graph is high, with constraintbased models doing better on graphs with a small number of highly similar relations and regularizationbased models dominating for graphs with relations of varying degrees of similarity.