Existing question answering methods infer answers either from a knowledge base or from raw text. While knowledge base (KB) methods are good at answering compositional questions, their performance is often affected by the incompleteness of the KB. Au contraire, web text contains millions of facts that are absent in the KB, however in an unstructured form. Universal schema can support reasoning on the union of both structured KBs and unstructured text by aligning them in a common embedded space. In this paper we extend universal schema to natural language question answering, employing to attend to the large body of facts in the combination of text and KB. Our models can be trained in an end-to-end fashion on question-answer pairs. Evaluation results on fill-in-the-blank question answering dataset show that exploiting universal schema for question answering is better than using either a KB or text alone. This model also outperforms the current state-of-the-art by 8.5 @math points. Code and data available in this https URL
We present a method for training a semantic parser using only a knowledge base and an unlabeled text corpus, without any individually annotated sentences. Our key observation is that multiple forms of weak supervision can be combined to train an accurate semantic parser: semantic supervision from a knowledge base, and syntactic supervision from dependency-parsed sentences. We apply our approach to train a semantic parser that uses 77 relations from Freebase in its knowledge representation. This semantic parser extracts instances of binary relations with state-of-the-art accuracy, while simultaneously recovering much richer semantic structures, such as conjunctions of multiple relations with partially shared arguments. We demonstrate recovery of this richer structure by extracting logical forms from natural language queries against Freebase. On this task, the trained semantic parser achieves 80 precision and 56 recall, despite never having seen an annotated logical form. One of the major challenges for automated question answering over Knowledge Bases (KBQA) is translating a natural language question to the Knowledge Base (KB) entities and predicates. Previous systems have used a limited amount of training data to learn a lexicon that is later used for question answering. This approach does not make use of other potentially relevant text data, outside the KB, which could supplement the available information. We introduce a new system, Text2KB, that enriches question answering over a knowledge base by using external text data. Specifically, we revisit different phases in the KBQA process and demonstrate that text resources improve question interpretation, candidate generation and ranking. Building on a state-of-the-art traditional KBQA system, Text2KB utilizes web search results, community question answering and general text document collection data, to detect question topic entities, map question phrases to KB predicates, and to enrich the features of the candidates derived from the KB. Text2KB significantly improves performance over the baseline KBQA method, as measured on a popular WebQuestions dataset. The results and insights developed in this work can guide future efforts on combining textual and structured KB data for question answering. We propose a novel semantic parsing framework for question answering using a knowledge base. We define a query graph that resembles subgraphs of the knowledge base and can be directly mapped to a logical form. Semantic parsing is reduced to query graph generation, formulated as a staged search problem. Unlike traditional approaches, our method leverages the knowledge base in an early stage to prune the search space and thus simplifies the semantic matching problem. By applying an advanced entity linking system and a deep convolutional neural network model that matches questions and predicate sequences, our system outperforms previous methods substantially, and achieves an F1 measure of 52.5 on the WEBQUESTIONS dataset. We consider the problem of building scalable semantic parsers for Freebase, and present a new approach for learning to do partial analyses that ground as much of the input text as possible without requiring that all content words be mapped to Freebase concepts. We study this problem on two newly introduced large-scale noun phrase datasets, and present a new semantic parsing model and semi-supervised learning approach for reasoning with partial ontological support. Experiments demonstrate strong performance on two tasks: referring expression resolution and entity attribute extraction. In both cases, the partial analyses allow us to improve precision over strong baselines, while parsing many phrases that would be ignored by existing techniques.  Answering natural language questions using the Freebase knowledge base has recently been explored as a platform for advancing the state of the art in open domain semantic parsing. Those efforts map questions to sophisticated meaning representations that are then attempted to be matched against viable answer candidates in the knowledge base. Here we show that relatively modest information extraction techniques, when paired with a webscale corpus, can outperform these sophisticated approaches by roughly 34 relative gain. Knowledge base (KB) completion adds new facts to a KB by making inferences from existing facts, for example by inferring with high likelihood nationality(X,Y) from bornIn(X,Y). Most previous methods infer simple one-hop relational synonyms like this, or use as evidence a multi-hop relational path treated as an atomic feature, like bornIn(X,Z) -> containedIn(Z,Y). This paper presents an approach that reasons about conjunctions of multi-hop relations non-atomically, composing the implications of a path using a recursive neural network (RNN) that takes as inputs vector embeddings of the binary relation in the path. Not only does this allow us to generalize to paths unseen at training time, but also, with a single high-capacity RNN, to predict new relation types not seen when the compositional model was trained (zero-shot learning). We assemble a new dataset of over 52M relational triples, and show that our method improves over a traditional classifier by 11 , and a method leveraging pre-trained embeddings by 7 . Much recent work focuses on formal interpretation of natural question utterances, with the goal of executing the resulting structured queries on knowledge graphs (KGs) such as Freebase. Here we address two limitations of this approach when applied to open-domain, entity-oriented Web queries. First, Web queries are rarely wellformed questions. They are “telegraphic”, with missing verbs, prepositions, clauses, case and phrase clues. Second, the KG is always incomplete, unable to directly answer many queries. We propose a novel technique to segment a telegraphic query and assign a coarse-grained purpose to each segment: a base entity e1, a relation type r, a target entity type t2, and contextual words s. The query seeks entity e2 2 t2 where r(e1,e2) holds, further evidenced by schema-agnostic words s. Query segmentation is integrated with the KG and an unstructured corpus where mentions of entities have been linked to the KG. We do not trust the best or any specific query segmentation. Instead, evidence in favor of candidate e2s are aggregated across several segmentations. Extensive experiments on the ClueWeb corpus and parts of Freebase as our KG, using over a thousand telegraphic queries adapted from TREC, INEX, and WebQuestions, show the efficacy of our approach. For one benchmark, MAP improves from 0.2‐0.29 (competitive baselines) to 0.42 (our system). NDCG@10 improves from 0.29‐0.36 to 0.54. Most recent question answering (QA) systems query large-scale knowledge bases (KBs) to answer a question, after parsing and transforming natural language questions to KBs-executable forms (e.g., logical forms). As a well-known fact, KBs are far from complete, so that information required to answer questions may not always exist in KBs. In this paper, we develop a new QA system that mines answers directly from the Web, and meanwhile employs KBs as a significant auxiliary to further boost the QA performance. Specifically, to the best of our knowledge, we make the first attempt to link answer candidates to entities in Freebase, during answer candidate generation. Several remarkable advantages follow: (1) Redundancy among answer candidates is automatically reduced. (2) The types of an answer candidate can be effortlessly determined by those of its corresponding entity in Freebase. (3) Capitalizing on the rich information about entities in Freebase, we can develop semantic features for each answer candidate after linking them to Freebase. Particularly, we construct answer-type related features with two novel probabilistic models, which directly evaluate the appropriateness of an answer candidate's types under a given question. Overall, such semantic features turn out to play significant roles in determining the true answers from the large answer candidate pool. The experimental results show that across two testing datasets, our QA system achieves an 18 54 improvement under F_1 metric, compared with various existing QA systems.  Path queries on a knowledge graph can be used to answer compositional questions such as "What languages are spoken by people living in Lisbon?". However, knowledge graphs often have missing facts (edges) which disrupts path queries. Recent models for knowledge base completion impute missing facts by embedding knowledge graphs in vector spaces. We show that these models can be recursively applied to answer path queries, but that they suffer from cascading errors. This motivates a new "compositional" training objective, which dramatically improves all models' ability to answer path queries, in some cases more than doubling accuracy. On a standard knowledge base completion task, we also demonstrate that compositional training acts as a novel form of structural regularization, reliably improving performance across all base models (reducing errors by up to 43 ) and achieving new state-of-the-art results.