Robust question answering over the web of linked data

Yahya, Mohamed; Berberich, Klaus; Elbassuoni, Shady; Weikum, Gerhard

doi:10.1145/2505515.2505677

Cited by 88 publications

(49 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The resulting problem is NP-hard but it is efficiently solvable in approximations by existing ILP solvers. The follow-up approach [147] uses DBpedia and Yago with a mapping of input queries to semantic relations based on text search. At QALD 2, it outperformed almost every other system on factoid questions and every other system on list questions.…”

Section: Ambiguitymentioning

confidence: 99%

Survey on challenges of Question Answering in the Semantic Web

et al. 2017

View full text Add to dashboard Cite

Abstract. Semantic Question Answering (SQA) removes two major access requirements to the Semantic Web: the mastery of a formal query language like SPARQL and knowledge of a specific vocabulary. Because of the complexity of natural language, SQA presents difficult challenges and many research opportunities. Instead of a shared effort, however, many essential components are redeveloped, which is an inefficient use of researcher's time and resources. This survey analyzes 62 different SQA systems, which are systematically and manually selected using predefined inclusion and exclusion criteria, leading to 72 selected publications out of 1960 candidates. We identify common challenges, structure solutions, and provide recommendations for future systems. This work is based on publications from the end of 2010 to July 2015 and is also compared to older but similar surveys.

show abstract

Section: Ambiguitymentioning

confidence: 99%

Survey on challenges of Question Answering in the Semantic Web

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Providing end users an easy-to-use interface to access RDF datasets in an effective way has been recognized as an important concern. This has lead to research for RDF question/answering (Q/A) systems [4,32,33,37]. We have designed gAnswer [37] to address the problem from the perspective of a graph database.…”

Section: Ganswer: Answering Natural Language Questions Using Subgraphmentioning

confidence: 99%

Graph-Based RDF Data Management

Zou

Özsu

2017

Data Sci. Eng.

View full text Add to dashboard Cite

The increasing size of RDF data requires efficient systems to store and query them. There have been efforts to map RDF data to a relational representation, and a number of systems exist that follow this approach. We have been investigating an alternative approach of maintaining the native graph model to represent RDF data, and utilizing graph database techniques (such as a structureaware index and a graph matching algorithm) to address RDF data management. Since 2009, we have been developing a set of graph-based RDF data management systems that follow this approach: gStore, gStore-D and gAnswer. The first two are designed to support efficient SPARQL query evaluation in a centralized and distributed/parallel environments, respectively, while the last one aims to provide an easy-to-use interface (natural language question/answering) for users to access a RDF repository. In this paper, we give an overview of these systems and also discuss our design philosophy.

show abstract

“…Much of prior work translates a natural language question into SPARQL and retrieves answers from a triple store (Lopez et al, 2005;Yahya et al, 2013;; however, SPARQL queries have been criticized to have unsatisfying query response time. In this work, we maintain flexibility by first parsing a question into First Order Logic, which is further translated into both SQL and SPARQL.…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

DeNero

Finlayson

Reddy

2015

View full text Add to dashboard Cite

We describe the design, development, and API for two discourse parsers for Rhetorical Structure Theory. The two parsers use the same underlying framework, but one uses features that rely on dependency syntax, produced by a fast shift-reduce parser, whereas the other uses a richer feature space, including both constituent-and dependency-syntax and coreference information, produced by the Stanford CoreNLP toolkit. Both parsers obtain state-of-the-art performance, and use a very simple API consisting of, minimally, two lines of Scala code. We accompany this code with a visualization library that runs the two parsers in parallel, and displays the two generated discourse trees side by side, which provides an intuitive way of comparing the two parsers. IntroductionThis paper describes the design and development of two practical parsers for Rhetorical Structure Theory (RST) discourse (Mann and Thompson, 1988). This work contributes to the already vast body of research on RST parsing (see, inter alia, Soricut and Marcu, 2003; Feng and Hirst, 2012; Joty et al., 2013, Joty et al., 2014) with the following:1. We propose two parsers that use constituentbased and dependency-based syntax, respectively. The underlying framework, other than the syntax-based features, is identical between the parsers, which permits a rigorous analysis of the impact of constituent and dependency syntax to RST parsing. We describe the parsers in Section 2 and empirically compare the impact of the two syntactic representations in Section 3. Our analysis indicates that both parsers achieve state-of-the-art performance. The parser based on dependency syntax performs marginally worse (by 0.1 F 1 points) but runs approximately 2.5 times faster than the parser based on constituent syntax. On average, the faster parser processes one document from the RST corpus in 2.3 seconds. Both parsers have been released as open-sourceScala code with a very simple API; consisting of, minimally, two lines of code. We discuss this API in Section 4.3. We also introduce a visualization tool that runs the two parsers in parallel, and displays the two generated discourse structures side by side. This allows users to directly compare the runtimes and outputs of the two parsers. This visualization tool will be the centerpiece of the proposed demo session. We summarize this tool in Section 5. The Two ParsersThe proposed parsing approach follows the architecture introduced by Hernault et al. (2010), andFeng and Hirst (2012). The parser first segments the text into elementary discourse units (EDUs) using an i.i.d. classifier that identifies which tokens end an EDU. Then the parser iteratively constructs the discourse tree (consisting of binary relations between discourse units) using a greedy bottom-up approach that interleaves two classifiers: the first de-1 tects which two adjacent discourse units are most likely to be connected given the current sequence of units; and the second labels the corresponding relation. The resulting discourse unit produced by the new rel...

show abstract

Robust question answering over the web of linked data

Cited by 88 publications

References 39 publications

Survey on challenges of Question Answering in the Semantic Web

Survey on challenges of Question Answering in the Semantic Web

Graph-Based RDF Data Management

Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

Contact Info

Product

Resources

About