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Book ChapterDOI

A Constraint Based Question Answering over Semantic Knowledge Base

01 Jan 2016-pp 121-131

TL;DR: The proposed system aims at extracting meaning from the natural language query for querying the semantic knowledge sources and the system is compared with other systems of QALD (Question Answering over Linked Data) standard.
Abstract: The proposed system aims at extracting meaning from the natural language query for querying the semantic knowledge sources. Semantic knowledge sources are systems conceptualized with Ontology. Characterization of a concept is through other concepts as a constraint over other. This very method to extract meaning from the natural language query has been experimented in this system. Constraints and entities from the query and the relationship between the entities is capable of transforming natural language query to a SPARQL (a query language for Semantic Knowledge sources). Further the SPARQL query is generated through recursive procedure from the intermediate query which is more efficient that mapping with patterns of the question. The system is compared with other systems of QALD (Question Answering over Linked Data) standard.
Topics: Query language (69%), SPARQL (69%), Question answering (62%), Natural language user interface (56%), Ontology (information science) (55%)
References
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Book ChapterDOI
Sören Auer1, Christian Bizer2, Georgi Kobilarov2, Jens Lehmann3  +2 moreInstitutions (3)
11 Nov 2007
TL;DR: The extraction of the DBpedia datasets is described, and how the resulting information is published on the Web for human-andmachine-consumption and how DBpedia could serve as a nucleus for an emerging Web of open data.
Abstract: DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web. DBpedia allows you to ask sophisticated queries against datasets derived from Wikipedia and to link other datasets on the Web to Wikipedia data. We describe the extraction of the DBpedia datasets, and how the resulting information is published on the Web for human-andmachine-consumption. We describe some emerging applications from the DBpedia community and show how website authors can facilitate DBpedia content within their sites. Finally, we present the current status of interlinking DBpedia with other open datasets on the Web and outline how DBpedia could serve as a nucleus for an emerging Web of open data.

4,118 citations


Proceedings ArticleDOI
16 Apr 2012
TL;DR: A novel approach that relies on a parse of the question to produce a SPARQL template that directly mirrors the internal structure of theQuestion answering system, which is then instantiated using statistical entity identification and predicate detection.
Abstract: As an increasing amount of RDF data is published as Linked Data, intuitive ways of accessing this data become more and more important. Question answering approaches have been proposed as a good compromise between intuitiveness and expressivity. Most question answering systems translate questions into triples which are matched against the RDF data to retrieve an answer, typically relying on some similarity metric. However, in many cases, triples do not represent a faithful representation of the semantic structure of the natural language question, with the result that more expressive queries can not be answered. To circumvent this problem, we present a novel approach that relies on a parse of the question to produce a SPARQL template that directly mirrors the internal structure of the question. This template is then instantiated using statistical entity identification and predicate detection. We show that this approach is competitive and discuss cases of questions that can be answered with our approach but not with competing approaches.

467 citations


Proceedings ArticleDOI
04 Nov 2005
TL;DR: A novel information retrieval method is proposed that is capable of detecting similarities between documents containing semantically similar but not necessarily lexicographically similar terms.
Abstract: Semantic Similarity relates to computing the similarity between concepts which are not lexicographically similar. We investigate approaches to computing semantic similarity by mapping terms (concepts) to an ontology and by examining their relationships in that ontology. Some of the most popular semantic similarity methods are implemented and evaluated using WordNet as the underlying reference ontology. Building upon the idea of semantic similarity, a novel information retrieval method is also proposed. This method is capable of detecting similarities between documents containing semantically similar but not necessarily lexicographically similar terms. The proposed method has been evaluated in retrieval of images and documents on the Web. The experimental results demonstrated very promising performance improvements over state-of-the-art information retrieval methods.

349 citations


Journal ArticleDOI
TL;DR: The main goal of the challenge was to get insight into the strengths, capabilities, and current shortcomings of question answering systems as interfaces to query linked data sources, as well as benchmarking how these interaction paradigms can deal with the fact that the amount of RDF data available on the web is very large and heterogeneous with respect to the vocabularies and schemas used.
Abstract: The availability of large amounts of open, distributed, and structured semantic data on the web has no precedent in the history of computer science. In recent years, there have been important advances in semantic search and question answering over RDF data. In particular, natural language interfaces to online semantic data have the advantage that they can exploit the expressive power of Semantic Web data models and query languages, while at the same time hiding their complexity from the user. However, despite the increasing interest in this area, there are no evaluations so far that systematically evaluate this kind of systems, in contrast to traditional question answering and search interfaces to document spaces. To address this gap, we have set up a series of evaluation challenges for question answering over linked data. The main goal of the challenge was to get insight into the strengths, capabilities, and current shortcomings of question answering systems as interfaces to query linked data sources, as well as benchmarking how these interaction paradigms can deal with the fact that the amount of RDF data available on the web is very large and heterogeneous with respect to the vocabularies and schemas used. Here, we report on the results from the first and second of such evaluation campaigns. We also discuss how the second evaluation addressed some of the issues and limitations which arose from the first one, as well as the open issues to be addressed in future competitions.

154 citations


Book ChapterDOI
11 Nov 2012
TL;DR: The main, novel contribution is a systematic empirical investigation of the impact of the single processing components on the overall performance of question answering over linked data.
Abstract: We present a question answering system architecture which processes natural language questions in a pipeline consisting of five steps: i) question parsing and query template generation, ii) lookup in an inverted index, iii) string similarity computation, iv) lookup in a lexical database in order to find synonyms, and v) semantic similarity computation. These steps are ordered with respect to their computational effort, following the idea of layered processing: questions are passed on along the pipeline only if they cannot be answered on the basis of earlier processing steps, thereby invoking computationally expensive operations only for complex queries that require them. In this paper we present an evaluation of the system on the dataset provided by the 2nd Open Challenge on Question Answering over Linked Data (QALD-2). The main, novel contribution is a systematic empirical investigation of the impact of the single processing components on the overall performance of question answering over linked data.

53 citations