Querying Multilingual DBpedia with QAKiS

Cabrio, Elena; Cojan, Julien; Gandon, Fabien; Hallili, Amine

doi:10.1007/978-3-642-41242-4_23

Cited by 23 publications

(18 citation statements)

References 4 publications

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“…Examples are the BOA pattern library [6] (used, e.g., in TBSL [13]) and the WikiFramework repository [10] (used, e.g. in QUAKiS [3]). Both go beyond semantic similarity measures in also involving co-occurence patterns.…”

Section: Discussion and Related Workmentioning

confidence: 99%

“…To give an idea of the various levels of difficulty, consider the following three questions taken from the DBpedia training questions of the 2nd Open Challenge on Question Answering over Linked Data (QALD-2, see Section 3.1 below): 3 Question 1a exemplifies the simplest case: All natural language expressions can be mapped to DBpedia resources in the target SPARQL query 1b by simply matching the expressions (currency and Czech Republic) with the resources' labels, in this case currency and Czech Republic. Furthermore, the resources are directly related, so that the SPARQL query consists of one triple relating the entity Czech Republic with its currency.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of a Layered Approach to Question Answering over Linked Data

Walter

Unger

Cimiano

et al. 2012

The Semantic Web – ISWC 2012

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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.

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Section: Discussion and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of a Layered Approach to Question Answering over Linked Data

Walter

Unger

Cimiano

et al. 2012

The Semantic Web – ISWC 2012

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“…QAKiS [24,35,26] queries several multilingual versions of DBpedia at the same time by filling the produced SPARQL query with the corresponding language-dependent properties and classes. Thus, QAKiS can retrieve correct answers even in cases of missing information in the language-dependent knowledge base.…”

Section: Systemsmentioning

confidence: 99%

Survey on challenges of Question Answering in the Semantic Web

et al. 2017

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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.

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“…Cabrio et al [2] present a demo of QAKiS, an agnostic QA system grounded in ontology-relation matches. The relation matches are based on surface forms extracted from Wikipedia to enforce a wide variety of context matches, e.g., a relation birthplace(person, place) can be explicated by X was born in Y or Y is the birthplace of X.…”

Section: Question Answeringmentioning

confidence: 99%

HAWK – Hybrid Question Answering Using Linked Data

Usbeck

Ngomo

Bühmann

et al. 2015

Lecture Notes in Computer Science

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Abstract. The decentral architecture behind the Web has led to pieces of information being distributed across data sources with varying structure. Hence, answering complex questions often required combining information from structured and unstructured data sources. We present HAWK, a novel entity search approach for Hybrid Question Answering based on combining Linked Data and textual data. The approach uses predicate-argument representations of questions to derive equivalent combinations of SPARQL query fragments and text queries. These are executed so as to integrate the results of the text queries into SPARQL and thus generate a formal interpretation of the query. We present a thorough evaluation of the framework, including an analysis of the influence of entity annotation tools on the generation process of the hybrid queries and a study of the overall accuracy of the system. Our results show that HAWK achieves 0.68 respectively 0.61 F-measure within the training respectively test phases on the Question Answering over Linked Data (QALD-4) hybrid query benchmark.

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Querying Multilingual DBpedia with QAKiS

Cited by 23 publications

References 4 publications

Evaluation of a Layered Approach to Question Answering over Linked Data

Evaluation of a Layered Approach to Question Answering over Linked Data

Survey on challenges of Question Answering in the Semantic Web

HAWK – Hybrid Question Answering Using Linked Data

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