Template-Based Question Answering over Linked Geospatial Data

Punjani, Dharmen; Singh, K. M.; Both, Andreas; Koubarakis, Manolis; Angelidis, Iosif; Bereta, Konstantina; Beris, Themis; Bilidas, Dimitris; Ioannidis, Theofilos; Karalis, Nikolaos; Lange, Christoph; Pantazi, Despina-Athanasia; Papaloukas, Christos; Stamoulis, George

doi:10.1145/3281354.3281362

Cited by 45 publications

(68 citation statements)

References 41 publications

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“…To convert an input question into a SPARQL query, TBSL , QUINT , GeoQA , LODQA , and the system proposed by Biermann and his colleagues all utilize template‐based approaches. In such approaches, templates are generated based on linguistic analysis of input questions.…”

Section: Related Workmentioning

confidence: 99%

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Ontology‐lexicon–based question answering over linked data

2020

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Recently, Linked Open Data has become a large set of knowledge bases. Therefore, the need to query Linked Data using question answering (QA) techniques has attracted the attention of many researchers. A QA system translates natural language questions into structured queries, such as SPARQL queries, to be executed over Linked Data. The two main challenges in such systems are lexical and semantic gaps. A lexical gap refers to the difference between the vocabularies used in an input question and those used in the knowledge base. A semantic gap refers to the difference between expressed information needs and the representation of the knowledge base. In this paper, we present a novel method using an ontology lexicon and dependency parse trees to overcome lexical and semantic gaps. The proposed technique is evaluated on the QALD‐5 benchmark and exhibits promising results.

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Section: Related Workmentioning

confidence: 99%

“…Some QA systems are limited to a specific domain, such as medicine, agriculture, or geography . In such systems, there is less ambiguity.…”

Section: Related Workmentioning

confidence: 99%

Ontology‐lexicon–based question answering over linked data

2020

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“…We extracted a new export, covering entire Europe, and made the dataset queriable by QAnswer. Note that there is no online demo querying geo-spatial data and we are aware of only one work tackling this problem, which is presented in [7]. Some example requests that can be answered using this dataset are:…”

Section: Descriptionmentioning

confidence: 99%

QAnswer: A Question Answering prototype bridging the gap between a considerable part of the LOD cloud and end-users

Diefenbach

Migliatti

Qawasmeh

et al. 2019

The World Wide Web Conference

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We present QAnswer, a Question Answering system which queries at the same time 3 core datasets of the Semantic Web, that are relevant for end-users. These datasets are Wikidata with Lexemes, LinkedGeodata and Musicbrainz. Additionally, it is possible to query these datasets in English, German, French, Italian, Spanish, Pourtuguese, Arabic and Chinese. Moreover, QAnswer includes a fallback option to the search engine Qwant when the answer to a question cannot be found in the datasets mentioned above. These features make QAnswer as the first prototype of a Question Answering System over a considerable part of the LOD cloud.

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“…In principle, geo-analytic questions may be answered by QA systems that rely on retrieval of factual knowledge from documents or knowledge bases [6,20,21,25]. However, it is quite unlikely that answers to geo-analytic questions are known a-priori and, therefore, such answers will probably not be accessible through information retrieval.…”

Section: Introductionmentioning

confidence: 99%

“…Question corpora provide means for analyzing question structures from a syntactic as well as semantic viewpoint [31]. Current efforts focus on the collection and analysis of Web queries [15,22] and directly answerable questions [6,25] for QA systems. However, corpora specific to geo-analytic questions are not available, and it is therefore impossible to assess how the complexity of geo-analytic questions differs from generic questions with a geographic component, as asked e.g.…”

Section: Introductionmentioning

confidence: 99%

Extracting interrogative intents and concepts from geo-analytic questions

Hamzei

Nyamsuren

et al. 2020

AGILE GIScience Ser.

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Abstract. Understanding syntactic and semantic structure of geographic questions is a necessary step towards true geographic question-answering (GeoQA) machines. The empirical basis for the understanding of the capabilities expected from GeoQA systems are geographic question corpora. Available corpora in English have been mostly drawn from generic Web search logs or limited user studies, supporting the focus of GeoQA systems on retrieving factoids: factual knowledge about particular places and everyday processes. Yet, the majority of questions enquired about in the spatial sciences go beyond simple place facts, with more complex analytical intents informing the questions. In this paper, we introduce a new corpus of geo-analytic questions drawn from English textbooks and scientific articles. We analyse and compare this corpus with two general-purpose GeoQA corpora in terms of grammatical complexity and semantic concepts, using a new parsing method that allows us to differentiate and quantify patterns of a question’s intent.

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Template-Based Question Answering over Linked Geospatial Data

Cited by 45 publications

References 41 publications

Ontology‐lexicon–based question answering over linked data

Ontology‐lexicon–based question answering over linked data

QAnswer: A Question Answering prototype bridging the gap between a considerable part of the LOD cloud and end-users

Extracting interrogative intents and concepts from geo-analytic questions

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