Multilingual online generation from semantic web ontologies

Dannélls, Dana; Damova, Mariana; Enache, Ramona; Chechev, Milen

doi:10.1145/2187980.2188018

Cited by 23 publications

(14 citation statements)

References 4 publications

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“…Most of the previous work on NLG with Semantic Web data has focused on the verbalisation of domain ontologies by using rules. Examples include systems that generate text in domains with limited linguistic variability, such as clinical narratives [14], summaries of football matches [2], and, descriptions of museum's exhibits [3]. Further Semantic Web oriented NLG applications can be found in [4].…”

Section: Related Workmentioning

confidence: 99%

“…So far, research has mostly focused on adapting rulebased approaches to generate text from Semantic Web data. These systems worked in domains with small vocabularies and restricted linguistic variability, such as football match summaries [2] and museum exhibits' descriptions [3]. However, the tedious repetition of their textual patterns along with the difficulty of transferring the involved rules across different domains or languages prevented them from becoming widely accepted [4].…”

Section: Introductionmentioning

confidence: 99%

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Neural Wikipedian: Generating Textual Summaries from Knowledge Base Triples

Vougiouklis

Elsahar

Kaffee

et al. 2018

Journal of Web Semantics

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Most people do not interact with Semantic Web data directly. Unless they have the expertise to understand the underlying technology, they need textual or visual interfaces to help them make sense of it. We explore the problem of generating natural language summaries for Semantic Web data. This is non-trivial, especially in an open-domain context. To address this problem, we explore the use of neural networks. Our system encodes the information from a set of triples into a vector of fixed dimensionality and generates a textual summary by conditioning the output on the encoded vector. We train and evaluate our models on two corpora of loosely aligned Wikipedia snippets and DBpedia and Wikidata triples with promising results.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neural Wikipedian: Generating Textual Summaries from Knowledge Base Triples

Vougiouklis

Elsahar

Kaffee

et al. 2018

Journal of Web Semantics

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“…We illustrate the use of the FrameNet-based API to GF RGL by re-engineering two existing multilingual GF application grammars: one for translating standard tourist phrases [12] and another for generating descriptions of paintings [13], both developed in the MOLTO project. 9 In both cases, we preserve the original functionality, and we do not make any changes in the application abstract syntax.…”

Section: Case Studiesmentioning

confidence: 99%

“…The Painting grammar is a part of a large scale Natural Language Generation (NLG) grammar developed for the cultural heritage (CH) domain in order to verbalize data about museum objects stored in an RDF-based ontology [13]. A set of RDF triples (subject-predicate-object expressions) forms the input to the application.…”

Section: Painting Grammarmentioning

confidence: 99%

Controlled Natural Language Generation from a Multilingual FrameNet-Based Grammar

Dannélls¹,

Grūzītis²

2014

Lecture Notes in Computer Science

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Abstract. This paper presents a currently bilingual but potentially multilingual FrameNet-based grammar library implemented in Grammatical Framework. The contribution of this paper is two-fold. First, it offers a methodological approach to automatically generate the grammar based on semantico-syntactic valence patterns extracted from FrameNetannotated corpora. Second, it provides a proof of concept for two use cases illustrating how the acquired multilingual grammar can be exploited in different CNL applications in the domains of arts and tourism.

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“…So far, research has mostly focused on adapting rulebased approaches to generate text from Semantic Web data. These systems work in domains with small vocabularies and restricted linguistic variability, such as football 25 match summaries [2] and museum exhibit descriptions [3]. However, the difficulty of transferring the involved rules across different domains or languages along with the tedious repetition of their textual patterns prevented them from becoming widely accepted [4].…”

Section: Introductionmentioning

confidence: 99%

Neural Wikipedian: Generating Textual Summaries from Knowledge Base Triples

et al. 2018

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Most people need textual or visual interfaces in order to make sense of Semantic Web data. In this paper, we investigate the problem of generating natural language summaries for Semantic Web data using neural networks. Our end-to-end trainable architecture encodes the information from a set of triples into a vector of fixed dimensionality and generates a textual summary by conditioning the output on the encoded vector. We explore a set of different approaches that enable our models to verbalise entities from the input set of triples in the generated text. Our systems are trained and evaluated on two corpora of loosely aligned Wikipedia snippets with triples from DBpedia and Wikidata, with promising results.

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Multilingual online generation from semantic web ontologies

Cited by 23 publications

References 4 publications

Neural Wikipedian: Generating Textual Summaries from Knowledge Base Triples

Neural Wikipedian: Generating Textual Summaries from Knowledge Base Triples

Controlled Natural Language Generation from a Multilingual FrameNet-Based Grammar

Neural Wikipedian: Generating Textual Summaries from Knowledge Base Triples

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