Generating Tailored Textual Summaries from Ontologies

Bontcheva, Kalina

doi:10.1007/11431053_36

Cited by 30 publications

(22 citation statements)

References 5 publications

(11 reference statements)

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“…Structure realization is the task to add markup such as HTML code to the generated text in order to be interpreted by the presentation system, such as a web browser. Bontcheva [2] points out that hypertext usability studies [18] have shown that formatting is important since it improves readability. Indenting complex verbalizations, adding bullet points and changing the font size can help to communicate the meaning of a query.…”

Section: Components and Tasksmentioning

confidence: 99%

SPARTIQULATION: Verbalizing SPARQL Queries

Ell¹,

Vrandečić²,

Simperl³

2015

Lecture Notes in Computer Science

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Abstract. Much research has been done to combine the fields of Databases and Natural Language Processing. While many works focus on the problem of deriving a structured query for a given natural language question, the problem of query verbalization -translating a structured query into natural language -is less explored. In this work we describe our approach to verbalizing SPARQL queries in order to create natural language expressions that are readable and understandable by the human day-to-day user. These expressions are helpful when having search engines that generate SPARQL queries for user-provided natural language questions or keywords. Displaying verbalizations of generated queries to a user enables the user to check whether the right question has been understood. While our approach enables verbalization of only a subset of SPARQL 1.1, this subset applies to 90% of the 209 queries in our training set. These observations are based on a corpus of SPARQL queries consisting of datasets from the QALD-1 challenge and the ILD2012 challenge.

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Section: Components and Tasksmentioning

confidence: 99%

SPARTIQULATION: Verbalizing SPARQL Queries

Ell¹,

Vrandečić²,

Simperl³

2015

Lecture Notes in Computer Science

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“…These resources normally attach labels in natural language to the concepts and relations that define their structure, and these labels can be used for a number of purposes, such as providing user interface localization , multilingual data access (Declerck et al 2010), information extraction (Müller et al 2004) and natural language generation (Bontcheva 2005). Applications that use such ontologies and taxonomies will require translation of the natural language descriptions associated with them in order to adapt these methods to new languages.…”

Section: Knowledge-based Mt Systemsmentioning

confidence: 99%

Getting Past the Language Gap: Innovations in Machine Translation

Delmonte

2012

Mobile Speech and Advanced Natural Language Solutions

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In this chapter, we will be reviewing state of the art machine translation systems, and will discuss innovative methods for machine translation, highlighting the most promising techniques and applications. Machine translation (MT) has benefited from a revitalization in the last 10 years or so, after a period of relatively slow activity. In 2005 the field received a jumpstart when a powerful complete experimental package for building MT systems from scratch became freely available as a result of the unified efforts of the MOSES international consortium. Around the same time, hierarchical methods had been introduced by Chinese researchers, which allowed the introduction and use of syntactic information in translation modeling. Furthermore, the advances in the related field of computational linguistics, making off-the-shelf taggers and parsers readily available, helped give MT an additional boost. Yet there is still more progress to be made. For example, MT will be enhanced greatly when both syntax and semantics are on board: this still presents a major challenge though many advanced research groups are currently pursuing ways to meet this challenge head-on. The next generation of MT will consist of a collection of hybrid systems. It also augurs well for the mobile environment, as we look forward to more advanced and improved technologies that enable the working of Speech-To-Speech machine translation on hand-held devices, i.e. speech recognition and speech synthesis. We review all of these developments and point out in the final section some of the most promising research avenues for the future of MT.

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“…The Semantic Web has made available a large amount of semantic data in the form of ontologies and there have been several attempts to apply this to NLP tasks such as question answering [17], information extraction [7] and text generation [2]. However, current standards such as RDFS and SKOS [18] only allow for limited linguistic information to be attached to an ontology, limiting the potential functionality of these applications.…”

Section: Introductionmentioning

confidence: 99%

Linking Lexical Resources and Ontologies on the Semantic Web with Lemon

McCrae

Spohr

Cimiano

2011

Lecture Notes in Computer Science

124

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Abstract. There are a large number of ontologies currently available on the Semantic Web. However, in order to exploit them within natural language processing applications, more linguistic information than can be represented in current Semantic Web standards is required. Further, there are a large number of lexical resources available representing a wealth of linguistic information, but this data exists in various formats and is difficult to link to ontologies and other resources. We present a model we call lemon (Lexicon Model for Ontologies) that supports the sharing of terminological and lexicon resources on the Semantic Web as well as their linking to the existing semantic representations provided by ontologies. We demonstrate that lemon can succinctly represent existing lexical resources and in combination with standard NLP tools we can easily generate new lexica for domain ontologies according to the lemon model. We demonstrate that by combining generated and existing lexica we can collaboratively develop rich lexical descriptions of ontology entities. We also show that the adoption of Semantic Web standards can provide added value for lexicon models by supporting a rich axiomatization of linguistic categories that can be used to constrain the usage of the model and to perform consistency checks.

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Generating Tailored Textual Summaries from Ontologies

Cited by 30 publications

References 5 publications

SPARTIQULATION: Verbalizing SPARQL Queries

SPARTIQULATION: Verbalizing SPARQL Queries

Getting Past the Language Gap: Innovations in Machine Translation

Linking Lexical Resources and Ontologies on the Semantic Web with Lemon

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