The Semantic Web as a Linguistic Resource: Opportunities for Natural Language Generation

Mellish, Chris; Sun, Xiantang

doi:10.1007/978-1-84628-226-3_7

Cited by 12 publications

(9 citation statements)

References 10 publications

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“…While to the best of our knowledge no work is published on the verbalization of SPARQL queries, related work comes from three areas: verbalization of RDF data [25,15,5,24,29], verbalization of OWL ontologies [1,28,23,3,11,12,8,20,7,11,4,26,9,14], and verbalization of SQL queries [16,17,13]. Although the first two fields provide techniques that we can apply to improve the lexicalization and aggregation tasks, such as the template-based approach presented in [5], the document structuring task, on which we focus here, is rarely explored.…”

Section: Related Workmentioning

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: Related Workmentioning

confidence: 99%

SPARTIQULATION: Verbalizing SPARQL Queries

Ell¹,

Vrandečić²,

Simperl³

2015

Lecture Notes in Computer Science

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“…More information on how ontosum compares to Naturalowl can be found elsewhere (Androutsopoulos et al, 2012). Mellish and Sun (2006) focus on lexicalization and sentence aggregation, aiming to produce a single aggregated sentence from an input collection of rdf triples; by contrast, Naturalowl produces multi-sentence texts. In complementary work, Mellish et al (2008) consider content selection for texts describing owl classes.…”

Section: Related Workmentioning

confidence: 99%

Generating Natural Language Descriptions from OWL Ontologies: the NaturalOWL System

Androutsopoulos¹,

Λάμπουρας²,

Galanis³

2013

jair

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We present Naturalowl, a natural language generation system that produces texts describing individuals or classes of owl ontologies. Unlike simpler owl verbalizers, which typically express a single axiom at a time in controlled, often not entirely fluent natural language primarily for the benefit of domain experts, we aim to generate fluent and coherent multi-sentence texts for end-users. With a system like Naturalowl, one can publish information in owl on the Web, along with automatically produced corresponding texts in multiple languages, making the information accessible not only to computer programs and domain experts, but also end-users. We discuss the processing stages of Naturalowl, the optional domain-dependent linguistic resources that the system can use at each stage, and why they are useful. We also present trials showing that when the domain-dependent linguistic resources are available, Naturalowl produces significantly better texts compared to a simpler verbalizer, and that the resources can be created with relatively light effort.

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“…For evidence on how entity names are formed in practice, we can refer to studies on the structure of identifiers and labels in ontology corpora [10,13], which show that individuals, classes and properties are named by distinctive part-of-speech sequences. Individual identifiers are made up mostly of proper nouns, common nouns and numbers; where the opening word is not a proper noun, the definite article 'the' is often implicit.…”

Section: Recognising Entity Namesmentioning

confidence: 99%

OWL Simplified English: A Finite-State Language for Ontology Editing

Power

2012

Lecture Notes in Computer Science

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Abstract. We describe a controlled fragment of English for editing ontologies in OWL. Although this language substantially overlaps other CNLs that have been proposed for this purpose, it has a number of special features designed to simplify its learning and use. First, the language allows users to start typing in sentences with little or no preliminary effort in building a controlled vocabulary or lexicon. Second, it disallows sentences that people interpret as structurally ambiguous. Third, it employs a finite-state grammar, so facilitating fast and reliable implementation of an editing tool. These advantages are gained at the cost of severe restrictions in coverage, which mean that the majority of potential OWL axioms cannot be expressed. However, analysis of axiom patterns from several ontology repositories suggests that these constraints are almost invariably respected by ontology developers, so that in practice the loss of expressivity is rarely noticeable.

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The Semantic Web as a Linguistic Resource: Opportunities for Natural Language Generation

Cited by 12 publications

References 10 publications

SPARTIQULATION: Verbalizing SPARQL Queries

SPARTIQULATION: Verbalizing SPARQL Queries

Generating Natural Language Descriptions from OWL Ontologies: the NaturalOWL System

OWL Simplified English: A Finite-State Language for Ontology Editing

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