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.
@cite_14: The worldwide adoption of the HL7 Clinical Document Architecture (CDA) is promoting the availability of coded data (CDA entries) within sections of clinical documents. At the moment, an increasing number of studies are investigating ways to transform the narratives of CDA documents into machine process able CDA entries. This paper addresses the reverse problem, i.e. obtaining linguistic representations (sentences) from CDA entries. The approach presented employs Natural Language Generation (NLG) techniques and deals with two major tasks: content selection and content expression. The current research proposes a formal semantic representation of CDA entries and investigates how expressive domain ontologies in OWL and SPARQL SELECT queries can contribute to NLG. To validate the proposal, the study has focused on CDA entries from the History of Present Illness sections of CDA consultation notes. The results obtained are encouraging, as the clinical narratives automatically generated from these CDA entries fulfil the clinicians' expectations.
@cite_32: In this paper we report on our ongoing work in the EU project Multilingual Online Translation (MOLTO), supported by the European Union Seventh Framework Programme under grant agreement FP7-ICT-247914. More specifically, we present work workpackage 8 (WP8): Case Study: Cultural Heritage. The objective of the work is to build an ontology-based multilingual application for museum information on the Web. Our approach relies on the innovative idea of Reason-able View of the Web of linked data applied to the domain of cultural heritage. We have been developing a Web application that uses Semantic Web ontologies for generating coherent multilingual natural language descriptions about museum objects. We have been experimenting with museum data to test our approach and find that it performs well for the examined languages.
@cite_3: We propose an architecture for generating natural language from Linked Data that automatically learns sentence templates and statistical document planning from parallel RDF datasets and text. We have built a proof-of-concept system (LOD-DEF) trained on un-annotated text from the Simple English Wikipedia and RDF triples from DBpedia, focusing exclusively on factual, non-temporal information. The goal of the system is to generate short descriptions, equivalent to Wikipedia stubs, of entities found in Linked Datasets. We have evaluated the LOD-DEF system against a simple generate-from-triples baseline and human-generated output. In evaluation by humans, LOD-DEF significantly outperforms the baseline on two of three measures: non-redundancy and structure and coherence.
@cite_6: 
@cite_24: Team sports commentaries call for techniques that are able to select content and generate wordings to reflect the affinity of the targeted reader for one of the teams. The existing works tend to have in common that they either start from knowledge sources of limited size to whose structures then different ways of realization are explicitly assigned, or they work directly with linguistic corpora, without the use of a deep knowledge source. With the increasing availability of large-scale ontologies this is no longer satisfactory: techniques are needed that are applicable to general purpose ontologies, but which still take user preferences into account. We take the best of both worlds in that we use a two-layer ontology. The first layer is composed of raw domain data modelled in an application-independent base OWL ontology. The second layer contains a rich perspective generation-motivated domain communication knowledge ontology, inferred from the base ontology. The two-layer ontology allows us to take into account user perspective-oriented criteria at different stages of generation to generate perspective-oriented commentaries. We show how content selection, discourse structuring, information structure determination, and lexicalization are driven by these criteria and how stage after stage a truly user perspective-tailored summary is generated. The viability of our proposal has been evaluated for the generation of football match summaries of the First Spanish Football League. The reported outcome of the evaluation demonstrates that we are on the right track.
@cite_25: With the rise of the Semantic Web more and more data become available encoded using the Semantic Web standard RDF. RDF is faced towards machines: designed to be easily processable by machines it is difficult to be understood by casual users. Transforming RDF data into human-comprehensible text would facilitate non-experts to assess this information. In this paper we present a languageindependent method for extracting RDF verbalization templates from a parallel corpus of text and data. Our method is based on distant-supervised simultaneous multi-relation learning and frequent maximal subgraph pattern mining. We demonstrate the feasibility of our method on a parallel corpus of Wikipedia articles and DBpedia data for English and German.
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 @cite_14 , summaries of football matches @cite_24 , and, descriptions of museum's exhibits @cite_32 . Further Semantic Web oriented NLG applications can be found in @cite_6 . Our work naturally lies on the path opened by recent unsupervised @cite_3 and @cite_25 based approaches for the extraction of RDF verbalisation templates using parallel data-to-text corpora. However, rather than making a prediction about the template that would be the most appropriate to verbalise a set of input triples, our model jointly performs content selection and surface realisation, without the inclusion of any hand-coded rules or templates.