RDF js R eal B : a Symbolic Approach for Generating Text from RDF Triples (2020)

Abstract: This paper describes the Resource Description Framework (RDF) triples verbalizer developed for the WEB NLG CHALLENGE 2020 shared task. After reviewing representative works in Natural Language Generation in the context of the Semantic Web, the task is then described. We then sketch the symbolic approach we used for verbalizing RDF triples: once the triples are grouped by subject, each group is realized as one or more sentences using templates written in Python whose output is feed to an English realizer written in Javascript. The system was developed using the test data of the previous edition of the task and the train and development data of this year’s task. The automatic scores for this year’s test data are quite competitive. We conclude with a critical review of the data and discuss the suitability of this competition results in a wider Natural Language Generation setting.

## introduction this paper describes our system developed for participating to the rdf-to-text generation for english subtask of the webnlg challenge 2020 @xcite as a followup to 2017 edition @xcite . the webnlg challenge 2020 data was developed for pushing the development of resource description framework (rdf) verbalizers for realizing short texts while dealing with micro-planning problems such as sentence segmentation and ordering, referring expression generation and aggregation. the first edition of the data featuring 15 dbpedia categories was created in 2017 @xcite . the 2020 challenge covers more categories and an additional language, russian and a new task: text-to-rdf semantic parsing for converting a text into the corresponding set of rdf triples. our english rdf verbalizer is based on a symbolic approach using python: each rdf triple corresponds to a sentence in which the subject and the object of a triple are mapped almost verbatim as subject and object of the sentence. the predicate of the triple corresponds to a verb phrase which determines the structure of the sentence. the predicates are ordered to create a meaningful story and parts of sentences are merged when they share subjects or predicates. the final realization is performed using jsrealb 1 , a french-english realizer that we upgraded in recent years. after presenting some representative natural language generation works dealing with semantic web information, we describe our approach and analyze the scores of the automatic evaluation on webnlg challenge 2020 test data. we then give a personal appraisal on the data for webnlg challenge 2020 and how it is representative of real semantic web data and conclude by giving ideas for future development. ## related work generating text from ontologies and rdf has a long history, because it is a typical case of computer-oriented data about entities that need to be explained to a human in order to be understood or modified. @xcite present a comprehensive discussion of semantic web concepts and its relation with natural language generation. one challenge of rdf verbalization is the fact that the information is spread over a graph linking many entities that must be linearized as a coherent text. rdf triples cannot be lexicalized directly as they need links with other information in order to be conveyed correctly. fortunately, most well-organized rdf triples depend on an ontology, a hierarchy of concepts, that allows inferencing to help drive the generation process. some systems target the generation of texts for explaining ontologies while other use ontologies for explaining facts expressed in rdf which reflects two trends in the semantic web area: ontology construction using the web ontology language (owl) with an emphasis on the logical consistency, or the publication of a large number of linked data without necessarily ensuring consistency. webnlg challenge 2020 targets the latter because it is limited to the verbalization of a few rdf triples, but we think it is interesting to briefly look at the former, because it gives a broader view of potential applications for future developments. ## text generation to illustrate our nlg process, we use the set of triples shown in the first step in text generation is determining the information to be conveyed in the text. in the context of webnlg challenge 2020, this is given: it is a set of at most 7 triples. as the predicate of a triple indicates a relation between its subject and object, in our case, it is mapped to a verb linking the subject and the object of the sentence realizing to this triple. ## conclusion this paper has described a symbolic approach to solve the shared task webnlg challenge 2020. the automatic scores are quite competitive compared to the ones of the other participants which, we conjecture, most often used machine learning approaches. the system is very fast on a standard laptop. it can also be quite easy to adapt to a new domain: considering about one minute per new predicate, it would have taken about 3 hours to develop 200 new predicates. it would be interesting if machine learning could be applied for developing new templates, even though we doubt it would be as fast.