Simone Tedeschi, Johan Bos, Thierry Declerck, Jan Hajič, Daniel Hershcovich, Eduard Hovy, Alexander Koller, Simon Krek, Steven Schockaert, Rico Sennrich, Ekaterina Shutova, Roberto Navigli. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

We present ongoing work dealing with a Linked Data compliant representation of infrastructures using wordnets for connecting multilingual Sign Language data sets.We build for this on already existing RDF and OntoLex representations of Open Multilingual Wordnet (OMW) data sets and work done by the European EAS-IER research project on the use of the CSV files of OMW for linking glosses and basic semantic information associated with Sign Language data sets in two languages: German and Greek.In this context, we started the transformation into RDF of a Danish data set, which links Danish Sign Language data and the wordnet for Danish, DanNet.The final objective of our work is to include Sign Language data sets (and their conceptual cross-linking via wordnets) in the Linguistic Linked Open Data cloud.

For Sign Languages (SLs), can we create a SignNet, like a WordNet for spoken languages: a network of semantic relations between constitutive elements of SLs?We first discuss approaches that link SL data to wordnets, or integrate such elements with some adaptations into the structure of WordNet.Then, we present requirements for a SignNet, which is built on SL data and then linked to WordNet.

This paper describes an approach aiming at utilizing Wiktionary data for creating specialized lexical datasets which can be used for enriching other lexical (semantic) resources or for generating datasets that can be used for evaluating or improving NLP tasks, like Word Sense Disambiguation, Word-in-Context challenges, or Sense Linking across lexicons and dictionaries. We have focused on Wiktionary data about pronunciation information in English, and grammatical number and grammatical gender in German.

We present the current status of a new ontology for representing constitutive elements of Sign Languages (SL). This development emerged from investigations on how to represent multimodal lexical data in the OntoLex-Lemon framework, with the goal to publish such data in the Linguistic Linked Open Data (LLOD) cloud. While studying the literature and various sites dealing with sign languages, we saw the need to harmonise all the data categories (or features) defined and used in those sources, and to organise them in an ontology to which lexical descriptions in OntoLex-Lemon could be linked. We make the code of the first version of this ontology available, so that it can be further developed collaboratively by both the Linked Data and the SL communities

This paper describes ongoing work aiming at adding pronunciation information to lexical semantic resources, with a focus on open wordnets. Our goal is not only to add a new modality to those semantic networks, but also to mark heteronyms listed in them with the pronunciation information associated with their different meanings. This work could contribute in the longer term to the disambiguation of multi-modal resources, which are combining text and speech.

Aligning senses across resources and languages is a challenging task with beneficial applications in the field of natural language processing and electronic lexicography. In this paper, we describe our efforts in manually aligning monolingual dictionaries. The alignment is carried out at sense-level for various resources in 15 languages. Moreover, senses are annotated with possible semantic relationships such as broadness, narrowness, relatedness, and equivalence. In comparison to previous datasets for this task, this dataset covers a wide range of languages and resources and focuses on the more challenging task of linking general-purpose language. We believe that our data will pave the way for further advances in alignment and evaluation of word senses by creating new solutions, particularly those notoriously requiring data such as neural networks. Our resources are publicly available at https://github.com/elexis-eu/MWSA.

In this paper we describe the contributions made by the European H2020 project “Prêt-à-LLOD” (‘Ready-to-use Multilingual Linked Language Data for Knowledge Services across Sectors’) to the further development of the Linguistic Linked Open Data (LLOD) infrastructure. Prêt-à-LLOD aims to develop a new methodology for building data value chains applicable to a wide range of sectors and applications and based around language resources and language technologies that can be integrated by means of semantic technologies. We describe the methods implemented for increasing the number of language data sets in the LLOD. We also present the approach for ensuring interoperability and for porting LLOD data sets and services to other infrastructures, as well as the contribution of the projects to existing standards.

Data is key in training modern language technologies. In this paper, we summarise the findings of the first pan-European study on obstacles to sharing language data across 29 EU Member States and CEF-affiliated countries carried out under the ELRC White Paper action on Sustainable Language Data Sharing to Support Language Equality in Multilingual Europe. Why Language Data Matters. We present the methodology of the study, the obstacles identified and report on recommendations on how to overcome those. The obstacles are classified into (1) lack of appreciation of the value of language data, (2) structural challenges, (3) disposition towards CAT tools and lack of digital skills, (4) inadequate language data management practices, (5) limited access to outsourced translations, and (6) legal concerns. Recommendations are grouped into addressing the European/national policy level, and the organisational/institutional level.

In this paper we describe our current work on representing a recently created German lexical semantics resource in OntoLex-Lemon and in conformance with Word-Net specifications.Besides presenting the representation effort, we show the utilization of OntoLex-Lemon to bridge from WordNet-like resources to full lexical descriptions and extend the coverage of WordNets to other types of lexical data, such as decomposition results, exemplified for German data, and inflectional phenomena, here outlined for English data.

In the context of a student software project we are investigating the use of Word-Net for improving the automatic detection and classification of actors (or characters) mentioned in folktales.Our starting point is the book "Classification of International Folktales", out of which we extract text segments that name the different actors involved in tales, taking advantage of patterns used by its author, Hans-Jörg Uther.We apply on those text segments functions that are implemented in the NLTK interface to WordNet in order to obtain lexical semantic information to enrich the original naming of characters proposed in the "Classification of International Folktales" and to support their translation in other languages.

The objective of this special issue is to report on recent trends in digital and media technologies responding to the challenges of managing and accessing multimedia (images, audio, video, 3D/4D material, etc.).In a highly selective review procedure we accepted contributions describing recent work that aims at narrowing the large disparity between the low-level multimedia descriptors and the richness of subjectivity of semantics in user queries and human interpretations of audiovisual data.The articles in this special issue can be grouped into following categories: Multimedia Analysis (Section 1), Multimedia Ontologies and Data Integration (Section 2), and Social Media and Retrieval (Section 3).

In this poster submission, we describe the actual state of development of textual analysis and ontology-based information extraction in real world applications, as they are defined in the context of the European R&D project “MUSING” dealing with Business Intelligence. We present in some details the actual state of ontology development, including a time and domain ontologies, which are guiding information extraction onto an ontology population task.

We describe an implemented hybrid reasoning architecture that is used in an EU-funded project called MUSING (www.musing.eu) which is dedicated towards the investigation of semantic-based business intelligence solutions. The reasoning platform builds on publicly available software, such as Pellet, OWLIM, Jena, and Sesame. The project uses and extends existing OWL ontologies (e.g., PROTON) and assumes rule-based reasoning to take place on top of OWL. We describe the pros and cons of each subsystem w.r.t. the needs we have encountered during our investigation. We explain the specific reasoning architecture that is based on a sequence and a fixpoint computation of three reasoners which we might sloppily write as Pellet + (OWLIM + Jena)^*. Pellet is used for checking the initial consistency of the ontology, whereas OWLIM and Jena are employed to execute rules outside the expressiveness of OWL. However, OWLIM is way much faster than Jena, but neither has means to do numerical comparison nor arithmetic. We explain our choice why SWRL is not enough and why we believe that binary OWL properties lead to an unwanted proliferation of objects, making representation and reasoning extremely complex.