Neural language models for the multilingual, transcultural, and multimodal Semantic Web

Gromann, Dagmar

doi:10.3233/sw-190373

Cited by 9 publications

(4 citation statements)

References 36 publications

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“…For the remaining tasks, utilizing multilingual models provide the desired functionality (Cruz et al, 2020;De Cao et al, 2021). Overall, multilingual models have shown significant improvement in the latest research (Baumann, 2019;Gromann, 2020).…”

Section: Discussionmentioning

confidence: 99%

Doc2KG

Vlachava

Konstantinidis

Bassiliades

et al. 2022

International Journal on Semantic Web and Information Systems

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Document Management Systems (DMS) are used for decades to store large amounts of information in textual form. Their technology paradigm is based on storing vast quantities of textual information enriched with metadata to support searchability. However, this exhibits limitations as it treats textual information as black box and is based exclusively on user-created metadata, a process that suffers from quality and completeness shortcomings. The use of knowledge graphs in DMS can substantially improve searchability, providing the ability to link data and enabling semantic searching. Recent approaches focus on either creating knowledge graphs from document collections or updating existing ones. In this paper, we introduce Doc2KG (Document-to-Knowledge-Graph), an intelligent framework that handles both creation and real-time updating of a knowledge graph, while also exploiting domain-specific ontology standards. We use DIAVGEIA (clarity), an award winning Greek open government portal, as our case-study and discuss new capabilities for the portal by implementing Doc2KG.

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Section: Discussionmentioning

confidence: 99%

Doc2KG

Vlachava

Konstantinidis

Bassiliades

et al. 2022

International Journal on Semantic Web and Information Systems

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“…It also should be stressed that the popular TEI data model used in digital humanities can be made compatible with RDF. From a different angle, Gromann [118] presents a vision of joining Neural Language Models (NLM) and LLOD towards a multilingual, transcultural, and multimodal information access. Different linguistic description levels are not considered explicitly, however, methods and application scenarios for all three dimensions are provided.…”

Section: Gromann Et Al / Multilinguality and Llodmentioning

confidence: 99%

Multilinguality and LLOD: A survey across linguistic description levels

Gromann,

Apostol,

Chiarcos

et al. 2024

Self Cite

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Limited accessibility to language resources and technologies represents a challenge for the analysis, preservation, and documentation of natural languages other than English. Linguistic Linked (Open) Data (LLOD) holds the promise to ease the creation, linking, and reuse of multilingual linguistic data across distributed and heterogeneous resources. However, individual language resources and technologies accommodate or target different linguistic description levels, e.g., morphology, syntax, phonology, and pragmatics. In this comprehensive survey, the state-of-the-art of multilinguality and LLOD is being represented with a particular focus on linguistic description levels, identifying open challenges and gaps as well as proposing an ideal ecosystem for multilingual LLOD across description levels. This survey seeks to contribute an introductory text for newcomers to the field of multilingual LLOD, uncover gaps and challenges to be tackled by the LLOD community in reference to linguistic description levels, and present a solid basis for a future best practice of multilingual LLOD across description levels.

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“…Semantic Web techniques and natural language processing (NLP) provide computers with the ability to decipher the meaning of information and process phrases written or spoken in natural language [19]. The combination of these techniques can allow users to search for data using natural language sentences [20,21] in multiple languages, and they can also be used to find semantic similarities among disparate documents from different disciplines [22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

A Virtual Community for Disability Advocacy: Development of a Searchable Artificial Intelligence–Supported Platform (Preprint)

Morr¹,

Maret²,

Muhlenbach³

et al. 2021

Preprint

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BACKGROUND The lack of availability of disability data has been identified as a major challenge hindering continuous disability equity monitoring. It is important to develop a platform that enables searching for disability data to expose systemic discrimination and social exclusion, which increase vulnerability to inequitable social conditions. OBJECTIVE Our project aims to create an accessible and multilingual pilot disability website that structures and integrates data about people with disabilities and provides data for national and international disability advocacy communities. The platform will be endowed with a document upload function with hybrid (automated and manual) paragraph tagging, while the querying function will involve an intelligent natural language search in the supported languages. METHODS We have designed and implemented a virtual community platform using Wikibase, Semantic Web, machine learning, and web programming tools to enable disability communities to upload and search for disability documents. The platform data model is based on an ontology we have designed following the United Nations Convention on the Rights of Persons with Disabilities (CRPD). The virtual community facilitates the uploading and sharing of validated information, and supports disability rights advocacy by enabling dissemination of knowledge. RESULTS Using health informatics and artificial intelligence techniques (namely Semantic Web, machine learning, and natural language processing techniques), we were able to develop a pilot virtual community that supports disability rights advocacy by facilitating uploading, sharing, and accessing disability data. The system consists of a website on top of a Wikibase (a Semantic Web–based datastore). The virtual community accepts 4 types of users: information producers, information consumers, validators, and administrators. The virtual community enables the uploading of documents, semiautomatic tagging of their paragraphs with meaningful keywords, and validation of the process before uploading the data to the disability Wikibase. Once uploaded, public users (information consumers) can perform a semantic search using an intelligent and multilingual search engine (QAnswer). Further enhancements of the platform are planned. CONCLUSIONS The platform ontology is flexible and can accommodate advocacy reports and disability policy and legislation from specific jurisdictions, which can be accessed in relation to the CRPD articles. The platform ontology can be expanded to fit international contexts. The virtual community supports information upload and search. Semiautomatic tagging and intelligent multilingual semantic search using natural language are enabled using artificial intelligence techniques, namely Semantic Web, machine learning, and natural language processing.

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Neural language models for the multilingual, transcultural, and multimodal Semantic Web

Cited by 9 publications

References 36 publications

Doc2KG

Doc2KG

Multilinguality and LLOD: A survey across linguistic description levels

A Virtual Community for Disability Advocacy: Development of a Searchable Artificial Intelligence–Supported Platform (Preprint)

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