Research Topic

Semantic Resources and Text Mining

About this Research Topic

The scientific literature is increasingly characterised by a deluge of articles and the cross-disciplinary use of information. Hence, there is a dire need of effective tools to aid in monitoring and managing information contained within the literature. The development of text mining technologies is leading to profound changes in information management. The coupling of text mining methods and semantic resources is contributing to the emergence of new practices covering the entire life cycle of knowledge management, relating to how scientific knowledge, information and data are interpreted, described, disseminated, discovered, shared and reused.

Shared semantic resources, such as thesauri, have long been used by librarians to index documents. Nowadays, by making these semantic resources available as machine readable knowledge bases, such as ontologies, they are of central importance in many text mining applications; their potential utility extends way beyond document indexing, to cover areas such as the interpretation, management and reuse of all kinds of data, including text.

Information extraction is an important step in text mining, in which methods based on semantic and knowledge resources are used to support text interpretation and to produce structured machine-readable information. More broadly, domain specific and general resources are extensively used in many fields of text mining, including question answering, summarisation, simplification, query expansion, topic modelling, sentiment/emotion analysis, etc.
Conversely, text mining methods provide the means to build, adapt and enrich semantic/knowledge resources through the automatic analysis of document corpora. Semantic resources may include terminological and lexical resources, case frames, ontologies, knowledge bases and annotated reference corpora.

This themed article collection aims to publish papers describing the coupling of semantic resources and text mining. We encourage submission of papers covering a broad range of methodological, applications and fundamental studies.

We solicit papers covering the topics including, but not limited to the following:

- Creating and updating semantic resources by mapping entities and relationships from text to semantic formal representations
- Text annotation using semantic resources (e.g. OBIE, distant learning methods, transfer learning, weak supervision methods, zero/few shot learning). Definition and use of semantic distances.
- Alignment or mapping between semantic resources using text corpus analysis.
- Populating or curating databases and knowledge bases through text mining.
- Entity linking or normalisation of multi-source data, using semantic resources for data integration. Making textual data more Findable, Accessible, Interoperable, and Reusable by using reference semantic resources (FAIR principles).
- Creation of reference corpora with semantic annotations derived from semantic resources for training machine learning methods and for the evaluation of methods.
- Evaluation framework. How semantic resources may be exploited to develop new criteria, measures and practices in shared tasks.
- Tools and workbenches for semantic annotation and for developing semantic resources. Platforms for creating NLP workflows.
- Applications such as curation of information and ontology building for specific domains
- User in the loop, including their role and cognitive processes in active learning, information curation, crowdsourcing, feedback providing or collaborative design of semantic resources. It may be related to Human-Machine Interfaces, explainability, interpretability and ethics.
- Domain adaptation and transfer learning. Using semantic resources for the adaptation of text mining to new tasks and domains. Conversely, extending semantic resources by analysing various corpora.
- Formal semantic representation, including philosophical considerations regarding the relationship between text corpora and formal semantic representation. Examples include formal representations capturing the meaning of words using corpus analysis, and the role of context in the meaning.


Keywords: semantic resources, text-mining, text annotation, distant learning methods, transfer learning, mapping of semantic resources, reference corpora, NLP


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

The scientific literature is increasingly characterised by a deluge of articles and the cross-disciplinary use of information. Hence, there is a dire need of effective tools to aid in monitoring and managing information contained within the literature. The development of text mining technologies is leading to profound changes in information management. The coupling of text mining methods and semantic resources is contributing to the emergence of new practices covering the entire life cycle of knowledge management, relating to how scientific knowledge, information and data are interpreted, described, disseminated, discovered, shared and reused.

Shared semantic resources, such as thesauri, have long been used by librarians to index documents. Nowadays, by making these semantic resources available as machine readable knowledge bases, such as ontologies, they are of central importance in many text mining applications; their potential utility extends way beyond document indexing, to cover areas such as the interpretation, management and reuse of all kinds of data, including text.

Information extraction is an important step in text mining, in which methods based on semantic and knowledge resources are used to support text interpretation and to produce structured machine-readable information. More broadly, domain specific and general resources are extensively used in many fields of text mining, including question answering, summarisation, simplification, query expansion, topic modelling, sentiment/emotion analysis, etc.
Conversely, text mining methods provide the means to build, adapt and enrich semantic/knowledge resources through the automatic analysis of document corpora. Semantic resources may include terminological and lexical resources, case frames, ontologies, knowledge bases and annotated reference corpora.

This themed article collection aims to publish papers describing the coupling of semantic resources and text mining. We encourage submission of papers covering a broad range of methodological, applications and fundamental studies.

We solicit papers covering the topics including, but not limited to the following:

- Creating and updating semantic resources by mapping entities and relationships from text to semantic formal representations
- Text annotation using semantic resources (e.g. OBIE, distant learning methods, transfer learning, weak supervision methods, zero/few shot learning). Definition and use of semantic distances.
- Alignment or mapping between semantic resources using text corpus analysis.
- Populating or curating databases and knowledge bases through text mining.
- Entity linking or normalisation of multi-source data, using semantic resources for data integration. Making textual data more Findable, Accessible, Interoperable, and Reusable by using reference semantic resources (FAIR principles).
- Creation of reference corpora with semantic annotations derived from semantic resources for training machine learning methods and for the evaluation of methods.
- Evaluation framework. How semantic resources may be exploited to develop new criteria, measures and practices in shared tasks.
- Tools and workbenches for semantic annotation and for developing semantic resources. Platforms for creating NLP workflows.
- Applications such as curation of information and ontology building for specific domains
- User in the loop, including their role and cognitive processes in active learning, information curation, crowdsourcing, feedback providing or collaborative design of semantic resources. It may be related to Human-Machine Interfaces, explainability, interpretability and ethics.
- Domain adaptation and transfer learning. Using semantic resources for the adaptation of text mining to new tasks and domains. Conversely, extending semantic resources by analysing various corpora.
- Formal semantic representation, including philosophical considerations regarding the relationship between text corpora and formal semantic representation. Examples include formal representations capturing the meaning of words using corpus analysis, and the role of context in the meaning.


Keywords: semantic resources, text-mining, text annotation, distant learning methods, transfer learning, mapping of semantic resources, reference corpora, NLP


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

30 November 2020 Abstract
28 February 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

30 November 2020 Abstract
28 February 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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