Research Topic

Semantic Relatedness for Social Network Analysis

About this Research Topic

In recent years, the analysis of relational patterns among people and groups, also known as Social Network Analysis (SNA), has gained momentum thanks to the great diffusion of online social networks, i.e., platforms where millions of interconnected people share huge amounts of data on many different topics. SNA can provide insights into social influences within groups of people and may help to identify relations and similarities among people. Several application fields can benefit from SNA, such as disaster management, epidemiologic surveillance and user profiling, etc...

At the same time, semantics-based technologies have shown to be effective tools when dealing with interconnected knowledge. With the advent of the Semantic Web, and the emergence of the Linked Data principles, we are assisting to the development of many approaches for reasoning over graph-shaped knowledge and, in particular, of several semantic relatedness methods that aim at estimating how much two resources in a knowledge graph are semantically related.

Social networks are rich data sources providing texts, images, audios, links, etc., and SNA can require specific text processing techniques because the language used in social media is often informal, full of neologisms, and poor of explicit and formal semantics (e.g., the semantics of a text can be ambiguous). Therefore, SNA can benefit from semantic relatedness methods and semantic technologies at large. Supporting SNA with semantic technologies aims at enriching entities with formal semantics to apply logic-based reasoning mechanisms to discover, for instance, new relations between entities. The use of RDF Knowledge Graphs, as for example DBpedia, is a promising research area that can be investigated in this direction. RDF offers the possibility to make assertions about web resources, each of these identified by a Uniform Resource Identifier (URI), in the form of triples following subject-predicate-object patterns.

This Research Topic calls for original research articles on methodological or technological solutions addressing SNA with the support of semantic technologies and, in particular, semantic relatedness. Here some specific research issues:
- SNA with semantic relatedness and similarity
- SNA and domain specific ontologies
- User profiling in SNA using semantic similarity
- Semantic relatedness and similarity in Linked-Data


Keywords: Semantic Relatedness, Similarity, Social Network Analysis, Ontologie, Semantic Similarity, Relational Patterns, Social Influence, Semantic-based Technology, Linked Data


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.

In recent years, the analysis of relational patterns among people and groups, also known as Social Network Analysis (SNA), has gained momentum thanks to the great diffusion of online social networks, i.e., platforms where millions of interconnected people share huge amounts of data on many different topics. SNA can provide insights into social influences within groups of people and may help to identify relations and similarities among people. Several application fields can benefit from SNA, such as disaster management, epidemiologic surveillance and user profiling, etc...

At the same time, semantics-based technologies have shown to be effective tools when dealing with interconnected knowledge. With the advent of the Semantic Web, and the emergence of the Linked Data principles, we are assisting to the development of many approaches for reasoning over graph-shaped knowledge and, in particular, of several semantic relatedness methods that aim at estimating how much two resources in a knowledge graph are semantically related.

Social networks are rich data sources providing texts, images, audios, links, etc., and SNA can require specific text processing techniques because the language used in social media is often informal, full of neologisms, and poor of explicit and formal semantics (e.g., the semantics of a text can be ambiguous). Therefore, SNA can benefit from semantic relatedness methods and semantic technologies at large. Supporting SNA with semantic technologies aims at enriching entities with formal semantics to apply logic-based reasoning mechanisms to discover, for instance, new relations between entities. The use of RDF Knowledge Graphs, as for example DBpedia, is a promising research area that can be investigated in this direction. RDF offers the possibility to make assertions about web resources, each of these identified by a Uniform Resource Identifier (URI), in the form of triples following subject-predicate-object patterns.

This Research Topic calls for original research articles on methodological or technological solutions addressing SNA with the support of semantic technologies and, in particular, semantic relatedness. Here some specific research issues:
- SNA with semantic relatedness and similarity
- SNA and domain specific ontologies
- User profiling in SNA using semantic similarity
- Semantic relatedness and similarity in Linked-Data


Keywords: Semantic Relatedness, Similarity, Social Network Analysis, Ontologie, Semantic Similarity, Relational Patterns, Social Influence, Semantic-based Technology, Linked Data


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 September 2021 Abstract
23 December 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 September 2021 Abstract
23 December 2021 Manuscript

Participating Journals

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

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