{"id":{"repo_id":"catania","oai_identifier":"oai:www.iris.unict.it:20.500.11769/581333"},"canonical_url":"https://search.dev.ndltd.org/etd/catania/oai:www.iris.unict.it:20.500.11769/581333","repository":{"repo_id":"catania","name":"Università degli Studi di Catania","base_url":"https://www.iris.unict.it/oai/request"},"display":{"title":"Natural Language Processing Solutions for Knowledge Extraction: NetME and EmotWion","abstract":"Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interaction between computers and humans in natural language. With an exponential growth of big data in this era, the advent of NLP based systems has enabled us to access relevant information through a wide range of applications. During my PhD I used these methodologies in two different domains, biomedical knowledge networks building and analysis of the contagion of emotions in social networks. In biomedical domain, with the increasing volume and unstructured nature of scientific literature most of the information embedded within them are lost. The inference of new knowledge and the development of new hypotheses from current literature analysis are a fundamental processes for foundation of new scientific discoveries, and get knowledge about relations and interactions among biological elements, a very important case study in complex systems domain. Knowledge Networks are helpful tools especially in the context of bio- 1 2 logical knowledge discovery and modeling, given the enormous amount of literature and knowledge bases available, and allow the researchers to obtain information on aspects already widely investigated by others researchers. In emotion analysis domain, thanks to the social networks phenomenon, that deeply pervaded today’s society, most of the communication paradigms have moved to online, hence there is a lot of social media data available which can be used for emotion analysis and classification. Emotion analysis is important because it affects our daily decision making capabilities, both socially or commercially context. In this thesis I present NetME, a framework which I developed that combines TAGME annotation framework based on Wikipedia corpus and NLP methodology. It allows to build on-the-fly knowledge graphs starting from a subset of full texts obtained by a real-time query on PubMed and applying several syntactic analysis methodologies. In this thesis I also describe another project, EmotWion, a framework which I developed that aims to study the contagion of emotions on complex networks like social networks and its duration over time.","abstract_html":"Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interaction between computers and humans in natural language. With an exponential growth of big data in this era, the advent of NLP based systems has enabled us to access relevant information through a wide range of applications. During my PhD I used these methodologies in two different domains, biomedical knowledge networks building and analysis of the contagion of emotions in social networks. In biomedical domain, with the increasing volume and unstructured nature of scientific literature most of the information embedded within them are lost. The inference of new knowledge and the development of new hypotheses from current literature analysis are a fundamental processes for foundation of new scientific discoveries, and get knowledge about relations and interactions among biological elements, a very important case study in complex systems domain. Knowledge Networks are helpful tools especially in the context of bio- 1 2 logical knowledge discovery and modeling, given the enormous amount of literature and knowledge bases available, and allow the researchers to obtain information on aspects already widely investigated by others researchers. In emotion analysis domain, thanks to the social networks phenomenon, that deeply pervaded today’s society, most of the communication paradigms have moved to online, hence there is a lot of social media data available which can be used for emotion analysis and classification. Emotion analysis is important because it affects our daily decision making capabilities, both socially or commercially context. In this thesis I present NetME, a framework which I developed that combines TAGME annotation framework based on Wikipedia corpus and NLP methodology. It allows to build on-the-fly knowledge graphs starting from a subset of full texts obtained by a real-time query on PubMed and applying several syntactic analysis methodologies. In this thesis I also describe another project, EmotWion, a framework which I developed that aims to study the contagion of emotions on complex networks like social networks and its duration over time.","abstract_has_math":false,"creators":["MUSCOLINO, ALESSANDRO MARIO"],"institution":"Università degli studi di Catania","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["RAPISARDA, Andrea"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-02-21","date_published":"2022-02-21","updated_at":"2026-07-24T01:34:58Z","subjects":["Knowledge Graph, Complex system, Complex network, Document Annotation, Syntactic Analysis Methodologies, Emotion Analysis Methodologies, Natural language processing, spaCy"],"languages":["ita"],"rights":["info:eu-repo/semantics/openAccess","license:PUBBLICO - Pubblico con Copyright","license uri:iris.PUB02"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/20.500.11769/581333","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Muscolino, ALESSANDRO MARIO","RAPISARDA, Andrea"]},{"key":"dc:creator","label":"Author","values":["MUSCOLINO, ALESSANDRO MARIO"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-02-21"]},{"key":"dc:publisher","label":"Institution","values":["Università degli studi di Catania","place:Catania"]},{"key":"dc:type","label":"Dc Type","values":["info:eu-repo/semantics/doctoralThesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Knowledge Graph, Complex system, Complex network, Document Annotation, Syntactic Analysis Methodologies, Emotion Analysis Methodologies, Natural language processing, spaCy"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["ita"]},{"key":"dc:rights","label":"Dc Rights","values":["info:eu-repo/semantics/openAccess","license:PUBBLICO - Pubblico con Copyright","license uri:iris.PUB02"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/20.500.11769/581333"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interaction between computers and humans in natural language. With an exponential growth of big data in this era, the advent of NLP based systems has enabled us to access relevant information through a wide range of applications. During my PhD I used these methodologies in two different domains, biomedical knowledge networks building and analysis of the contagion of emotions in social networks. In biomedical domain, with the increasing volume and unstructured nature of scientific literature most of the information embedded within them are lost. The inference of new knowledge and the development of new hypotheses from current literature analysis are a fundamental processes for foundation of new scientific discoveries, and get knowledge about relations and interactions among biological elements, a very important case study in complex systems domain. Knowledge Networks are helpful tools especially in the context of bio- 1 2 logical knowledge discovery and modeling, given the enormous amount of literature and knowledge bases available, and allow the researchers to obtain information on aspects already widely investigated by others researchers. In emotion analysis domain, thanks to the social networks phenomenon, that deeply pervaded today’s society, most of the communication paradigms have moved to online, hence there is a lot of social media data available which can be used for emotion analysis and classification. Emotion analysis is important because it affects our daily decision making capabilities, both socially or commercially context. In this thesis I present NetME, a framework which I developed that combines TAGME annotation framework based on Wikipedia corpus and NLP methodology. It allows to build on-the-fly knowledge graphs starting from a subset of full texts obtained by a real-time query on PubMed and applying several syntactic analysis methodologies. In this thesis I also describe another project, EmotWion, a framework which I developed that aims to study the contagion of emotions on complex networks like social networks and its duration over time."]},{"key":"dc:title","label":"Title","values":["Natural Language Processing Solutions for Knowledge Extraction: NetME and EmotWion"]}]}],"canonical_facts":{"dc:contributor":["Muscolino, ALESSANDRO MARIO","RAPISARDA, Andrea"],"dc:creator":["MUSCOLINO, ALESSANDRO MARIO"],"dc:date":["2022-02-21"],"dc:description":["Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interaction between computers and humans in natural language. With an exponential growth of big data in this era, the advent of NLP based systems has enabled us to access relevant information through a wide range of applications. During my PhD I used these methodologies in two different domains, biomedical knowledge networks building and analysis of the contagion of emotions in social networks. In biomedical domain, with the increasing volume and unstructured nature of scientific literature most of the information embedded within them are lost. The inference of new knowledge and the development of new hypotheses from current literature analysis are a fundamental processes for foundation of new scientific discoveries, and get knowledge about relations and interactions among biological elements, a very important case study in complex systems domain. Knowledge Networks are helpful tools especially in the context of bio- 1 2 logical knowledge discovery and modeling, given the enormous amount of literature and knowledge bases available, and allow the researchers to obtain information on aspects already widely investigated by others researchers. In emotion analysis domain, thanks to the social networks phenomenon, that deeply pervaded today’s society, most of the communication paradigms have moved to online, hence there is a lot of social media data available which can be used for emotion analysis and classification. Emotion analysis is important because it affects our daily decision making capabilities, both socially or commercially context. In this thesis I present NetME, a framework which I developed that combines TAGME annotation framework based on Wikipedia corpus and NLP methodology. It allows to build on-the-fly knowledge graphs starting from a subset of full texts obtained by a real-time query on PubMed and applying several syntactic analysis methodologies. In this thesis I also describe another project, EmotWion, a framework which I developed that aims to study the contagion of emotions on complex networks like social networks and its duration over time."],"dc:identifier":["https://hdl.handle.net/20.500.11769/581333"],"dc:language":["ita"],"dc:publisher":["Università degli studi di Catania","place:Catania"],"dc:rights":["info:eu-repo/semantics/openAccess","license:PUBBLICO - Pubblico con Copyright","license uri:iris.PUB02"],"dc:subject":["Knowledge Graph, Complex system, Complex network, Document Annotation, Syntactic Analysis Methodologies, Emotion Analysis Methodologies, Natural language processing, spaCy"],"dc:title":["Natural Language Processing Solutions for Knowledge Extraction: NetME and EmotWion"],"dc:type":["info:eu-repo/semantics/doctoralThesis"]},"updated_at":"2026-07-24T01:34:58Z"}