際際滷shows by User: cpesquita / http://www.slideshare.net/images/logo.gif 際際滷shows by User: cpesquita / Mon, 30 May 2022 07:02:46 GMT 際際滷Share feed for 際際滷shows by User: cpesquita Powering Biomedical Artificial Intelligence with a Holistic Knowledge Graph (SeWebMeDa2022) /slideshow/powering-biomedical-artificial-intelligence-with-a-holistic-knowledge-graph-sewebmeda2022/251881886 sewebmeda2022-keynote-cpesquita-220530070246-bc2fd61f
Biomedical AI applications increasingly rely on multi-domain and heterogeneous data, especially in areas such as personalised medicine and systems biology. Biomedical Ontologies are a golden opportunity in this area because they add meaning to the underlying data which can be used to support heterogeneous data integration, provide scientific context to the data augmenting AI performance, and afford explanatory mechanisms allowing the contextualization of AI predictions. In particular, ontologies and knowledge graphs support the computation of semantic similarity between objects, providing an understanding of why certain objects are considered similar or different. This is a basic aspect of explainability and is at the core of many machine learning applications. However, when data covers multiple domains, it may be necessary to integrate different ontologies to cover the full semantic landscape of the underlying data. In this talk I will present our recent work on building an integrated knowledge graph that is based on the semantic annotation and interlinking of heterogeneous data into a holistic semantic landscape that supports semantic similarity assessments. In this talk I will discuss the challenges in building the knowledge graph from public resources, the methodology we are using and the road-ahead in biomedical ontology and knowledge graph alignment as AI becomes an integral part of biomedical research.]]>
Biomedical AI applications increasingly rely on multi-domain and heterogeneous data, especially in areas such as personalised medicine and systems biology. Biomedical Ontologies are a golden opportunity in this area because they add meaning to the underlying data which can be used to support heterogeneous data integration, provide scientific context to the data augmenting AI performance, and afford explanatory mechanisms allowing the contextualization of AI predictions. In particular, ontologies and knowledge graphs support the computation of semantic similarity between objects, providing an understanding of why certain objects are considered similar or different. This is a basic aspect of explainability and is at the core of many machine learning applications. However, when data covers multiple domains, it may be necessary to integrate different ontologies to cover the full semantic landscape of the underlying data. In this talk I will present our recent work on building an integrated knowledge graph that is based on the semantic annotation and interlinking of heterogeneous data into a holistic semantic landscape that supports semantic similarity assessments. In this talk I will discuss the challenges in building the knowledge graph from public resources, the methodology we are using and the road-ahead in biomedical ontology and knowledge graph alignment as AI becomes an integral part of biomedical research.]]> Mon, 30 May 2022 07:02:46 GMT /slideshow/powering-biomedical-artificial-intelligence-with-a-holistic-knowledge-graph-sewebmeda2022/251881886 cpesquita@slideshare.net(cpesquita) Powering Biomedical Artificial Intelligence with a Holistic Knowledge Graph (SeWebMeDa2022) cpesquita Biomedical AI applications increasingly rely on multi-domain and heterogeneous data, especially in areas such as personalised medicine and systems biology. Biomedical Ontologies are a golden opportunity in this area because they add meaning to the underlying data which can be used to support heterogeneous data integration, provide scientific context to the data augmenting AI performance, and afford explanatory mechanisms allowing the contextualization of AI predictions. In particular, ontologies and knowledge graphs support the computation of semantic similarity between objects, providing an understanding of why certain objects are considered similar or different. This is a basic aspect of explainability and is at the core of many machine learning applications. However, when data covers multiple domains, it may be necessary to integrate different ontologies to cover the full semantic landscape of the underlying data. In this talk I will present our recent work on building an integrated knowledge graph that is based on the semantic annotation and interlinking of heterogeneous data into a holistic semantic landscape that supports semantic similarity assessments. In this talk I will discuss the challenges in building the knowledge graph from public resources, the methodology we are using and the road-ahead in biomedical ontology and knowledge graph alignment as AI becomes an integral part of biomedical research. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sewebmeda2022-keynote-cpesquita-220530070246-bc2fd61f-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Biomedical AI applications increasingly rely on multi-domain and heterogeneous data, especially in areas such as personalised medicine and systems biology. Biomedical Ontologies are a golden opportunity in this area because they add meaning to the underlying data which can be used to support heterogeneous data integration, provide scientific context to the data augmenting AI performance, and afford explanatory mechanisms allowing the contextualization of AI predictions. In particular, ontologies and knowledge graphs support the computation of semantic similarity between objects, providing an understanding of why certain objects are considered similar or different. This is a basic aspect of explainability and is at the core of many machine learning applications. However, when data covers multiple domains, it may be necessary to integrate different ontologies to cover the full semantic landscape of the underlying data. In this talk I will present our recent work on building an integrated knowledge graph that is based on the semantic annotation and interlinking of heterogeneous data into a holistic semantic landscape that supports semantic similarity assessments. In this talk I will discuss the challenges in building the knowledge graph from public resources, the methodology we are using and the road-ahead in biomedical ontology and knowledge graph alignment as AI becomes an integral part of biomedical research.
Powering Biomedical Artificial Intelligence with a Holistic Knowledge Graph (SeWebMeDa2022) from Catia Pesquita
]]> 149 0 https://cdn.slidesharecdn.com/ss_thumbnails/sewebmeda2022-keynote-cpesquita-220530070246-bc2fd61f-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Knowledge Science for AI-based biomedical and clinical applications /slideshow/knowledge-science-for-aibased-biomedical-and-clinical-applications/251510141 knowledgescience-mar2022-220404172307
The great barrier to AI adoption in healthcare and biomedical research is lack of trust. Assessing trustworthiness requires data, domain and user context, which can be supported by ontologies, knowledge graphs and FAIR data. ]]>
The great barrier to AI adoption in healthcare and biomedical research is lack of trust. Assessing trustworthiness requires data, domain and user context, which can be supported by ontologies, knowledge graphs and FAIR data. ]]> Mon, 04 Apr 2022 17:23:07 GMT /slideshow/knowledge-science-for-aibased-biomedical-and-clinical-applications/251510141 cpesquita@slideshare.net(cpesquita) Knowledge Science for AI-based biomedical and clinical applications cpesquita The great barrier to AI adoption in healthcare and biomedical research is lack of trust. Assessing trustworthiness requires data, domain and user context, which can be supported by ontologies, knowledge graphs and FAIR data. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/knowledgescience-mar2022-220404172307-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The great barrier to AI adoption in healthcare and biomedical research is lack of trust. Assessing trustworthiness requires data, domain and user context, which can be supported by ontologies, knowledge graphs and FAIR data.
Knowledge Science for AI-based biomedical and clinical applications from Catia Pesquita
]]> 47 0 https://cdn.slidesharecdn.com/ss_thumbnails/knowledgescience-mar2022-220404172307-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 VOWLMap: Graph-based Ontology Alignment Visualization and Editing /slideshow/vowlmap-graphbased-ontology-alignment-visualization-and-editing/250522742 voila2021-cpesquita-211025123332
VOWLMap is a tool for visualizing, editing, and validating ontology alignments. It implements the Visual Notation for OWL Ontologies (VOWL). Available at: https://github.com/liseda-lab/VOWLMap]]>
VOWLMap is a tool for visualizing, editing, and validating ontology alignments. It implements the Visual Notation for OWL Ontologies (VOWL). Available at: https://github.com/liseda-lab/VOWLMap]]> Mon, 25 Oct 2021 12:33:32 GMT /slideshow/vowlmap-graphbased-ontology-alignment-visualization-and-editing/250522742 cpesquita@slideshare.net(cpesquita) VOWLMap: Graph-based Ontology Alignment Visualization and Editing cpesquita VOWLMap is a tool for visualizing, editing, and validating ontology alignments. It implements the Visual Notation for OWL Ontologies (VOWL). Available at: https://github.com/liseda-lab/VOWLMap <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/voila2021-cpesquita-211025123332-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> VOWLMap is a tool for visualizing, editing, and validating ontology alignments. It implements the Visual Notation for OWL Ontologies (VOWL). Available at: https://github.com/liseda-lab/VOWLMap
VOWLMap: Graph-based Ontology Alignment Visualization and Editing from Catia Pesquita
]]> 212 0 https://cdn.slidesharecdn.com/ss_thumbnails/voila2021-cpesquita-211025123332-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Empirical user studies in Semantic Web contexts /slideshow/empirical-user-studies-in-semantic-web-contexts/123067670 ekaw2018-user-studies-181115080719
My presentation at EKAW 2018 for our position paper that argues better user studies and their reporting are needed in the Semantic Web community, and proposes a framework to design and report empirical studies. Read the paper at: http://steffen-lohmann.de/publications/2018_EKAW_user_studies_semweb.pdf]]>
My presentation at EKAW 2018 for our position paper that argues better user studies and their reporting are needed in the Semantic Web community, and proposes a framework to design and report empirical studies. Read the paper at: http://steffen-lohmann.de/publications/2018_EKAW_user_studies_semweb.pdf]]> Thu, 15 Nov 2018 08:07:19 GMT /slideshow/empirical-user-studies-in-semantic-web-contexts/123067670 cpesquita@slideshare.net(cpesquita) Empirical user studies in Semantic Web contexts cpesquita My presentation at EKAW 2018 for our position paper that argues better user studies and their reporting are needed in the Semantic Web community, and proposes a framework to design and report empirical studies. Read the paper at: http://steffen-lohmann.de/publications/2018_EKAW_user_studies_semweb.pdf <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ekaw2018-user-studies-181115080719-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> My presentation at EKAW 2018 for our position paper that argues better user studies and their reporting are needed in the Semantic Web community, and proposes a framework to design and report empirical studies. Read the paper at: http://steffen-lohmann.de/publications/2018_EKAW_user_studies_semweb.pdf
Empirical user studies in Semantic Web contexts from Catia Pesquita
]]> 371 2 https://cdn.slidesharecdn.com/ss_thumbnails/ekaw2018-user-studies-181115080719-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Sense and Similarity: making sense of similarity for ontologies /slideshow/sense-and-similarity-making-sense-of-similarity-for-ontologies/79082322 cpesquita-bioontologies2017-170823083804
My keynote at the 20th BioOntologies meeting at ISMB/ECCB 2017. It focuses on discussing the challenges and opportunities of ontology semantic similarity and ontology matching to support semantic interoperability.]]>
My keynote at the 20th BioOntologies meeting at ISMB/ECCB 2017. It focuses on discussing the challenges and opportunities of ontology semantic similarity and ontology matching to support semantic interoperability.]]> Wed, 23 Aug 2017 08:38:03 GMT /slideshow/sense-and-similarity-making-sense-of-similarity-for-ontologies/79082322 cpesquita@slideshare.net(cpesquita) Sense and Similarity: making sense of similarity for ontologies cpesquita My keynote at the 20th BioOntologies meeting at ISMB/ECCB 2017. It focuses on discussing the challenges and opportunities of ontology semantic similarity and ontology matching to support semantic interoperability. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cpesquita-bioontologies2017-170823083804-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> My keynote at the 20th BioOntologies meeting at ISMB/ECCB 2017. It focuses on discussing the challenges and opportunities of ontology semantic similarity and ontology matching to support semantic interoperability.
Sense and Similarity: making sense of similarity for ontologies from Catia Pesquita
]]> 435 4 https://cdn.slidesharecdn.com/ss_thumbnails/cpesquita-bioontologies2017-170823083804-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-cpesquita-48x48.jpg?cb=1653891434 https://cdn.slidesharecdn.com/ss_thumbnails/sewebmeda2022-keynote-cpesquita-220530070246-bc2fd61f-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/powering-biomedical-artificial-intelligence-with-a-holistic-knowledge-graph-sewebmeda2022/251881886 Powering Biomedical Ar... https://cdn.slidesharecdn.com/ss_thumbnails/knowledgescience-mar2022-220404172307-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/knowledge-science-for-aibased-biomedical-and-clinical-applications/251510141 Knowledge Science for ... https://cdn.slidesharecdn.com/ss_thumbnails/voila2021-cpesquita-211025123332-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/vowlmap-graphbased-ontology-alignment-visualization-and-editing/250522742 VOWLMap: Graph-based O...