The Applied Innovation unit works at the intersection between data science research and practice in industry and elsewhere. The group achieves this through the application of research carried out at the Data Science Institute (DSI) along with software engineering skills either on it's own or in collaboration with internal research units and/or external partners - often industry.

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The Applied Innovation unit works at the intersection between data science research and practice in industry and elsewhere. The group achieves this through the application of research carried out at the Data Science Institute (DSI) along with software engineering skills either on it's own or in collaboration with internal research units and/or external partners - often industry.

Within the DKE team, we look at the whole life-cycle of data and knowledge. It is said that 80% of data analytics processes is data preparation. We aim to make this easier and to investigate the methods, approaches and techniques that support making data exploitable, and knowledge accessible.

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Within the DKE team, we look at the whole life-cycle of data and knowledge. It is said that 80% of data analytics processes is data preparation. We aim to make this easier and to investigate the methods, approaches and techniques that support making data exploitable, and knowledge accessible.

The Open Distribued Systems unit is investigating novel techniques to contribute to decentralised and open distributed infrastructure. In collaboration with scientific and industrial partners the domain will identify and develop techniques to impacts people and organisation.

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The Open Distribued Systems unit is investigating novel techniques to contribute to decentralised and open distributed infrastructure. In collaboration with scientific and industrial partners the domain will identify and develop techniques to impacts people and organisation.

The Internet of Things and Stream Processing Research Unit (UIoT) at the Data Science Institute, National University of Ireland, Galway and part of the Insight Centre for Data Analytics is a multidisciplinary research unit where we investigate the convergence of Software Systems, the Semantic Web and the Internet, heavily focused on the evolution of Sensor Technologies, Artificial Intelligence and Robotics Systems with the objective to contribute in advancing the state of the art in computer science using Semantic Technologies, Internet of Things and Cloud Systems and Cyber-security fundamentals. The UIoT Research Unit is one of the European’s leading application-oriented research groups for IoT middleware technologies and IoT system solutions and services to safely unify the real and the virtual worlds. We are experts in Semantic Interoperability and Stream Processing, we have described the IoT stack and Fog Computing Models and contributed on describing the Data Federation Model for IoT edge nodes, we have extended the use of Semantic Web Models & Linked data for IoT and worked towards supporting flash-friendly databases and graph-based models for heterogeneous data integration using spatial-temporal Data Management. We have created the Linked Sensors Midleware (LSM), The Super Streams Colliders (SSC) amongs other middlewares and the Semantic Interoperability Framework (SEG 3.0) for Distributed Systems Validation and the Data Interplay in Edge Computing using the Linked Data paradigm.

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The Internet of Things and Stream Processing Research Unit (UIoT) at the Data Science Institute, National University of Ireland, Galway and part of the Insight Centre for Data Analytics is a multidisciplinary research unit where we investigate the convergence of Software Systems, the Semantic Web and the Internet, heavily focused on the evolution of Sensor Technologies, Artificial Intelligence and Robotics Systems with the objective to contribute in advancing the state of the art in computer science using Semantic Technologies, Internet of Things and Cloud Systems and Cyber-security fundamentals. The UIoT Research Unit is one of the European’s leading application-oriented research groups for IoT middleware technologies and IoT system solutions and services to safely unify the real and the virtual worlds. We are experts in Semantic Interoperability and Stream Processing, we have described the IoT stack and Fog Computing Models and contributed on describing the Data Federation Model for IoT edge nodes, we have extended the use of Semantic Web Models & Linked data for IoT and worked towards supporting flash-friendly databases and graph-based models for heterogeneous data integration using spatial-temporal Data Management. We have created the Linked Sensors Midleware (LSM), The Super Streams Colliders (SSC) amongs other middlewares and the Semantic Interoperability Framework (SEG 3.0) for Distributed Systems Validation and the Data Interplay in Edge Computing using the Linked Data paradigm.

The Unit for Natural Language Processing at the Data Science Institute, National University of Ireland Galway has a focus on research in knowledge extraction, social media analysis, multilinguality and dialogue systems. In our knowledge extraction research, we develop methods for the extraction, representation, integration and exploitation of meaning from unstructured and semi-structured text data. Our demonstrator system <a href="https://github.com/insight-centre/saffron/">Saffron</a> encapsulates this into a readily available tool for knowledge graph extraction. Our social media analysis research has a focus on developing automatic methods for the proper understanding and classification of metaphoric language use, offensive content, financial sentiment and informal health reporting. In multilinguality, we focus on the integration of knowledge graphs into neural approaches to machine translation, whereas in the context of dialogue systems our research is focused on knowledge learning for goal-oriented dialogue as well as on emotional state identification in computer-human interaction.

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The Unit for Natural Language Processing at the Data Science Institute, National University of Ireland Galway has a focus on research in knowledge extraction, social media analysis, multilinguality and dialogue systems. In our knowledge extraction research, we develop methods for the extraction, representation, integration and exploitation of meaning from unstructured and semi-structured text data. Our demonstrator system <a href="https://github.com/insight-centre/saffron/">Saffron</a> encapsulates this into a readily available tool for knowledge graph extraction. Our social media analysis research has a focus on developing automatic methods for the proper understanding and classification of metaphoric language use, offensive content, financial sentiment and informal health reporting. In multilinguality, we focus on the integration of knowledge graphs into neural approaches to machine translation, whereas in the context of dialogue systems our research is focused on knowledge learning for goal-oriented dialogue as well as on emotional state identification in computer-human interaction.

At the Unit for Reasoning, Querying & Real-time Data Analytics we focus on scalable and interoperable ways of transforming web data streams into actionable knowledge, leveraging Linked Open Data and Standards for data representation and management. At the processing level, we tackle crucial challenges for enabling IoT-intelligence, including large-scale stream query processing and optimization, hybrid reasoning involving inductive learning and deduction processes, quality-aware query federation, adaptive stream processing, stream reasoning and the application of those in Smart Cities, eHealth, Smart Manufacturing, Smart Farming and Enterprise Communication Systems.

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At the Unit for Reasoning, Querying & Real-time Data Analytics we focus on scalable and interoperable ways of transforming web data streams into actionable knowledge, leveraging Linked Open Data and Standards for data representation and management. At the processing level, we tackle crucial challenges for enabling IoT-intelligence, including large-scale stream query processing and optimization, hybrid reasoning involving inductive learning and deduction processes, quality-aware query federation, adaptive stream processing, stream reasoning and the application of those in Smart Cities, eHealth, Smart Manufacturing, Smart Farming and Enterprise Communication Systems.

Given the global growth and pervasiveness of social media, the Unit for Social Semantics (USS) at NUI Galway was set up to look at social media from a unique standpoint: building the technologies to analyse and improve social systems, but also developing an understanding of how people/organisations are using social media to collaborate around shared goals and collectively drive social change and innovation. The Unit provides a focussed hub for Social Semantic Web research, such that it can be referred to nationally and internationally as a contact point for those interested in both the technologies and the understanding of social media. The fusion of technology-oriented research along with the social science and information systems research at NUI Galway provides the necessary combination of intellectual capital and diverse research environments that are required for social media research activities. The Unit works with many international partners and is a leader of and/or member of various working groups that enable it to stay abreast of and involved in emerging activities. USS is the longest established research group within the Institute, set up as the Social Software Subcluster in 2005. http://socialsemantics.org/

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Given the global growth and pervasiveness of social media, the Unit for Social Semantics (USS) at NUI Galway was set up to look at social media from a unique standpoint: building the technologies to analyse and improve social systems, but also developing an understanding of how people/organisations are using social media to collaborate around shared goals and collectively drive social change and innovation. The Unit provides a focussed hub for Social Semantic Web research, such that it can be referred to nationally and internationally as a contact point for those interested in both the technologies and the understanding of social media. The fusion of technology-oriented research along with the social science and information systems research at NUI Galway provides the necessary combination of intellectual capital and diverse research environments that are required for social media research activities. The Unit works with many international partners and is a leader of and/or member of various working groups that enable it to stay abreast of and involved in emerging activities. USS is the longest established research group within the Institute, set up as the Social Software Subcluster in 2005. http://socialsemantics.org/

The Unit for Linguistic Data (ULD) is concerned with the creation, improvement and maintenance of linguistic data (also known as language resources) through a variety of methods. The term linguistic data refers to a range of data types that are of use to researchers in linguistics and natural language processing (NLP). Principally, linguistic data can be split into four major categories: firstly, lexical data contains descriptions of words and their meanings, syntax and relations; secondly, corpora consist of collections of texts made for a particular purpose; thirdly, language descriptions document typological properties of language to enable comparative studies; and finally, metadata about language resources and their availability. As a primary research method, this group is focussed on exploring the use of linked data technologies, that is Linguistic Linked Open Data (LLOD), as a method of processing linguistic data. This has led to the development of several key tools and resources that use linked data as a key part of its mechanism. One such tool, the Naisc tool is a novel tool developed by the group for linking together resources of different kinds and has been applied to the task of linking lexicographical resources in the context of the ELEXIS project. Another tool, Teanga, enables the construction of pipelines of NLP tools that can be composed and integrated through the use of linked data and standards for linguistic data, such as the OntoLex-Lemon standard developed in this project. Finally, ULD maintains and develops several catalogues for the discovery of resources of linguistic data, including the Linghub website as well as the Linked Open Data Cloud and its Linguistic Linked Open Data Subcloud. In the context of the Prêt-à-LLOD project, ULD is further exploring how the quality and availability of resources can be improved. One of the major applications of linguistic data is the use of already developed NLP technologies to new languages and domains. As such, a major part of this group's work is on under-resourced languages, and there is much ongoing work on the development of technologies for minority languages as well as an active collaboration with the Irish Department and the Moore Institute on the development of NLP techniques for historical languages, in particular Old Irish. Furthermore, the unit is working on expanding WordNet to many under-resourced languages by means of machine translation.

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The Unit for Linguistic Data (ULD) is concerned with the creation, improvement and maintenance of linguistic data (also known as language resources) through a variety of methods. The term linguistic data refers to a range of data types that are of use to researchers in linguistics and natural language processing (NLP). Principally, linguistic data can be split into four major categories: firstly, lexical data contains descriptions of words and their meanings, syntax and relations; secondly, corpora consist of collections of texts made for a particular purpose; thirdly, language descriptions document typological properties of language to enable comparative studies; and finally, metadata about language resources and their availability. As a primary research method, this group is focussed on exploring the use of linked data technologies, that is Linguistic Linked Open Data (LLOD), as a method of processing linguistic data. This has led to the development of several key tools and resources that use linked data as a key part of its mechanism. One such tool, the Naisc tool is a novel tool developed by the group for linking together resources of different kinds and has been applied to the task of linking lexicographical resources in the context of the ELEXIS project. Another tool, Teanga, enables the construction of pipelines of NLP tools that can be composed and integrated through the use of linked data and standards for linguistic data, such as the OntoLex-Lemon standard developed in this project. Finally, ULD maintains and develops several catalogues for the discovery of resources of linguistic data, including the Linghub website as well as the Linked Open Data Cloud and its Linguistic Linked Open Data Subcloud. In the context of the Prêt-à-LLOD project, ULD is further exploring how the quality and availability of resources can be improved. One of the major applications of linguistic data is the use of already developed NLP technologies to new languages and domains. As such, a major part of this group's work is on under-resourced languages, and there is much ongoing work on the development of technologies for minority languages as well as an active collaboration with the Irish Department and the Moore Institute on the development of NLP techniques for historical languages, in particular Old Irish. Furthermore, the unit is working on expanding WordNet to many under-resourced languages by means of machine translation.

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The primary focus of the Biomedical Discovery Informatics unit at Data Science Institute of NUI Galway are cutting-edge AI and machine learning solutions motivated by and validated within a wide range of practically-relevant life science use cases. This research falls under the general umbrella of discovery informatics that has recently emerged as a field that explores the potential of applying various computer science technologies like Semantic Web, big data analytics, AI or machine learning to interdisciplinary challenges in turning data and information into actual knowledge. Life sciences are one of the most relevant application areas of discovery informatics, both in terms of interesting research problems and societal impact opportunities. This is reflected by the strategic vision of the unit that is to deliver novel scientific results with substantial impact potential in life sciences and healthcare. The core research topics of the unit are as follows: Representation learning for biomedical data (including networked data, protein/gene sequences, etc.); Network analytics for biomedical data; Supervised, semi-supervised, unsupervised and reinforcement learning models for sparsely annotated and/or noisy data (primarily focusing on open biomedical data); Knowledge graph generation (transformation of legacy data into knowledge graphs, integration, cleansing); Knowledge graph embeddings (statistical relational learning models, focused primarily on link prediction and knowledge base completion, but also on more advanced issues like discovery of causal relationships); Mixed embedding models for knowledge graph and unstructured (primarily text) data; Explainable AI (with special focus on hybrid approaches combining latent and graph feature models); Informing explainable / predictive models with background domain knowledge; Augmenting standard deep learning models by knowledge graphs. Selected application areas include: Drug repurposing and discovery; Prediction of adverse drug effects (including polypharmacy scenarios); Cellular signalling prediction (with specific focus on pathways prevalent in cancer and neurodegenerative diseases); General-purpose predictive “shells” and/or software libraries for processing biomedical data; Survival analysis (with specific focus on models based on integrated multi-omics data); Clinical decision support systems (with specific focus on precision medicine).

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The primary focus of the Biomedical Discovery Informatics unit at Data Science Institute of NUI Galway are cutting-edge AI and machine learning solutions motivated by and validated within a wide range of practically-relevant life science use cases. This research falls under the general umbrella of discovery informatics that has recently emerged as a field that explores the potential of applying various computer science technologies like Semantic Web, big data analytics, AI or machine learning to interdisciplinary challenges in turning data and information into actual knowledge. Life sciences are one of the most relevant application areas of discovery informatics, both in terms of interesting research problems and societal impact opportunities. This is reflected by the strategic vision of the unit that is to deliver novel scientific results with substantial impact potential in life sciences and healthcare. The core research topics of the unit are as follows: Representation learning for biomedical data (including networked data, protein/gene sequences, etc.); Network analytics for biomedical data; Supervised, semi-supervised, unsupervised and reinforcement learning models for sparsely annotated and/or noisy data (primarily focusing on open biomedical data); Knowledge graph generation (transformation of legacy data into knowledge graphs, integration, cleansing); Knowledge graph embeddings (statistical relational learning models, focused primarily on link prediction and knowledge base completion, but also on more advanced issues like discovery of causal relationships); Mixed embedding models for knowledge graph and unstructured (primarily text) data; Explainable AI (with special focus on hybrid approaches combining latent and graph feature models); Informing explainable / predictive models with background domain knowledge; Augmenting standard deep learning models by knowledge graphs. Selected application areas include: Drug repurposing and discovery; Prediction of adverse drug effects (including polypharmacy scenarios); Cellular signalling prediction (with specific focus on pathways prevalent in cancer and neurodegenerative diseases); General-purpose predictive “shells” and/or software libraries for processing biomedical data; Survival analysis (with specific focus on models based on integrated multi-omics data); Clinical decision support systems (with specific focus on precision medicine).

The Unit for Information Mining and Retrieval is focused on mining for and retrieving the latent useful information which is often hidden in linked and network data. Research topics include: Web and Data Mining, Dynamic Graph and Network Analysis, Modelling Behavioural Dynamics in Social Networks, Cross-community Analysis, Modelling and Visualisation, Graph-based Recommendation and Adaptive Personalisation, Linked-data Analytics, Graph-based approaches to topic disambiguation, linking and labelling, Real time social media analytics

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The Unit for Information Mining and Retrieval is focused on mining for and retrieving the latent useful information which is often hidden in linked and network data. Research topics include: Web and Data Mining, Dynamic Graph and Network Analysis, Modelling Behavioural Dynamics in Social Networks, Cross-community Analysis, Modelling and Visualisation, Graph-based Recommendation and Adaptive Personalisation, Linked-data Analytics, Graph-based approaches to topic disambiguation, linking and labelling, Real time social media analytics