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Professor David Lewis

Professor In (Computer Science)
OREILLY INSTITUTE
      
Profile Photo

Professor David Lewis

Professor In (Computer Science)
OREILLY INSTITUTE

 dave.lewis@tcd.ie

 3531896 8428


Dave Lewis is an Associate Professor at the School of Computer Science and Statistics at Trinity College Dublin where he served as the head of its Artificial Intelligence Discipline. He is the Acting Director of Ireland"s ADAPT Centre for human centric AI and digital content technology research. He investigates open semantic models for trustworthy AI and data governance and contributes to international standards in digital content processing and trustworthy AI. His research focuses on the use of open semantic models to manage the Data Protection and Data Ethics issues associated with digital content processing. He has led the development of international standards in AI-based linguistic processing of digital content at the W3C and OASIS and is currently active in international standardisation of Trustworthy AI at ISO/IEC JTC1/SC42 and CEN/CENELEC JTC21.
  Automated service composition   Autonomic Computing   Communication engineering, technology   Distributed systems   Internet technologies   Knowledge and data engineering   Knowledge based networking   Knowledge Management   Knowledge Representation   MOBILE COMMUNICATION   Mobile Communications   Network management   Networks and telecommunications research   Pervasive Computing   Policy based management   SEMANTIC RULES   Semantic Web   Service Management   Software Engineering   Systems analysis and models development   Ubiquitous Computing   Virtual Organisations   Virtual Reality
Project Title
 ADAPT Centre
From
1 Jan 2015
To
31 Dec 2020
Summary
The ADAPT Centre will address the grand challenge in Digital Platform Content and Applications: how can enterprises, institutions and individuals easily assimilate, reuse and interact with the global torrent of digital content? Transitioning from the Centre for Global Intelligent Content(CNGL), ADAPT will build upon demonstrated, world-recognised research and significant commercial impacts of the multidisciplinary applicant group to develop breakthroughs in Global Digital Content technology, which will turn this heterogeneous torrent of content into meaningful global conversations between organisations and users. This will be achieved by making unprecedented advances in the areas of multilingual natural language processing (NLP), multimedia content analysis and transformation, personalisation and multimodal delivery. The ADAPT Centre's research programme will enable: deeper understanding of multilingual content through multilingual NLP; dynamic transformation of multimedia content to break down language and cultural barriers; personalisation of the user experience to ensure rapid assimilation and reuse of content; and multimodal/multimedia interaction with global content for natural user engagement. ADAPT will deliver measurable world-class research and economic impact: 1650 publications, 200 high-quality postgraduates, 12 spin-out companies, 80 licences to industry and over 500 jobs. This will revolutionise Global Digital Content R&D with unprecedented societal impact and benefit to the Irish economy, and beyond. The ADAPT Centre helps industry and individuals understand, manage and control the overwhelming amount of content with which we are all increasingly confronted. It revolutionises conventional models of content production, analysis and delivery by embedding rich knowledge, automatic understanding, translation, personalisation and delivery capabilities in Global content. ADAPT empowers enterprises, institutions and individuals to seamlessly create, modify and share content in the way most suited to their preferences and needs. It unites world-leading experts to concretely address important research and industry needs identified by the Irish Government. ADAPT will deliver cutting-edge R&D with benefits to Irish industries, institutions and individuals.
Funding Agency
Science Foundation Ireland
Programme
SFI Research Centre
Project Type
SFI Research Centre
Project Title
 CNGL, The Centre for Next Generation Localisation
From
2013
To
2015
Summary
SFI CSET 2nd Plase funding: One of the most significant changes to people's lives in recent years has been the explosion of content available to users, enterprises and communities. The Content Revolution is at the heart of a new, empowering trend which sees enterprises and users in new roles as creators, curators and consumers of content, in social and corporate contexts. The scale of this change is astonishing: over 175 million tweets will be sent per day in 2012 (Solis, 2012), with the majority in languages other than English. Today Facebook alone is larger than the entire World Wide Web (WWW) in 2004 (Solis, 2011). New content is not restricted to text, but includes richer media, with 60 hours of video uploaded every minute, or one hour every second to YouTube. (YouTube, 2012). Filtering and personalised retrieval are emerging as key trends in accessing content, with Google using as many as 57 different signals to influence search (Pariser, 2011)(Boyd, 2011). However, these trends do not address the desire of organisations, communities and individuals for content and services to be delivered in their own language, according to their own needs, preferences and context. Fundamental challenges arise in the way content can be dynamically created, curated, processed and delivered for global usage. CNGL II (Centre for Next Generation Localisation II) seeks to revolutionise the way people can seamlessly interact with content, systems and each other to achieve unprecedented levels of access, efficiency and empowerment. Breakthroughs in the processing, unification and seamless integration of multilingual, multi-modal and multimedia content are key goals in enabling the global content industry and society of the future. CNGL II will pioneer a new concept called 'Global Intelligent Content'. Global Intelligent Content will enable digital content to be more discoverable, adaptable and repurposeable. By embedding new levels of knowledge and intelligence into the content, it will enable advanced intelligent content services to automatically process and transform that content. Global Intelligent Content will thus enable itself to be actively discovered and transformed by advanced intelligent content services so as to make that content more discoverable, semantically rich, adaptable, contextually aware and reusable across global markets. This represents a major shift in the way content is managed, creating a more robust, agile and interoperable relationship between content and the value chain of services acting upon it. In this way Global Intelligent Content can be dynamically transformed based on current user interaction, perceived user intention or current delivery context.
Funding Agency
SFI
Programme
CSET
Project Title
 Centre for Next Generation Localisation
From
1st December 2007
To
30th November 2012
Summary
Language barriers constitute a formidable obstacle to the free flow of information, products and services in an increasingly globalised economy and information society. "Localisation" refers to the process of adapting digital content to culture, locale and linguistic environments at high quality and speed. Localisation is a key enabling, value-adding, multiplier component of the global software and content distribution industry. Localisation seeks to overcome language barriers. The Centre for Next Generation Localisation (CNGL) is a Academia-Industry partnership with over 100 researchers developing novel technologies addressing the key localisation challenges of volume, access and personalisation. Its objective is to produce substantial advances in the basic and applied research underpinning the design, implementation and evaluation of the blueprints for the Next Generation Localisation Factory. Our mission is to revolutionise localisation via breakthroughs in automation, composition and integration, focusing on: (i)Integrated machine translation technology, (ii) Speech-based interfaces and more personalised speech output, (iii) Multilingual digital content management for personalised multilingual content access and delivery, (iv) Localisation workflows and system integration. Dr Lewis' team focusses on the latter, systems integration, including the models and methods for the rapid integration of workflows, service oriented architectures and service management.
Funding Agency
Science Foundation Ireland
Programme
Centres for Science, Engineering and Technology
Project Title
 FALCON
From
1 Oct 2013
To
30 sept 2015
Summary
Federated Active Linguistic data CuratiON: This project will deliver a modular software platform that will enable SMEs in the localisation industry to leverage open language resources published by public bodies. This platform will bootstrap a commercially sustainable localisation process that allows SME Language Service Providers (LSPs) to continuously reuse the output of web content translation projects. By integrating these outputs with language resources from public bodies, SME LSPs can easily and cheaply optimise and tailor open source machine translation and automated terminology extraction components to the needs of their customers. Today, the curation of parallel text involves the publication of the undifferentiated results of translation projects in differing formats or via centralised repositories. This restricts SMEs in leveraging these resources to train machine translation engines to match incoming translation projects. FALCON will enable the controlled sharing and reuse of language resources, combining open corpora from public bodies with richly annotated output from commercial translation projects. Federated access control will enable sharing and reuse of commercial resources while respecting business partnerships, client relationships, competitive and licensing concerns. Fine grained traceability language resource reuse will enable return on investment calculations on resource curation in public bodies and SME LSPs alike. The consortium combines academic expertise in localisation interoperability, linked data management, statistical machine translation and statistical parsing with SMEs offering services and solutions for translation and terminology management. This project will enable the language service industry to realise its potential as a major sources of shared linked data by measurably improving cost, speed and quality benefits of reuse. The consortium members will then be strongly placed to commercially exploit these benefits.
Funding Agency
EU
Programme
FP7-ICT-2013-SME-DCA
Project Type
STREP
Project Title
 LIDER
From
1 Nov 2013
To
31 Oct 2015
Summary
Linked Data as an enabler of cross-media and multilingual content analytics for enterprises across Europe: The explosive growth of content in volume, velocity and variety on the Web demands new approaches to content analytics, addressing issues in large scale analysis and interpretation of heterogeneous data sets, originating in different media, human languages, jurisdictions, etc. Among these, language diversity in particular has become a ubiquitous aspect of the Web in light of increasing globalization. Recently, semantic-level, i.e., language- and media-independent, data analysis and representation methods such as those provided by Linked Data and Semantic Web technologies, have been introduced to provide innovative content analytics solutions for such heterogeneous, multilingual and multimedia content. An important missing component however, is the representation of language- and media-specific information that will be needed for interpreting such data correctly - across different media and across the increasing variety of human languages used nowadays on the Web. In this proposal we therefore aim at addressing this missing component by providing an ecosystem for the establishment of a new Linked Open Data (LOD) based ecosystem of free, interlinked, and semantically interoperable language (corpora, dictionaries, lexical and syntactic metadata, etc.) and media (image, video, etc. metadata) resources that will allow for free and open exploitation of such resources in multilingual, cross-media content analytics across the EU and beyond, with specific use cases in different industries. LIDER addresses three key objectives: 1. To support multilingual and multimedia content analytics in enterprises. 2. To provide a discussion platform for the development of novel LOD based multilingual, cross-media content analytics systems that will be able to use Free, Open and Interoperable language and media resources and services. 3. To reach consensus on short and long term goals in LOD-based multilingual, cross-media content analytics in enterprises.
Funding Agency
EU
Programme
FP7-ICT-2013-10
Project Type
Coordination and Support Action

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Details Date
Head of Delegation representing Ireland at Trustworthy AI Standardisation Working Group at the ISO-IEC/JTC1 subcommittee on Artificial Intelligence Standards 2018
TCD Representative to the World Wide Web Consortium, Technical Advisory Committee Member, Co-chair Multilingual Web Language Technology Working Group 2012
Organising Committee member for the 2nd International workshop Management of Ubiquitous Communications and Service (MUCS)
Reviewer for IEEE Communications Magazine; Proceedings of the IEEE; IEEE Pervasive Computing; Communications Networks; Journal of Network and System Management; Journal of Autonomic and Trusted Computing, and Journal of Computer Science.
Local Arrangements Chair for IEEE Vehicular Technology Conference, Dublin April 2007
Board member and Architecture Committee co-chair of the Autonomic Communication Forum
Editorial board member for Springer's Journal of Network and System Management
Details Date From Date To
Member of the Association of Computing Machinery (ACM)
Member of the Institute of Electrical and Electronics Engineers (IEEE)
Member of the Institute of Electric Engineers (IEE)
Golpayegani, D. and Esteves, B. and Pandit, H.J. and Lewis, D., AIUP: an ODRL Profile for Expressing AI Use Policies to Support the EU AI Act, 3759, 2024, Notes: [cited By 0], Conference Paper, PUBLISHED
Golpayegani, D. and Hupont, I. and Panigutti, C. and Pandit, H.J. and Schade, S. and O†Sullivan, D. and Lewis, D., AI Cards: Towards an Applied Framework for Machine-Readable AI and Risk Documentation Inspired by the EU AI Act, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14831 LNCS, 2024, p48-72 , Notes: [cited By 1], Journal Article, PUBLISHED  DOI
Delaram Golpayegani, Harshvardhan J. Pandit, Dave Lewis, To Be High-Risk, or Not To Be-Semantic Specifications and Implications of the AI Act's High-Risk AI Applications and Harmonised Standards, ACM Conference on Fairness, Accountability, and Transparency (FAccT), Chicago, IL, 12-15 June, 2023, Conference Paper, IN_PRESS  TARA - Full Text
Saxena, Deepak and Wall, P. J. and Lewis, Dave, 2023 IEEE International Symposium on Technology and Society (ISTAS), 2023 IEEE International Symposium on Technology and Society (ISTAS), 2023, pp1-5 , Conference Paper, PUBLISHED  DOI
Beatriz Esteves, Víctor Rodríguez Doncel, Harshvardhan J. Pandit, Dave Lewis, Semantics for Implementing Data Reuse and Altruism Under EU"s Data Governance Act, International Conference on Semantic Systems (SEMANTiCS), Leipzig, Germany, 15 aug 2023, 2023, pp210 - 226, Conference Paper, PUBLISHED  DOI
Saxena, D. and Wall, P.J. and Lewis, D., Artificial Intelligence (AI) Ethics: A Critical Realist Emancipatory Approach, 2023, Notes: [cited By 0], Conference Paper, PUBLISHED  DOI
Cathy Roche, PJ Wall, Dave Lewis, Ethics and diversity in artificial intelligence policies, strategies and initiatives, AI and Ethics, 3, 2023, p1095 - 1115, Journal Article, PUBLISHED  DOI
H.Asgarinia, A.Chomczyk Penedo, B.Esteves, D.Lewis, "Who Should I Trust with My Data?" Ethical and Legal Challenges for Innovation in New Decentralized Data Management Technologies, Information, 14, (7), 2023, Journal Article, PUBLISHED  DOI
Asgarinia, H. and Chomczyk Penedo, A. and Esteves, B. and Lewis, D., †Who Should I Trust with My Data?†Ethical and Legal Challenges for Innovation in New Decentralized Data Management Technologies, Information (Switzerland), 14, (7), 2023, Notes: [cited By 3], Journal Article, PUBLISHED  DOI
Delaram Golpayegani, Comparison and Analysis of 3 Key AI Documents: EU's Proposed AI Act, Assessment List for Trustworthy AI (ALTAI), and ISO/IEC 42001 AI Management System, 30th Irish Conference on Artificial Intelligence and Cognitive Science (AICS), Ireland, 8-9 December 2022, 2022, Conference Paper, PUBLISHED
  

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Standardization and the Governance of Artificial Intelligence Standards in, editor(s)Deborah C Poff, Alex C. Michalos , Encyclopedia of Business and Professional Ethics, Springer, 2021, [Dave Lewis, Harshvardhan J. PanditP. J. Wall, David Filip], Book Chapter, PUBLISHED
Dave Lewis, Identifying and managing your data: Questions & Answers, European Language Resource Coordination workshop Dublin, Dublin, 13 October, 2017, DCU, Notes: [event report: http://www.lr-coordination.eu/sites/default/files/Ireland2/ELRC%2B%20Ireland%20Workshop%20Report-Public_0.pdf], Invited Talk, PUBLISHED
D. Lewis, S. Dobson, Guest Editorial: Autonomic Pervasive and Context-Aware Systems, Journal of Network and Systems Management, 15, (1), 2007, Journal Article, PUBLISHED
J. Keeney, D. Lynch, D. Lewis, D. O'Sullivan, On the Role of Ontological Semantics in Routing Contextual Knowledge in Highly Distributed Autonomic Systems, Department of Computer Science, Trinity College Dublin, 2006, (Technical Report (TCD-CS-2006-15)), Report, PUBLISHED
D. Lewis, Panel Report: "How the Autonomic Network Interacts with the Knowledge Plane?", 1st IFIP WG6.6 International Workshop on Autonomic Communication (WAC 2004), Berlin, Germany, 18-19 October 2004, LNCS 3457, Springer, 2005, pp275 - 278, Conference Paper, PUBLISHED
D. Lewis, Adaptive Systems for Ubiquitous Computing: Chair's introductions to the invited workshop on Adaptive Systems for Ubiquitous Computing, International Symposium of Information and Communications Technologies, Dublin, Ireland, 24-26 September 2003, 2003, pp164 - 164, Conference Paper, PUBLISHED
Service Management in, editor(s)J. Hall , Management of Telecommunication Systems and Services: Modelling and Implementing TMN-based Multi-domain Management, Springer-Verlag, 1996, pp41 - 120, [David Lewis], Notes: [Lecture Notes in Computer Science 1116 ISBN 3-540-61578-4 ], Book Chapter, PUBLISHED

  


My research interest is in Management Knowledge, and how it can be captured, modelled, analysed and exchanged in decentralised decision-making settings. I am therefore interested in developing data and knowledge engineering techniques for management knowledge that is used to achieve shared goals in situations where decision-making authority is not centralised, but diffused across multiple individuals or organisations. For such collaborative decision-making, I investigate the use of declarative knowledge techniques, including open data formats, ontological knowledge formats such as description logic and declarative operational instructions such as policy rules, service specifications and workflow models. The effectiveness of employing these techniques is measured through: i) The ease with which software engineers can integrate knowledge sharing models with native data models existing in legacy and proprietary management systems. ii) The additional computational and data retrieval loads imposed by employing declarative, knowledge-based representations of management information, compared to existing solutions with implicit representations. iii) The reduction in cognitive load for human decision makers against their need to compensate for the shortfalls exhibited by automated decision support that from components leveraging explicit management knowledge, e.g. in accuracy of machine-learning and natural language processing (NLP) components and the limits in domain knowledge completeness in reasoning components. iv) The degree to which decision-makers can assimilate knowledge in making accurate and timely management decisions and the impact of personalised and interactive data visualsiation techniques. Application of these techniques and their evaluation has had impact in the following application areas: a) Telecommunications Management: I was an early proponent of the extensible mark-up language for management information exchange and services, highlighting the performance and engineering tradeoffs. This approach is now common place in the telecoms industry, where long value chains are common. I also championed the topic of open semantic knowledge formats and issues of federated management in the research community. b) Smart Building Management: Here we have used open semantic models and 3D interactive data representations to enable building occupants and building managers to reconcile the management of several heterogeneous heating, ventilation and air conditioning systems; location tracking and environmental sensors and electronic access control systems to achieve physical security, comfort and energy efficiency. c) Online Communities: These exhibit a high degree decentralisation in managing the conduct of the community and the creation and sharing of digital resources, here we developed and evaluated a novel set of design considerations for developing community data visualisation tools to assist community decision-making. d) Intelligent Content Processing: This field addresses the application of NLP and personalisation technology to the processing of digital content, primarily in applications aimed at engaging with customers, citizens and leaners online. This is the core topic of CNGL and the new ADAPT Centre. Here I have pioneered the use of open semantic models to monitor the interplay between human language workers (e.g. translators & terminologists) and NLP components, and curating the result to improve those components. I have therefore demonstrated a substantial and sustained research output in decentralised management knowledge applied to a number of domains.