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1. Preface of the Proceedings of the 1st International Workshop on Semantic Technologies for Scientific, Technical and Legal Data co-located with Extended Semantic Web Conference 2023

Author

Alam, M, Trojahn, C, Hertling, S, Pesquita, C, Aebeloe, C, Aras, H, Azzam, A, Cano, J, Domingue, J, Gottschalk, S, Hartig, O, Hose, K, Kirrane, S, Lisena, P, Osborne, F, Rohde, P, Steels, L, Taelman, R, Third, A, Tiddi, I, Turker, R, Dessi, R, Dessi R., Alam M., Osborne F., Aras H., Alam, M, Trojahn, C, Hertling, S, Pesquita, C, Aebeloe, C, Aras, H, Azzam, A, Cano, J, Domingue, J, Gottschalk, S, Hartig, O, Hose, K, Kirrane, S, Lisena, P, Osborne, F, Rohde, P, Steels, L, Taelman, R, Third, A, Tiddi, I, Turker, R, Dessi, R, Dessi R., Alam M., Osborne F., and Aras H.

Published
2023

3. Knowledge graphs

Author

Hogan, A, Blomqvist, E, Cochez, M, D'Amato, C, Melo, G, Gutierrez, C, Kirrane, S, Gayo, J, Navigli, R, Neumaier, S, Ngomo, A, Polleres, A, Rashid, S, Rula, A, Schmelzeisen, L, Sequeda, J, Staab, S, Zimmermann, A, Hogan A., Blomqvist E., Cochez M., D'Amato C., Melo G. D., Gutierrez C., Kirrane S., Gayo J. E. L., Navigli R., Neumaier S., Ngomo A. -C. N., Polleres A., Rashid S. M., Rula A., Schmelzeisen L., Sequeda J., Staab S., Zimmermann A., Hogan, A, Blomqvist, E, Cochez, M, D'Amato, C, Melo, G, Gutierrez, C, Kirrane, S, Gayo, J, Navigli, R, Neumaier, S, Ngomo, A, Polleres, A, Rashid, S, Rula, A, Schmelzeisen, L, Sequeda, J, Staab, S, Zimmermann, A, Hogan A., Blomqvist E., Cochez M., D'Amato C., Melo G. D., Gutierrez C., Kirrane S., Gayo J. E. L., Navigli R., Neumaier S., Ngomo A. -C. N., Polleres A., Rashid S. M., Rula A., Schmelzeisen L., Sequeda J., Staab S., and Zimmermann A.

Abstract

In this article, we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models, as well as languages used to query and validate knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We conclude with high-level future research directions for knowledge graphs.

Published
2021

4. A decade of Semantic Web research through the lenses of a mixed methods approach

Author

Kirrane, S, Sabou, M, Fernandez, J, Osborne, F, Robin, C, Buitelaar, P, Motta, E, Polleres, A, Kirrane S, Sabou M, Fernandez JD, Osborne F, Robin C, Buitelaar P, Motta E, Polleres A, Kirrane, S, Sabou, M, Fernandez, J, Osborne, F, Robin, C, Buitelaar, P, Motta, E, Polleres, A, Kirrane S, Sabou M, Fernandez JD, Osborne F, Robin C, Buitelaar P, Motta E, and Polleres A

Abstract

The identification of research topics and trends is an important scientometric activity, as it can help guide the direction of future research. In the Semantic Web area, initially topic and trend detection was primarily performed through qualitative, top-down style approaches, that rely on expert knowledge. More recently, data-driven, bottom-up approaches have been proposed that offer a quantitative analysis of the evolution of a research domain. In this paper, we aim to provide a broader and more complete picture of Semantic Web topics and trends by adopting a mixed methods methodology, which allows for the combined use of both qualitative and quantitative approaches. Concretely, we build on a qualitative analysis of the main seminal papers, which adopt a top-down approach, and on quantitative results derived with three bottom-up data-driven approaches (Rexplore, Saffron, PoolParty), on a corpus of Semantic Web papers published between 2006 and 2015. In this process, we both use the latter for 'fact-checking' on the former and also to derive key findings in relation to the strengths and weaknesses of top-down and bottom-up approaches to research topic identification. Although we provide a detailed study on the past decade of Semantic Web research, the findings and the methodology are relevant not only for our community but beyond the area of the Semantic Web to other research fields as well.

Published
2020

5. National scientific medical meeting 1997 abstracts

Author

Willison, H. J., Lastovica, A. J., Prendergast, M. M., Moran, A. P., Walsh, C., Flitcroft, I., Eustace, P., McMahon, C., Smith, J., Smith, O. P., Lakshmandass, G., Taylor, M. R. H., Holland, C. V., Cox, D., Good, B., Kearns, G. M., Gaffney, P., Shark, K., Frauenshuh, M., Ortmann, W., Messner, R., King, R., Rich, S., Behrens, T., Mahmud, N., Molloy, A., McPartlin, J., Scott, J. M., Weir, D. G., Walsh, K. M., Thorburn, D., Mills, P., Morris, A. J., Good, T., Cameron, S., McCruden, E. A. B., Bennett, M. W., O’Connell, J., Brady, C., Roche, D., Collins, J. K., Shanahan, F., O’Sullivant, G. C., Henry, M., Koston, S., McMahon, K., MacNee, W., FitzGerald, M. X., O’Connor, C. M., McGonagle, D., Gibbon, W., O’Connor, P., Emery, P., Murphy, M., Watson, R., Casey, E., Naidu, E., Murphy, M., Watson, R., Barnes, L., McCann, S., Murphy, M., Watson, R., Barnes, L., Sweeney, E., Barrett, E. J., Graham, H., Cunningham, R. T., Johnston, C. F., Curry, W. J., Buchanan, K. D., Courtney, C. H., McAllister, A. S., McCance, D. R., Hadden, D. R., Bell, P. M., Leslie, H., Sheridan, B., Atkinson, A. B., Kilbane, M. T., Smith, D. F., Murray, M. J., Shering, S. G., McDermott, E. W. M., O’Higgins, N. J., Smyth, P. P. A., McEneny, J., Trimble, E. R., Young, I. S., Sharpe, P., Mercer, C., McMaster, D., Young, I. S., Evans, A. E., Young, I. S., Cundick, J., Hasselwander, O., McMaster, D., McGeough, J., Savage, D., Maxwell, A. P., Evans, A. E., Kee, F., Larkin, C. J., Watson, R. G. P., Johnston, C., Ardill, J. E. S., Buchanan, K. D., McNamara, D. A., Walsh, T. N., Bouchier-Hayes, D. J., Madden, C., Timon, C., Gardiner, N., Lawler, M., O’Riordan, J., Duggan, C., McCann, S. R., Gowing, H., Braakman, E., Lawler, M., Byrne, C., Martens, A. C. M., Hagenbeek, A., McCann, S. R., Kinsella, N., Cusack, S., Lawler, M., Baker, H., White, B., Smith, O. P., Lawler, M., Gardiner, N., Molloy, K., Gowing, H., Wogan, A., McCann, S. R., McElwaine, S., Lawler, M., Hollywood, D., McCann, S. R., Mcmahon, C., Merry, C., Ryan, M., Smith, O., Mulcahy, F. M., Murphy, C., Briones, J., Gardiner, N., McCann, S. R., Lawler, M., White, B., Lawler, M., Cusack, S., Kinsella, N., Smith, O. P., Lavin, P., McCaffrey, M., Gillen, P., White, B., Smith, O. P., Thompson, L., Lalloz, M., Layton, M., Barnes, L., Corish, C., Kennedy, N. P., Flood, P., Mulligan, S., McNamara, E., Kennedy, N. P., Flood, P., Mathias, P. M., Ball, E., Duiculescu, D., Calistru, P., O’Gorman, N., Kennedy, N. P., Abuzakouk, M., Feighery, C., Brannigan, M., Pender, S., Keeling, F., Varghese, J., Lee, M., Colreavy, M., Gaffney, R., Hone, S., Herzig, M., Walsh, M., Dolan, C., Wogan, A., Lawler, M., McCann, S. R., Hollywood, D., Donovan, D., Harmey, J., Bouchier-Hayes, D. J., Haverty, A., Wang, J. H., Harmey, J. H., Redmond, H. P., Bouchier-Hayes, D. J., McGreal, G., Shering, S. G., Moriarty, M. J., Shortt, A., Kilbane, M. T., Smith, D. F., McDermott, E. W. M., O’Higgins, N. J., Smyth, P. P. A., McNamara, D. A., Harmey, J., Wang, J. H., Donovan, D., Walsh, T. N., Bouchier-Hayes, D. J., Kay, E., Pidgeon, G., Harmey, J., McNamara, D. A., Bouchier-Hayes, D. J., Dunne, P., Lambkin, H., Russell, J. M., O’Neill, A. J., Dunne, B. M., O’Donovan, M., Lawler, M., Gaffney, E. F., Gillan, J. E., Cotter, T. G., Horan, J., Jones, D., Biswas, S. K., Mulkerrin, E. C., Brady, H., O’Donnell, J., Neary, J., Healy, E., Watson, A., Keogh, B., Ryan, M., Cassidy, C., Ward, S., Stokes, E., Keoghan, F., Barrett, A., O’Connell, P., Ryall, N., O’Connell, P. A., Jenkinson, A., O’Brien, T., O’Connell, P. G., Harrison, R., Barrett, T., Bailey, D. M. D., Butler, A., Barton, D. E., Byrne, C., McElwaine, S., McCann, S. R., Lawler, M., Cusack, S., Lawler, M., White, B., Smith, O. P., Daly, G., Gill, M., Heron, S., Hawi, Z., Fitzgerald, M., Hawi, Z., Mynett-Johnson, L., Shiels, D., Kendler, K., McKeon, P., Gill, M., Straub, R., Walsh, D., Ryan, F., Barton, D. E., McCabe, D., Murphy, R., Segurado, R., Mulcahy, T., Larson, B., Comerford, C., O’Connell, R., O’Mahony, E., Gill, M., Donnelly, J., Minahan, F., O’Neill, D., Farrell, Z., O’Neill, D., Jones, D., Horan, J., Glynn, C., Biswas, S. K., Mulkerrin, E., Brady, H., Lennox, S. E., Murphy, A., Rea, I. M., McNulty, H., McMeel, C., O’Neill, D., McEvoy, H., Freaney, R., McKenna, M. J., Crowe, M., Keating, D., Colreavy, M., Hone, S., Norman, G., Widda, S., Viani, L., Galvin, Nolan, C. M., Hardiman, O., Hardiman, O., Brett, F., Droogan, O., Gallagher, P., Harmey, M., King, M., Murphy, J., Perryrnan, R., Sukumaran, S., Walsh, J., Farrell, M. A., Hughes, G., Cunningham, C., Walsh, J. B., Coakley, D., O’Neill, D., Hurson, M., Flood, P., McMonagle, P., Hardiman, O., Ryan, F., O’Sullivan, S., Merry, C., Dodd, P., Redmond, J., Mulcahy, F. M., Browne, R., Keating, S., O’Connor, J., Cassidy, B. P., Smyth, R., Sheppard, N. P., Cullivan, R., Crown, J., Walsh, N., Denihan, A., Bruce, I., Radic, A., Coakley, D., Lawlor, B. A., Bridges, P. K., O’Doherty, M., Farrington, A., O’Doherty, M., Farragher, B., Fahy, S., Kelly, R., Carey, T., Owens, J., Gallagher, O., Sloan, D., McDonough, C., Casey, P., Horgan, A., Elneihum, A., O’Neill, C., McMonagle, T., Quinn, J., Meagher, D., Murphy, P., Kinsella, A., Mullaney, J., Waddington, J. L., Rooney, S., Rooney, S., Bamford, L., Sloan, D., O’Connor, J. J., Franklin, R., O’Brien, K., Fitzpatrick, G., Laffey, J. G., Boylan, J. F., Laffey, J., Coleman, M., Boylan, J., Laffey, J. G., McShane, A. J., Boylan, J. F., Loughrey, J. P. R., Gardiner, J., McGinley, J., Leonard, I., Carey, M., Neligan, P., O’Rourke, J., Cunningham, A., Fennessy, F., Kelly, C., Bouchier-Hayes, D., Fennessy, F., Wang, J. H., Kelly, C., Bouchier-Hayes, D., Fennessy, F., Wang, J. H., Kelly, C., Bouchier-Hayes, D. J., Kellett, J., Laffey, J., Murphy, D., Regan, J., O’Keeffe, D., Mahmud, A., Hemeryck, L., Feely, J., Mahmud, A., Hemeryck, L., Hall, M., Feely, J., Menown, I. B. A., Mathew, T. P., Nesbitt, G. S., Syme, M., Young, I. S., Adgey, A. A. J., Menown, I. B. A., Turtle, F., Allen, J., Anderson, J., Adgey, A. A. J., O’Hanlon, R., Codd, M. B., Walkin, S., McCann, H. A., Sugrue, D. D., Rasheed, A. M., Chen, G., Kelly, C., Bouchier-Hayes, D. J., Leahy, A., Rasheed, A. M., Kay, E., Jina, S., Bouchier-Hayes, D. J., Leahy, A., McDowell, I., Rasheed, A. M., Wang, J. H., Wo, Q., Kelly, C., Bouchier-Hayes, D. J., Leahy, A., Shuhaibar, M. N., McGovern, E., Turtle, F., Menown, I. B. A., Manoharan, G., Kirkpatrick, R., Campbell, N. P. S., Walkin, S., Codd, M. B., O’Hanlon, R., McCarthy, C., McCann, H. A., Sugrue, D. D., Wen, Y., Killalea, S., Hall, M., Hemeryck, L., Feely, J., Fahy, C. J., Griffith, A., McGinley, J., McCabe, D., Fraser, A., Casey, E., Ryan, T., Murphy, R., Browne, M., Fenton, J., Hughes, J., Timon, C. I., Fenton, J., Curran, A., Smyth, D., Viani, L., Walsh, M., Hughes, J. P., Fenton, J., Lee, P., Kelly, A., Timon, C. I., Hughes, J. P., Fenton, J., Shine, N., Blayney, A., McShane, D. P., Timon, C. I., Hussey, J., Howlett, M., Langton, A., McEvoy, A., Slevin, J., Fitzpatrick, C., Turner, M. J., Enright, F., Goggin, N., Costigan, C., Duff, D., Osizlok, P., Wood, F., Watson, R., Fitzsimons, R. B., Flanagan, N., Enright, F., Barnes, L., Watson, R., Molloy, E., Griffin, E., Deasy, P. F., Sheridan, M., White, M. J., Moore, R., Gray, A., Hill, J., Glasgow, J. F. T., Middleton, B., Slattery, D., Donoghue, V., McMahon, A., Murphy, J., Slattery, D., McCarthy, A., Oslislok, P., Duff, D., Colreavy, M., Keogh, I., Hone, S., Walsh, M., Henry, M., Koston, S., McMahon, K., MacNee, W., FitzGerald, M. X., O’Connor, C. M., Russell, K. J., Henry, M., Fitzgerald, M. X., O’Connor, C. M., Kavanagh, P. V., McNamara, S. M., Feely, J., Barry, M., O’Brien, J. E., McCormick, P., Molony, C., Doyle, R. M., Walsh, J. B., Coakley, D., Codd, M. B., O’Connell, P. R., Dowey, L. C., McGlynn, H., Thurnham, D. I., Elborn, S. J., Flynn, L., Carton, J., Byrne, B., O’Farrelly, C., Kelehan, P., O’Herlihy, C., O’Hara, A. M., Moran, A. P., Orren, A., Fernie, B. A., Merry, C., Clarke, S., Courtney, G., de Gascun, C., Mulcahy, F. M., Merry, C., Ryan, M., Barry, M., Mulcahy, F. M., Merry, C., Ryan, M., Barry, M., Mulcahy, F. M., Byrne, M., Moylett, E., Murphy, H., Butler, K., Nourse, C., Thaker, H., Barry, C., Russell, J., Sheehan, G., Boyle, B., Hone, R., Conboy, B., Butler, C., Moris, D., Cormican, M., Flynn, J., McCormack, O., Corbally, N., Murray, A., Kirrane, S., O’Keane, C., Hone, R., Lynch, S. M., Cryan, B., Whyte, D., Morris, D., Butler, C., Cormican, M., Flynn, J., Corbett-Feeney, G., Murray, A., Corbally, N., Hone, R., Mackle, T., Colreavy, M., Perkins, J., Saidlear, C., Young, A., Eustace, P., Wrigley, M., Clifford, J., Waddington, J. L., Tighe, O., Croke, D. T., Drago, J., Sibley, D. R., Feely, J., Kelly, A., Carvalho, M., Hennessy, M., Kelly, M., Feely, J., Hughes, C., Hanlon, M., Feely, J., Sabra, K., Keane, T., Egan, D., Ryan, M., Maerry, C., Ryan, M., Barry, M., Mulcahy, F. M., Maerry, C., Ryan, M., Barry, M., Mulcahy, F. M., Sharma, S. C., Williams, D., Kelly, A., Carvalho, M., Feely, J., Williams, D., Kelly, A., Carvalho, M., Feely, J., Codd, M. B., Mahon, N. G., McCann, H. A., Sugrue, D. D., Sayers, G. M., Johnson, Z., McNamara, S. M., Kavanagh, P. V., and Feely, J.

Published
1998
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6. Preface

Author

Harth, Andreas, Kirrane, Sabrina, Ngomo, Axel Cyrille Ngonga, Paulheim, Heiko, Rula, Anisa, Gentile, Anna Lisa, Haase, Peter, Cochez, Michael, Harth, A., Kirrane, S., Ngonga Ngomo, A.-C., Paulheim, H., Rula, A., Gentile, A.L., Haase, P., Cochez, M, Artificial intelligence, Network Institute, and Artificial Intelligence (section level)

Published
2020

7. SPIRIT: A Semantic Transparency and Compliance Stack

Author

Westphal, P., Javier D. Fernández, Kirrane, S., and Lehmann, J.

Subjects
ComputingMilieux_LEGALASPECTSOFCOMPUTING
Abstract

The European General Data Protection Regulation (GDPR) sets new precedents for the processing of personal data. In this paper, we propose an architecture that provides an automated means to enable transparency with respect to personal data processing and sharing transactions and compliance checking with respect to data subject usage policies and GDPR legislative obligations.

Published
2019
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8. The Semantic Web - 17th International Conference, {ESWC} 2020, Heraklion, Crete, Greece, May 31-June 4, 2020, Proceedings

Author

Andreas Harth, Sabrina Kirrane, Axel-Cyrille Ngonga Ngomo, Heiko Paulheim, Anisa Rula, Anna Lisa Gentile, Peter Haase, Michael Cochez, Harth, A, Kirrane, S, Ngonga Ngomo, A, Paulheim, H, Rula, A, Lisa Gentile, A, Haase, P, Cochez, M, Andreas Harth, Sabrina Kirrane, Axel-Cyrille Ngonga Ngomo, Heiko Paulheim, Anisa Rula, Anna Lisa Gentile, Peter Haase, Michael Cochez, Harth, A, Kirrane, S, Ngonga Ngomo, A, Paulheim, H, Rula, A, Lisa Gentile, A, Haase, P, and Cochez, M

Published
2020

9. D2.5 Policy Language V2

Author

Bonatti, P.A., Kirrane, S., Petrova, I., Sauro, L., and Schlehahn, E.

Subjects
policy language
Abstract

This is the second version of the deliverable devoted to defining SPECIAL’s policy language and related vocabularies. It refines the first version by taking into account the work on the pilots and their specific policies, plus the discussion that is taking place within the DPVCG community group of W3C.The additional needs emerging from the above activities require only one structural change to SPECIAL’s language, namely, allowing object properties to be used in the vocabularies (while so far they were only applied to policies, to specify their attributes “data category”, “purpose”,“processing”, and so on). In particular, the usage policy language grammar reported in Figure 1.1 has not been changed.1The other changes concern the need for pilot-specific terms, for more general categorizations associated to GDPR’s requirements (such as distinguishing sensitive data and criminal records,for example), and for additional concepts stemming from the GDPR, such as the legal basis for processing.The novel features are discussed in Section 9. The rest of the deliverable corresponds to the first version, with the exception of a few minor corrections.

Published
2018
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10. MantisTable: A Tool for Creating Semantic Annotations on Tabular Data

Author

Hitzler, P, Kirrane, S, Hartig, O, de Boer, V, Vidal, ME, Maleshkova, M, Schlobach, S, Hammar, K, Lasierra, N, Stadtm"uller, S, Hose, K, Verborgh, R, Cremaschi, M, Rula, A, Siano, A, De Paoli, F, Hitzler, P, Kirrane, S, Hartig, O, de Boer, V, Vidal, ME, Maleshkova, M, Schlobach, S, Hammar, K, Lasierra, N, Stadtm"uller, S, Hose, K, Verborgh, R, Cremaschi, M, Rula, A, Siano, A, and De Paoli, F

Abstract

This paper describes MantisTable, an open source Semantic Table Interpretation tool, which automatically annotates tables using a Knowledge Graph. MantisTable provides a graphical interface allowing users to analyse the results of the semantic table interpretation process and validate the final annotations. The tool also provides a guided mode for viewing and editing annotations by users. Thanks to MantisTable features, it is possible to create semantic annotations and favour the publication and exchange of tabular data.

Published
2019

11. Mining Scholarly Publications for Scientific Knowledge Graph Construction

Author

Hitzler, P, Kirrane, S, Hartig, O, de Boer, V, Schlobach, S, Vidal, ME, Maleshkova, M, Hammar, K, Lasierra, N, Stadtmüller, S, Hose, K, Verborgh, R, Buscaldi, D, Dessì, D, Motta, E, Osborne, F, Reforgiato Recupero, D, Buscaldi D, Dessì D, Motta E, Osborne F, Reforgiato Recupero D, Hitzler, P, Kirrane, S, Hartig, O, de Boer, V, Schlobach, S, Vidal, ME, Maleshkova, M, Hammar, K, Lasierra, N, Stadtmüller, S, Hose, K, Verborgh, R, Buscaldi, D, Dessì, D, Motta, E, Osborne, F, Reforgiato Recupero, D, Buscaldi D, Dessì D, Motta E, Osborne F, and Reforgiato Recupero D

Abstract

In this paper, we present a preliminary approach that uses a set of NLP and Deep Learning methods for extracting entities and relationships from research publications and then integrates them in a Knowledge Graph. More specifically, we (i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools, (ii) describe an approach for integrating entities and relationships generated by these tools, and (iii) analyse an automatically generated Knowledge Graph including 10, 425 entities and 25, 655 relationships in the field of Semantic Web.

Published
2019

12. Trust, Accountability, and Autonomy in Knowledge Graph-Based AI for Self-Determination

Author

Ibáñez, Luis-Daniel, Domingue, John, Kirrane, Sabrina, Seneviratne, Oshani, Third, Aisling, and Vidal, Maria-Esther

Subjects
trust, accountability, autonomy, ai, knowledge graphs, Electronic computers. Computer science, QA75.5-76.95
Abstract

Knowledge Graphs (KGs) have emerged as fundamental platforms for powering intelligent decision-making and a wide range of Artificial Intelligence (AI) services across major corporations such as Google, Walmart, and AirBnb. KGs complement Machine Learning (ML) algorithms by providing data context and semantics, thereby enabling further inference and question-answering capabilities. The integration of KGs with neuronal learning (e.g., Large Language Models (LLMs)) is currently a topic of active research, commonly named neuro-symbolic AI. Despite the numerous benefits that can be accomplished with KG-based AI, its growing ubiquity within online services may result in the loss of self-determination for citizens as a fundamental societal issue. The more we rely on these technologies, which are often centralised, the less citizens will be able to determine their own destinies. To counter this threat, AI regulation, such as the European Union (EU) AI Act, is being proposed in certain regions. The regulation sets what technologists need to do, leading to questions concerning How the output of AI systems can be trusted? What is needed to ensure that the data fuelling and the inner workings of these artefacts are transparent? How can AI be made accountable for its decision-making? This paper conceptualises the foundational topics and research pillars to support KG-based AI for self-determination. Drawing upon this conceptual framework, challenges and opportunities for citizen self-determination are illustrated and analysed in a real-world scenario. As a result, we propose a research agenda aimed at accomplishing the recommended objectives.

Published
2023
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14. Data privacy vocabularies and controls: Semantic web for transparency and privacy

Author

Bonatti, P. A., Bos, B., Decker, S., Javier D. Fernández, Kirrane, S., Peristeras, V., Polleres, A., and Wenning, R.

Abstract

Managing Privacy and understanding the handling of personal data has turned into a fundamental right-at least for Europeans-since May 25th with the coming into force of the General Data Protection Regulation. Yet, whereas many different tools by different vendors promise companies to guarantee their compliance to GDPR in terms of consent management and keeping track of the personal data they handle in their processes, interoperability between such tools as well uniform user facing interfaces will be needed to enable true transparency, user-configurable and -manageable privacy policies and data portability (as also implicitly promised by GDPR). We argue that such interoperability can be enabled by agreed upon vocabularies and Linked Data.

18. [Untitled]

Author

Blomqvist, Eva, Groth, Paul, de Boer, Victor, Pellegrini, Tassilo, Alam, Mehwish, Käfer, Tobias, Kieseberg, Peter, Kirrane, Sabrina, Meroño-Peñuela, Albert, and Pandit, Harshvardhan J.

Abstract

This open access book constitutes the refereed proceedings of the 16th International Conference on Semantic Systems, SEMANTiCS 2020, held in Amsterdam, The Netherlands, in September 2020. The conference was held virtually due to the COVID-19 pandemic.

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19. Knowledge Graphs

Author

Axel Polleres, Lukas Schmelzeisen, Axel-Cyrille Ngonga Ngomo, Roberto Navigli, Steffen Staab, Eva Blomqvist, Claudio Gutierrez, José Emilio Labra Gayo, Juan F. Sequeda, Sabrina Kirrane, Aidan Hogan, Sabbir M. Rashid, Michael Cochez, Claudia d'Amato, Antoine Zimmermann, Sebastian Neumaier, Gerard de Melo, Anisa Rula, Millennium Institute for Foundational Research on Data (IMFD), Pontificia Universidad Católica de Chile (UC), Linköping University (LIU), Vrije Universiteit Brussel (VUB), Discovery Lab, Polytechnic University of Bari, Rutgers University System (Rutgers), WU Vienna, Universidad de Oviedo [Oviedo], Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome], Universität Paderborn (UPB), Tetherless World Constellation, Rensselaer Polytechnic Institute (RPI), University of Milano, University of Bonn, Universität Stuttgart [Stuttgart], data.world, École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), Institut Mines-Télécom [Paris] (IMT), Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS), Ecole Nationale Supérieure des Mines de St Etienne-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)-Institut national polytechnique Clermont Auvergne (INP Clermont Auvergne), Université Clermont Auvergne (UCA)-Université Clermont Auvergne (UCA), Institut Henri Fayol (FAYOL-ENSMSE), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Département Informatique et systèmes intelligents ( FAYOL-ENSMSE), Ecole Nationale Supérieure des Mines de St Etienne, Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome] (UNIROMA), Universität Bonn = University of Bonn, Ecole Nationale Supérieure des Mines de St Etienne (ENSM ST-ETIENNE)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)-Institut national polytechnique Clermont Auvergne (INP Clermont Auvergne), Ecole Nationale Supérieure des Mines de St Etienne (ENSM ST-ETIENNE), Department of Computer and Information Science - Linköping University, Computer Systems Section - Vrije Universiteit Amsterdam, Vrije Universiteit Amsterdam [Amsterdam] (VU), University of Bari Aldo Moro (UNIBA), Department of Informatics and System Sciences (Sapienza University of Rome), Università degli Studi di Milano-Bicocca [Milano] (UNIMIB), Universität Koblenz-Landau [Koblenz], University of Southampton, Laboratoire Hubert Curien [Saint Etienne] (LHC), Institut d'Optique Graduate School (IOGS)-Université Jean Monnet [Saint-Étienne] (UJM)-Centre National de la Recherche Scientifique (CNRS), Département Informatique et systèmes intelligents (FAYOL-ENSMSE), Mines Saint-Etienne, Breuil, Florent, Hogan, A, Blomqvist, E, Cochez, M, D'Amato, C, Melo, G, Gutierrez, C, Kirrane, S, Gayo, J, Navigli, R, Neumaier, S, Ngomo, A, Polleres, A, Rashid, S, Rula, A, Schmelzeisen, L, Sequeda, J, Staab, S, and Zimmermann, A

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Graph databases, Theoretical computer science, Computer science, 505002 Data protection, Ontologie, 02 engineering and technology, computer.software_genre, Data modeling, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Machine Learning (cs.LG), 102001 Artificial intelligence, 0202 electrical engineering, electronic engineering, information engineering, Graph query language, Graph algorithms, 102015 Informationssysteme, Computer Sciences, Graph algorithm, Shape, Contrast (statistics), Databases (cs.DB), Graph neural network, Embeddings, Graph (abstract data type), Graph neural networks, Graph query languages, Knowledge graphs, Ontologies, Rule mining, Shapes, 020201 artificial intelligence & image processing, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, Embedding, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], General Computer Science, Computer Science - Artificial Intelligence, [INFO] Computer Science [cs], Theoretical Computer Science, 502050 Wirtschaftsinformatik, Computer Science - Databases, 020204 information systems, 505002 Datenschutz, Knowledge graphs, graph databases, graph query languages, shapes, ontologies, graph algorithms, embeddings, graph neural networks, rule mining, 102015 Information systems, [INFO]Computer Science [cs], graph databases, graph query languages, shapes, ontologies, graph algorithms, embeddings, graph neural networks, rule mining, Knowledge graph, Graph database, 502050 Business informatics, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Datavetenskap (datalogi), Artificial Intelligence (cs.AI), computer
Abstract

In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models and query languages that are used for knowledge graphs. We discuss the roles of schema, identity, and context in knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of knowledge graphs. We provide an overview of prominent open knowledge graphs and enterprise knowledge graphs, their applications, and how they use the aforementioned techniques. We conclude with high-level future research directions for knowledge graphs., Comment: Revision from v5: Correcting errata from previous version for entailment/models, and some other minor typos

Published
2020
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20. A decade of Semantic Web research through the lenses of a mixed methods approach

Author

Axel Polleres, Enrico Motta, Sabrina Kirrane, Cécile Robin, Paul Buitelaar, Marta Sabou, Francesco Osborne, Javier D. Fernández, Kirrane, S, Sabou, M, Fernandez, J, Osborne, F, Robin, C, Buitelaar, P, Motta, E, and Polleres, A

Subjects
Relation (database), Computer Networks and Communications, Computer science, Research Topics, 02 engineering and technology, research trend, 01 natural sciences, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, scientometric, Semantic Web, Research topic, 010401 analytical chemistry, Scientometrics, Top-down and bottom-up design, Linked data, Data science, 0104 chemical sciences, Computer Science Applications, Identification (information), Quantitative analysis (finance), Linked Data, Strengths and weaknesses, Research Trends, Information Systems
Abstract

The identification of research topics and trends is an important scientometric activity, as it can help guide the direction of future research. In the Semantic Web area, initially topic and trend detection was primarily performed through qualitative, top-down style approaches, that rely on expert knowledge. More recently, data-driven, bottom-up approaches have been proposed that offer a quantitative analysis of the evolution of a research domain. In this paper, we aim to provide a broader and more complete picture of Semantic Web topics and trends by adopting a mixed methods methodology, which allows for the combined use of both qualitative and quantitative approaches. Concretely, we build on a qualitative analysis of the main seminal papers, which adopt a top-down approach, and on quantitative results derived with three bottom-up data-driven approaches (Rexplore, Saffron, PoolParty), on a corpus of Semantic Web papers published between 2006 and 2015. In this process, we both use the latter for “fact-checking” on the former and also to derive key findings in relation to the strengths and weaknesses of top-down and bottom-up approaches to research topic identification. Although we provide a detailed study on the past decade of Semantic Web research, the findings and the methodology are relevant not only for our community but beyond the area of the Semantic Web to other research fields as well.

Published
2020

21. REWARD : ontology for reward schemes

Author

Ioannis Chrysakis, Ruben Verborgh, Theodore Patkos, Anastasia Dimou, Giorgos Flouris, Harth, A., Kirrane, S., Ngonga Ngomo, A.C., Paulheim, H., Rula, A., Gentile, A.L., Haase, P., and Cochez, M.

Subjects
Technology and Engineering, Computer science, business.industry, 05 social sciences, 02 engineering and technology, Ontology (information science), Service provider, Crowdsourcing, Data science, Domain (software engineering), Terminology, 020204 information systems, Human resource management, 0502 economics and business, 0202 electrical engineering, electronic engineering, information engineering, business, 050203 business & management, ComputingMilieux_MISCELLANEOUS
Abstract

Rewarding people is common in several contexts, such as human resource management and crowdsourcing applications. However, designing a reward strategy is not straightforward, as it requires considering different parameters. These parameters include, for example, management of rewarding tasks and identifying critical features, such as the type of rewards and possibilities such as gamification. Moreover, the lack of a common terminology introduces the problem of communication among experts and prevents integration among different reward strategies. An ontology can offer a common understanding among domain experts and flexible management of rewarding parameters. Apart from that, an ontology can also help in the interrelationship and integration between different reward schemes employed by different service providers. In this paper, we present REWARD, a general-purpose ontology for capturing various common features of diverse reward schemes. This ontology is a result of the CAP-A European project and its application to the crowdsourcing domain, but it is designed to cover different needs and domains.

Published
2020

22. Big Data and Analytics in the Age of the GDPR

Author

Piero A. Bonatti, Sabrina Kirrane, Bonatti, P. A., and Kirrane, S.

Subjects
Usage Control Policies, Data processing, Personal Data Processing, Computer science, business.industry, Big data, Anonymization, 02 engineering and technology, Explicit consent, Analytic, Data science, Compliance (psychology), Exploratory data analysis, Analytics, 020204 information systems, General Data Protection Regulation, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, GDPR, business
Abstract

The new European General Data Protection Regulation places stringent restrictions on the processing of personally identifiable data. The GDPR does not only affect European companies, as the regulation applies to all the organizations that track or provide services to European citizens. Free exploratory data analysis is permitted only on anonymous data, at the cost of some legal risks. We argue that for the other kinds of personal data processing, the most flexible and safe legal basis is explicit consent. We illustrate the approach to consent management and compliance with the GDPR being developed by the European H2020 project SPECIAL, and highlight some related big data aspects.

Published
2019

23. Mining Scholarly Publications for Scientific Knowledge Graph Construction

Author

Davide Buscaldi, Enrico Motta, Francesco Osborne, Diego Reforgiato Recupero, Danilo Dessì, Hitzler, P, Kirrane, S, Hartig, O, de Boer, V, Schlobach, S, Vidal, ME, Maleshkova, M, Hammar, K, Lasierra, N, Stadtmüller, S, Hose, K, Verborgh, R, Buscaldi, D, Dessì, D, Motta, E, Osborne, F, and Reforgiato Recupero, D

Subjects
Sociology of scientific knowledge, Information retrieval, Text mining, Computer science, business.industry, Deep learning, 010401 analytical chemistry, 020207 software engineering, 02 engineering and technology, 01 natural sciences, 0104 chemical sciences, Knowledge extraction, Knowledge graph, Knowledge representation, Learning system, 0202 electrical engineering, electronic engineering, information engineering, Graph (abstract data type), Artificial intelligence, business, Semantic Web, Natural language processing system
Abstract

In this paper, we present a preliminary approach that uses a set of NLP and Deep Learning methods for extracting entities and relationships from research publications and then integrates them in a Knowledge Graph. More specifically, we (i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools, (ii) describe an approach for integrating entities and relationships generated by these tools, and (iii) analyse an automatically generated Knowledge Graph including 10,425 entities and 25,655 relationships in the field of Semantic Web.

Published
2019
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24. MantisTable: A Tool for Creating Semantic Annotations on Tabular Data

Author

Anisa Rula, Marco Cremaschi, Alessandra Siano, Flavio De Paoli, Hitzler, P, Kirrane, S, Hartig, O, de Boer, V, Vidal, ME, Maleshkova, M, Schlobach, S, Hammar, K, Lasierra, N, Stadtm'uller, S, Hose, K, Verborgh, R, Cremaschi, M, Rula, A, Siano, A, and De Paoli, F

Subjects
Knowledge Graph, Computer science, Semantic Table Interpretation, Semantic Web, Ontology, Linked Data, Semantic annotations, Ontology (information science), Table (information), 01 natural sciences, Mode (computer interface), 0501 psychology and cognitive sciences, Graphical user interface, Interpretation (logic), Information retrieval, business.industry, 010401 analytical chemistry, 05 social sciences, Linked data, Semantic Web, Ontology, Linked Data, Knowledge Graph, Semantic Table Interpretation, Semantic annotations, 0104 chemical sciences, Knowledge graph, business, 050104 developmental & child psychology
Abstract

This paper describes MantisTable, an open source Semantic Table Interpretation tool, which automatically annotates tables using a Knowledge Graph. MantisTable provides a graphical interface allowing users to analyse the results of the semantic table interpretation process and validate the final annotations. The tool also provides a guided mode for viewing and editing annotations by users. Thanks to MantisTable features, it is possible to create semantic annotations and favour the publication and exchange of tabular data.

Published
2019

25. SAD Generator: Eating Our Own Dog Food to Generate KGs and Websites for Academic Events

Author

Anastasia Dimou, Freddy Priyatna, Juan F. Sequeda, Pieter Heyvaert, David Chaves-Fraga, Hitzler, P, Kirrane, S, Hartig, O, De Boer, V, Vidal, ME, Maleshkova, M, Schlobach, S, Hammar, K, Lasierra, N, Stadtmuller, S, Hose, K, and Verborgh, R

Subjects
Technology and Engineering, Web development, Computer science, 02 engineering and technology, SPARQL, 01 natural sciences, RDF, World Wide Web, 0202 electrical engineering, electronic engineering, information engineering, Dissemination, Semantic Web, GraphQL, Generator (computer programming), Event (computing), business.industry, 010401 analytical chemistry, 020207 software engineering, computer.file_format, RML, 0104 chemical sciences, YARRRML, Publishing, business, computer
Abstract

Nowadays, a website is used to disseminate information about an event (e.g., location, dates, time). In the academic world, it is common to develop a website for an event, such as workshops or conferences. Aligning with the “Web of data”, its dissemination should also happen by publishing the information of the event as a knowledge graph, e.g., via RDF that is available through a SPARQL endpoint or a Triple Patterns Fragment server. However, the RDF generation and website development is not always straightforward and can be time-consuming. In this demo, we present the Semantic Academic-event Dissemination (SAD) Generator for generating RDF and websites for academic events. The generator allows to (i) annotate CSV files that contain academic event data and use the annotations to generate a knowledge graph and (ii) generate a website with the information for the event querying the knowledge graph. We used our generator to generate the RDF and website of a real workshop, the KGB workshop. It can be easily reused by organizers of other academic events by simply providing the event’s information in CSV files.

Published
2019
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26. A scalable consent, transparency and compliance architecture

Author

Sabrina Kirrane, Wouter Dullaert, Uros Milosevic, Philip Raschke, Olha Drozd, Javier D. Fernández, Rigo Wenning, Piero A. Bonatti, Axel Polleres, Kirrane, S., Fernandez, J. D., Dullaert, W., Milosevic, U., Polleres, A., Bonatti, P. A., Wenning, R., Drozd, O., and Raschke, P.

Subjects
business.industry, Computer science, Control (management), Internet privacy, ComputingMilieux_LEGALASPECTSOFCOMPUTING, 02 engineering and technology, Transparency (behavior), Compliance (psychology), 020204 information systems, General Data Protection Regulation, Scalability, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Architecture, business
Abstract

In this demo we present the SPECIAL consent, transparency and compliance system. The objective of the system is to afford data subjects more control over personal data processing and sharing, while at the same time enabling data controllers and processors to comply with consent and transparency obligations mandated by the European General Data Protection Regulation. A short promotional video can be found at https://purl.com/specialprivacy/demos/ESWC2018.

Published
2018

27. Transparent Personal Data Processing: The Road Ahead

Author

Axel Polleres, Piero A. Bonatti, Sabrina Kirrane, Rigo Wenning, Bonatti, P., Kirrane, S., Polleres, A., and Wenning, R.

Subjects
Data processing, Computer science, business.industry, Logging, 02 engineering and technology, Explicit consent, Computer security, computer.software_genre, Transparency (behavior), 020204 information systems, General Data Protection Regulation, 0202 electrical engineering, electronic engineering, information engineering, Erasure, Position paper, 020201 artificial intelligence & image processing, Architecture, Telecommunications, business, computer
Abstract

The European General Data Protection Regulation defines a set of obligations for personal data controllers and processors. Primary obligations include: obtaining explicit consent from the data subject for the processing of personal data, providing full transparency with respect to the processing, and enabling data rectification and erasure (albeit only in certain circumstances). At the core of any transparency architecture is the logging of events in relation to the processing and sharing of personal data. The logs should enable verification that data processors abide by the access and usage control policies that have been associated with the data based on the data subject's consent and the applicable regulations. In this position paper, we: (i) identify the requirements that need to be satisfied by such a transparency architecture, (ii) examine the suitability of existing logging mechanisms in light of said requirements, and (iii) present a number of open challenges and opportunities.

Published
2017
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28. Towards search on encrypted graph data

Author

Kasten, Andreas, Scherp, Ansgar, Armknecht, Frederik, Krause, Matthias, Kirrane, S, Hendler, J, and Decker, S

Abstract

We present an approach where one can execute user defined SPARQL queries on encrypted graph data. The graph data is only partially revealed to those users authorised for executing a query. The approach is based on eight different types of queries, corresponding to the different binding possibilities in a single SPARQL triple pattern. The allowed queries can be further restricted by the owner of the graph data, e. g., through pre-defining a specific predicate or object. Single triple patterns can be combined to query group patterns as they can be stated in SPARQL queries and allow to execute a wide range of SELECT and ASK queries.

Published
2014

29. Towards a configurable framework for iterative signing of distributed graph data

Author

Kasten, Andreas, Scherp, Ansgar, Decker, S, Hendler, J, and Kirrane, S

Subjects
Computer Science::Digital Libraries, MathematicsofComputing_DISCRETEMATHEMATICS
Abstract

When publishing graph data on the web such as vocabularies using RDF(S) or OWL, one has only limited means to verify its authenticity and integrity. Today’s approaches require a high signature overhead and do not allow for an iterative signing of graph data. This paper presents a configurable framework for signing arbitrary graph data provided in RDF(S), Named Graphs, or OWL. Our framework supports signing graph data at different levels of granularity: minimum self-contained graphs (MSG), sets of MSGs, and entire graphs. It supports an iterative signing of graph data, e. g., when different parties provide different parts of a common graph, and allows for signing multiple graphs. Both can be done with a constant, low overhead for the signature graph, even when iteratively signing graph data.

Published
2013

31. T helper/suppressor ratio and acute attacks in multiple sclerosis.

Author

O'Keeffe F, Staunton H, Woods R, and Kirrane S

Subjects
Acute Disease, Adult, Female, Humans, Longitudinal Studies, Middle Aged, Multiple Sclerosis immunology, T-Lymphocytes, Helper-Inducer classification, T-Lymphocytes, Regulatory classification
Published
1984

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