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Lambrix, Patrick, ProfessorORCID iD iconorcid.org/0000-0002-9084-0470
Publications (10 of 137) Show all publications
Dórea, F. C., Vial, F., Hammar, K., Lindberg, A., Lambrix, P., Blomqvist, E. & Revie, C. W. (2019). Drivers for the development of an Animal Health Surveillance Ontology (AHSO). Preventive Veterinary Medicine, 166(1), 39-48
Open this publication in new window or tab >>Drivers for the development of an Animal Health Surveillance Ontology (AHSO)
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2019 (English)In: Preventive Veterinary Medicine, ISSN 0167-5877, E-ISSN 1873-1716, Vol. 166, no 1, p. 39-48Article in journal (Refereed) Published
Abstract [en]

Comprehensive reviews of syndromic surveillance in animal health have highlighted the hindrances to integration and interoperability among systems when data emerge from different sources. Discussions with syndromic surveillance experts in the fields of animal and public health, as well as computer scientists from the field of information management, have led to the conclusion that a major component of any solution will involve the adoption of ontologies. Here we describe the advantages of such an approach, and the steps taken to set up the Animal Health Surveillance Ontological (AHSO) framework. The AHSO framework is modelled in OWL, the W3C standard Semantic Web language for representing rich and complex knowledge. We illustrate how the framework can incorporate knowledge directly from domain experts or from data-driven sources, as well as by integrating existing mature ontological components from related disciplines. The development and extent of AHSO will be community driven and the final products in the framework will be open-access.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
syndromic surveillance classification, vocabulary, terminology, standards
National Category
Computer Sciences Veterinary Science
Identifiers
urn:nbn:se:liu:diva-155036 (URN)10.1016/j.prevetmed.2019.03.002 (DOI)000465055100006 ()30935504 (PubMedID)
Funder
Vinnova
Note

Funding agencies: Swedens innovation agency (VINNOVA)

Available from: 2019-03-09 Created: 2019-03-09 Last updated: 2019-07-26Bibliographically approved
Sans Fuentes, C., Carlsson, N. & Lambrix, P. (2019). Player impact measures for scoring in ice hockey. In: Dimitris Karlis, Ioannis Ntzoufras, Sotiris Drikos (Ed.), Proceedings of MathSport International 2019 Conference: . Paper presented at MathSport International Conference, Athens, 1-3 July 2019 (pp. 307-317). Athen: Athens University of Economics and Business
Open this publication in new window or tab >>Player impact measures for scoring in ice hockey
2019 (English)In: Proceedings of MathSport International 2019 Conference / [ed] Dimitris Karlis, Ioannis Ntzoufras, Sotiris Drikos, Athen: Athens University of Economics and Business , 2019, p. 307-317Conference paper, Published paper (Refereed)
Abstract [en]

A commonly used method to evaluate player performance is to attribute values to the different actions that players perform and sum up these values every time a player performs these actions. In ice hockey, such metrics include the number of goals, assists, points, plus-minus statistics and recently Corsi and Fenwick. However, these metrics do not capture the context of player actions and the impact they have on the outcome of later actions. Therefore, recent works have introduced more advanced metrics that take into account the context of the actions and perform look-ahead. The use of look-ahead is particularly valuable in low-scoring sports such as ice hockey. In this paper, we first extend a recent approach based on reinforcement learning for measuring a player's impact on a team's scoring. Second, using NHL play-by-play data for several regular seasons, we analyze and compare these and other traditional measures of player impact. Third, we introduce notions of streaks and show that these may provide information about good players, but do not provide a good predictor for the impact that a player will have the next game. Finally, streaks are compared for different player categories, highlighting differences between player positions and correlations with player salaries.

Place, publisher, year, edition, pages
Athen: Athens University of Economics and Business, 2019
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-157992 (URN)
Conference
MathSport International Conference, Athens, 1-3 July 2019
Available from: 2019-06-22 Created: 2019-06-22 Last updated: 2019-06-26Bibliographically approved
Nsolo, E., Lambrix, P. & Carlsson, N. (2019). Player Valuation in European Football. In: Ulf Brefeld, Jesse Davis, Jan Van Haaren, Albrecht Zimmermann (Ed.), Proceedings of the 5th Workshop on Machine Learning and Data Mining for Sports Analytics: co-located with 2018 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2018). Paper presented at 5th International Workshop, MLSA 2018, Co-located with ECML/PKDD 2018, Dublin, Ireland, September 10, 2018, Proceedings (pp. 42-54). Cham: Springer, 11330
Open this publication in new window or tab >>Player Valuation in European Football
2019 (English)In: Proceedings of the 5th Workshop on Machine Learning and Data Mining for Sports Analytics: co-located with 2018 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2018) / [ed] Ulf Brefeld, Jesse Davis, Jan Van Haaren, Albrecht Zimmermann, Cham: Springer, 2019, Vol. 11330, p. 42-54Conference paper, Published paper (Refereed)
Abstract [en]

As the success of a team depends on the performance of individual players, the valuation of player performance has become an important research topic. In this paper, we compare and contrast which attributes and skills best predict the success of individual players in their positions in five European top football leagues. Further, we evaluate different machine learning algorithms regarding prediction performance. Our results highlight features distinguishing top-tier players and show that prediction performance is higher for forwards than for other positions, suggesting that equally good prediction of defensive players may require more advanced metrics.

Place, publisher, year, edition, pages
Cham: Springer, 2019
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11330
Keywords
Sports analytics, football, soccer
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-153594 (URN)10.1007/978-3-030-17274-9_4 (DOI)9783030172732 (ISBN)9783030172749 (ISBN)
Conference
5th International Workshop, MLSA 2018, Co-located with ECML/PKDD 2018, Dublin, Ireland, September 10, 2018, Proceedings
Available from: 2018-12-22 Created: 2018-12-22 Last updated: 2019-06-25Bibliographically approved
Ivanova, V., Lambrix, P., Lohmann, S. & Pesquita, C. (2019). Visualization and interaction for ontologies and linked data - Editorial. Journal of Web Semantics, 55, 145-149
Open this publication in new window or tab >>Visualization and interaction for ontologies and linked data - Editorial
2019 (English)In: Journal of Web Semantics, ISSN 1570-8268, E-ISSN 1873-7749, Vol. 55, p. 145-149Article in journal, Editorial material (Other academic) Published
Place, publisher, year, edition, pages
Elsevier, 2019
National Category
Computer Sciences Human Computer Interaction
Identifiers
urn:nbn:se:liu:diva-152356 (URN)10.1016/j.websem.2018.10.001 (DOI)000462166300010 ()
Available from: 2018-10-29 Created: 2018-10-29 Last updated: 2019-04-08Bibliographically approved
Lambrix, P., Armiento, R., Delin, A. & Li, H. (2018). Big Semantic Data Processing in the Materials Design Domain. In: Sherif Sakr and Albert Zomaya (Ed.), Encyclopedia of Big Data Technologies: . Cham: Springer
Open this publication in new window or tab >>Big Semantic Data Processing in the Materials Design Domain
2018 (English)In: Encyclopedia of Big Data Technologies / [ed] Sherif Sakr and Albert Zomaya, Cham: Springer, 2018Chapter in book (Refereed)
Abstract [en]

To speed up the progress in the field of materials design, a number of challenges related to big data need to be addressed. This entry discusses these challenges and shows the semantic technologies that alleviate the problems related to variety, variability, and veracity.

Place, publisher, year, edition, pages
Cham: Springer, 2018
National Category
Computer Sciences Materials Engineering
Identifiers
urn:nbn:se:liu:diva-147715 (URN)10.1007/978-3-319-63962-8_293-1 (DOI)9783319639628 (ISBN)
Funder
Swedish e‐Science Research Center
Available from: 2018-05-07 Created: 2018-05-07 Last updated: 2018-05-07Bibliographically approved
Ljung, D., Carlsson, N. & Lambrix, P. (2018). Player Pairs Valuation in Ice Hockey. In: Ulf Brefeld, Jesse Davis, Jan Van Haaren, Albrecht Zimmermann (Ed.), Proceedings of the 5th Workshop on Machine Learning and Data Mining for Sports Analytics: co-located with 2018 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2018). Paper presented at 5th International Workshop, MLSA 2018, Co-located with ECML/PKDD 2018, Dublin, Ireland, September 10, 2018 (pp. 82-92). Cham: Springer, 11330
Open this publication in new window or tab >>Player Pairs Valuation in Ice Hockey
2018 (English)In: Proceedings of the 5th Workshop on Machine Learning and Data Mining for Sports Analytics: co-located with 2018 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2018) / [ed] Ulf Brefeld, Jesse Davis, Jan Van Haaren, Albrecht Zimmermann, Cham: Springer, 2018, Vol. 11330, p. 82-92Conference paper, Published paper (Refereed)
Abstract [en]

To overcome the shortcomings of simple metrics for evaluating player performance, recent works have introduced more advanced metrics that take into account the context of the players’ actions and perform look-ahead. However, as ice hockey is a team sport, knowing about individual ratings is not enough and coaches want to identify players that play particularly well together. In this paper we therefore extend earlier work for evaluating the performance of players to the related problem of evaluating the performance of player pairs. We experiment with data from seven NHL seasons, discuss the top pairs, and present analyses and insights based on both the absolute and relative ice time together.

Place, publisher, year, edition, pages
Cham: Springer, 2018
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11330Lecture notes in artificial intelligence ; 11330
Keywords
Sports analytics, ice hockey
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-153593 (URN)10.1007/978-3-030-17274-9_7 (DOI)9783030172732 (ISBN)9783030172749 (ISBN)
Conference
5th International Workshop, MLSA 2018, Co-located with ECML/PKDD 2018, Dublin, Ireland, September 10, 2018
Available from: 2018-12-22 Created: 2018-12-22 Last updated: 2019-06-25Bibliographically approved
Ivanova, V., Lambrix, P., Lohmann, S. & Pesquita, C. (Eds.). (2018). VOILA 2018 Visualization and Interaction for Ontologies and Linked Data: Proceedings of the Fourth International Workshop on Visualization and Interaction for Ontologies and Linked Data co-located with the 17th International Semantic Web Conference (ISWC 2018). Paper presented at Fourth International Workshop on Visualization and Interaction for Ontologies and Linked Data. Aachen, Germany: CEUR Workshop Proceedings
Open this publication in new window or tab >>VOILA 2018 Visualization and Interaction for Ontologies and Linked Data: Proceedings of the Fourth International Workshop on Visualization and Interaction for Ontologies and Linked Data co-located with the 17th International Semantic Web Conference (ISWC 2018)
2018 (English)Conference proceedings (editor) (Refereed)
Abstract [en]

A picture is worth a thousand words, we often say, yet many areas are in demand of sophisticated visualization techniques, and the Semantic Web is not an exception. The size and complexity of ontologies and Linked Data in the Semantic Web constantly grows and the diverse backgrounds of the users and application areas multiply at the same time. Providing users with visual representations and intuitive interaction techniques can significantly aid the exploration and understanding of the domains and knowledge represented by ontologies and Linked Data.

Ontology visualization is not a new topic and a number of approaches have become available in recent years, with some being already well-established, particularly in the field of ontology modeling. In other areas of ontology engineering, such as ontology alignment and debugging, although several tools have been developed, few provide a graphical user interface, not to mention navigational aids or comprehensive visualization and interaction techniques.

In the presence of a huge network of interconnected resources, one of the challenges faced by the Linked Data community is the visualization of multidimensional datasets to provide for efficient overview, exploration and querying tasks, to mention just a few. With the focus shifting from a Web of Documents to a Web of Data, changes in the interaction paradigms are in demand as well. Novel approaches also need to take into consideration the technological challenges and opportunities given by new interaction contexts, ranging from mobile, touch, and gesture interaction to visualizations on large displays, and encompassing highly responsive web applications.

There is no one-size-fits-all solution but different use cases demand different visualization and interaction techniques. Ultimately, providing better user interfaces, visual representations and interaction techniques will foster user engagement and likely lead to higher quality results in different applications employing ontologies and proliferate the consumption of Linked Data.

Special Theme & Topics of Interest

This year, we plan to have a dedicated look on empirical evidence on the benefits and limitations of visualizations and interactions in the context of the Semantic Web. We are particularly interested in success and failure stories, in learning which visualization and interaction approaches work and which do not - to which extent and in which contexts. We would like to hear about novel research findings and insights, backed with empirical data from user studies and use cases. Submissions addressing this special theme could include one or more of the following:

  • success stories
  • failure stories
  • empirical studies

We also welcome other research contributions providing empirical evidence that advances the field.

Apart from that - and as in the last years -, we are looking for submissions addressing one or more of the following topics, subjects, and contexts (or related ones):

  • Topics:
    • visualizations
    • user interfaces
    • visual analytics
    • requirements analysis
    • case studies
    • user evaluations
    • cognitive aspects
  • Subjects:
    • ontologies
    • linked data
    • ontology engineering (development, collaboration, ontology design patterns, alignment, debugging, evolution, provenance, etc.)
  • Contexts:
    • classical interaction contexts (desktop, keyboard, mouse, etc.)
    • novel interaction contexts (mobile, touch, gesture, etc.)
    • special settings (large, high-resolution, and multiple displays, etc.)
    • specific user groups and needs (people with disabilities, domain experts, etc.)
Place, publisher, year, edition, pages
Aachen, Germany: CEUR Workshop Proceedings, 2018. p. 77
Series
CEUR Workshop Proceedings, ISSN 1613-0073 ; 2187
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-150797 (URN)
Conference
Fourth International Workshop on Visualization and Interaction for Ontologies and Linked Data
Available from: 2018-08-31 Created: 2018-08-31 Last updated: 2018-08-31Bibliographically approved
Ivanova, V., Bach, B., Pietriga, E. & Lambrix, P. (2017). Alignment Cubes: Towards Interactive Visual Exploration and Evaluation of Multiple Ontology Alignments. In: Claudia d'Amato, Miriam Fernandez, Valentina A. M. Tamma, Freddy Lecue, Philippe Cudre-Mauroux, Juan F. Sequeda, Christoph Lange and Jeff Heflin (Ed.), Claudia d'Amato, Miriam Fernandez, Valentina Tamma, Freddy Lecue, Philippe Cudré-Mauroux, Juan Sequeda, Christoph Lange and Jeff Heflin (Ed.), The Semantic Web – ISWC 2017 16th International Semantic Web Conference, Vienna, Austria, October 21–25, 2017, Proceedings, Part I: . Paper presented at The Semantic Web – ISWC 2017 16th International Semantic Web Conference, Vienna, Austria, October 21–25, 2017 (pp. 400-417). Cham, Switzerland: Springer Publishing Company, 10587
Open this publication in new window or tab >>Alignment Cubes: Towards Interactive Visual Exploration and Evaluation of Multiple Ontology Alignments
2017 (English)In: The Semantic Web – ISWC 2017 16th International Semantic Web Conference, Vienna, Austria, October 21–25, 2017, Proceedings, Part I / [ed] Claudia d'Amato, Miriam Fernandez, Valentina Tamma, Freddy Lecue, Philippe Cudré-Mauroux, Juan Sequeda, Christoph Lange and Jeff Heflin, Cham, Switzerland: Springer Publishing Company, 2017, Vol. 10587, p. 400-417Conference paper, Published paper (Refereed)
Abstract [en]

Ontology alignment is an area of active research where many algorithms and approaches are being developed. Their performance is usually evaluated by comparing the produced alignments to a reference alignment in terms of precision, recall and F-measure. These measures, however, only provide an overall assessment of the quality of the alignments, but do not reveal differences and commonalities between alignments at a finer-grained level such as, e.g., regions or individual mappings. Furthermore, reference alignments are often unavailable, which makes the comparative exploration of alignments at different levels of granularity even more important. Making such comparisons efficient calls for a “human-in-the-loop” approach, best supported through interactive visual representations of alignments. Our approach extends a recent tool, Matrix Cubes, used for visualizing dense dynamic networks. We first identify use cases for ontology alignment evaluation that can benefit from interactive visualization, and then detail how our Alignment Cubes support interactive exploration of multiple ontology alignments. We demonstrate the usefulness of Alignment Cubes by describing visual exploration scenarios, showing how Alignment Cubes support common tasks identified in the use cases.

Place, publisher, year, edition, pages
Cham, Switzerland: Springer Publishing Company, 2017
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10587
Series
Information Systems and Applications, incl. Internet/Web, and HCI ; 10587
Keywords
Ontology alignment evaluation, Visual exploration, Multiple alignment comparison
National Category
Computer Sciences Human Computer Interaction
Identifiers
urn:nbn:se:liu:diva-141986 (URN)10.1007/978-3-319-68288-4_24 (DOI)9783319682877 (ISBN)9783319682884 (ISBN)9783319682884 (ISBN)
Conference
The Semantic Web – ISWC 2017 16th International Semantic Web Conference, Vienna, Austria, October 21–25, 2017
Funder
Swedish e‐Science Research CenterCUGS (National Graduate School in Computer Science)EU, FP7, Seventh Framework Programme, P7-IP-608142
Available from: 2017-10-16 Created: 2017-10-16 Last updated: 2018-11-27Bibliographically approved
Sundvall, E., Wei-Kleiner, F., Freire, S. M. & Lambrix, P. (2017). Querying archetype-based Electronic Health Records using Hadoop and Dewey encoding of openEHR models. In: Rebecca Randell, Ronald Cornet, Colin McCowan, Niels Peek, Philip J. Scott (Ed.), Rebecca Randell; Ronald Cornet; Colin McCowan; Niels Peek; Philip J. Scott (Ed.), Informatics for Health: Connected Citizen-Led Wellness and Population Health. Paper presented at Informatics for Health 2017, Manchester, UK, April 2017 (pp. 406-410). Amsterdam, The Netherlands: IOS Press
Open this publication in new window or tab >>Querying archetype-based Electronic Health Records using Hadoop and Dewey encoding of openEHR models
2017 (English)In: Informatics for Health: Connected Citizen-Led Wellness and Population Health / [ed] Rebecca Randell; Ronald Cornet; Colin McCowan; Niels Peek; Philip J. Scott, Amsterdam, The Netherlands: IOS Press, 2017, p. 406-410Conference paper, Published paper (Refereed)
Abstract [en]

Archetype-based Electronic Health Record (EHR) systems using generic reference models from e.g. openEHR, ISO 13606 or CIMI should be easy to update and reconfigure with new types (or versions) of data models or entries, ideally with very limited programming or manual database tweaking. Exploratory research (e.g. epidemiology) leading to ad-hoc querying on a population-wide scale can be a challenge in such environments. This publication describes implementation and test of an archetype-aware Dewey encoding optimization that can be used to produce such systems in environments supporting relational operations, e.g. RDBMs and distributed map-reduce frameworks like Hadoop. Initial testing was done using a nine-node 2.2 GHz quad-core Hadoop cluster querying a dataset consisting of targeted extracts from 4+ million real patient EHRs, query results with sub-minute response time were obtained.

Place, publisher, year, edition, pages
Amsterdam, The Netherlands: IOS Press, 2017
Series
Studies in Health Technology and Informatics, ISSN 0926-9630 ; 235
Keywords
medical record systems, computerzied; database management systems; Dewey encoding; Archetypes; open EHR; Hadoop; Epidemiology; XML
National Category
Computer Sciences Other Medical Engineering
Identifiers
urn:nbn:se:liu:diva-136902 (URN)10.3233/978-1-61499-753-5-406 (DOI)28423824 (PubMedID)978-1-61499-752-8 (ISBN)978-1-61499-753-5 (ISBN)
Conference
Informatics for Health 2017, Manchester, UK, April 2017
Funder
Swedish e‐Science Research Center
Available from: 2017-04-28 Created: 2017-04-28 Last updated: 2019-07-03Bibliographically approved
Freire, S. M., Teodoro, D., Wei-Kleiner, F., Sundvall, E., Karlsson, D. & Lambrix, P. (2016). Comparing the Performance of NoSQL Approaches for Managing Archetype-Based Electronic Health Record Data. PLoS ONE, 11(3), Article ID e0150069.
Open this publication in new window or tab >>Comparing the Performance of NoSQL Approaches for Managing Archetype-Based Electronic Health Record Data
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2016 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 11, no 3, article id e0150069Article in journal (Refereed) Published
Abstract [en]

This study provides an experimental performance evaluation on population-based queries of NoSQL databases storing archetype-based Electronic Health Record (EHR) data. There are few published studies regarding the performance of persistence mechanisms for systems that use multilevel modelling approaches, especially when the focus is on population-based queries. A healthcare dataset with 4.2 million records stored in a relational database (MySQL) was used to generate XML and JSON documents based on the openEHR reference model. Six datasets with different sizes were created from these documents and imported into three single machine XML databases (BaseX, eXistdb and Berkeley DB XML) and into a distributed NoSQL database system based on the MapReduce approach, Couchbase, deployed in different cluster configurations of 1, 2, 4, 8 and 12 machines. Population-based queries were submitted to those databases and to the original relational database. Database size and query response times are presented. The XML databases were considerably slower and required much more space than Couchbase. Overall, Couchbase had better response times than MySQL, especially for larger datasets. However, Couchbase requires indexing for each differently formulated query and the indexing time increases with the size of the datasets. The performances of the clusters with 2, 4, 8 and 12 nodes were not better than the single node cluster in relation to the query response time, but the indexing time was reduced proportionally to the number of nodes. The tested XML databases had acceptable performance for openEHR-based data in some querying use cases and small datasets, but were generally much slower than Couchbase. Couchbase also outperformed the response times of the relational database, but required more disk space and had a much longer indexing time. Systems like Couchbase are thus interesting research targets for scalable storage and querying of archetype-based EHR data when population-based use cases are of interest.

Place, publisher, year, edition, pages
Public Library Science, 2016
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-125961 (URN)10.1371/journal.pone.0150069 (DOI)000371992300032 ()26958859 (PubMedID)
Funder
Swedish e‐Science Research Center
Note

Funding agencies: Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES Foundation - Brazil) [4055/11]; Conselho Brasileiro de Desenvolvimento Cientifico e Tecnologico (CNPq) [150916/2013-2]

Available from: 2016-03-09 Created: 2016-03-09 Last updated: 2018-03-22
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ORCID iD: ORCID iD iconorcid.org/0000-0002-9084-0470

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