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Lambrix, Patrick, ProfessorORCID iD iconorcid.org/0000-0002-9084-0470
Publications (10 of 129) Show all publications
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.), The Semantic Web: ISWC 2017 - 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part I (pp. 400-417). Cham, Switzerland: Springer Publishing Company.
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 A. M. Tamma, Freddy Lecue, Philippe Cudre-Mauroux, Juan F. Sequeda, Christoph Lange and Jeff Heflin, Cham, Switzerland: Springer Publishing Company, 2017, 400-417 p.Chapter in book (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
Keyword
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)
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: 2018-01-13Bibliographically 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, 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: 2018-01-10
Paulheim, H., Lehmann, J., Svatek, V., Knoblock, C., Horridge, M., Lambrix, P. & Parsia, B. (Eds.). (2016). Joint Proceedings of 5th Workshop on Data Mining and Knowledge Discovery meets Linked Open Data (Know@LOD 2016) and 1st International Workshop on Completing and Debugging the Semantic Web (CoDeS 2016). Paper presented at 1st International Workshop on Completing and Debugging the Semantic Web, Heraklion, Greece, May 30th, 2016. Aachen, Germany: Rheinisch-Westfaelische Technische Hochschule Aachen University.
Open this publication in new window or tab >>Joint Proceedings of 5th Workshop on Data Mining and Knowledge Discovery meets Linked Open Data (Know@LOD 2016) and 1st International Workshop on Completing and Debugging the Semantic Web (CoDeS 2016)
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2016 (English)Conference proceedings (editor) (Refereed)
Abstract [en]

Knowledge discovery is an well-established field with a large community investigating methods for the discovery of patterns and regularities in large data sets, including relational databases and unstructured text. Research in this field has led to the development of practically relevant and scalable approaches such as association rule mining, subgroup discovery, graph mining or clustering. At the same time, the Web of Data has grown to one of the largest publicly available collections of structured, cross-domain data sets. While the growing success of Linked Data and its use in applications, e.g., in the e-Government area, has provided numerous novel opportunities, its scale and heterogeneity are posing challenges to knowledge discovery and data mining:

  • The extraction and discovery of knowledge from very large data sets;
  • The maintenance of high quality data and provenance information; 
  • The scalability of processing and mining the distributed Web of Data; and 
  • The discovery of novel links, both on the instance and the schema level. 

Contributions from the knowledge discovery field may help foster the future growth of Linked Open Data. Some recent works on statistical schema induction, mapping, and link mining have already shown that there is a fruitful intersection of both fields. With the proposed workshop, we want to investigate possible synergies between the Linked Data and Knowledge Discovery communities, and to explore novel directions for joint research. On the one hand, we wish to stimulate a discussion about how state-of-the-art algorithms for knowledge discovery and data mining can be adapted to fit the characteristics of Linked Data, such as its distributed nature, incompleteness (incl. absence of negative examples), and identify concrete use cases and applications. On the other hand, we hope to show that Linked Data can support traditional knowledge discovery tasks (e.g., as a source of additional background knowledge and of predictive features) for mining from existing, not natively linked data like, for instance, in business intelligence settings. 

The workshop addresses researchers and practitioners from the fields of knowledge discovery in databases and data mining, as well as researchers from the Semantic Web community applying such techniques to Linked Data. The goal of the workshop is to provide a platform for knowledge exchange between the different research communities, and to foster future collaborations. We expect at least 30 participants. Authors of contributed papers are especially encouraged to publish their data sets and/or the implementation of their algorithms, and to discuss these implementations and data sets with other attendees. The goal is to establish a common benchmark that can be used for competitive evaluations of algorithms and tools. 

This workshop continues a successful series of past events. It follows the first four editions of Know@LOD at ESWC 2012, 2013, 2104, and 2015, each of which were attended by 25 or more participants, respectively, as well as the Data Mining on Linked Data (DMoLD) workshop, which was held at ECML/PKDD 2013 with around 40 participants.

Besides a track for research papers and a keynote talk, the workshop will host the fourth Linked Data Mining Challenge. The proceedings of the workshop including research and challenge papers will be published with CEUR-WS.

Place, publisher, year, edition, pages
Aachen, Germany: Rheinisch-Westfaelische Technische Hochschule Aachen University, 2016
Series
CEUR Workshop Proceedings, ISSN 1613-0073 ; 1586
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-127723 (URN)
Conference
1st International Workshop on Completing and Debugging the Semantic Web, Heraklion, Greece, May 30th, 2016
Available from: 2016-05-11 Created: 2016-05-11 Last updated: 2018-01-10Bibliographically approved
Färnqvist, T., Heintz, F., Lambrix, P., Mannila, L. & Wang, C. (2016). Supporting Active Learning by Introducing an Interactive Teaching Tool in a Data Structures and Algorithms. In: Proceedings of the 47th ACM Technical Symposium on Computer Science Education (SIGCSE 2016): . Paper presented at 47th ACM Technical Symposium on Computer Science Education (SIGCSE 2016), Memphis, Tennessee, USA, March 2-5, 2016 (pp. 663-668). ACM Publications.
Open this publication in new window or tab >>Supporting Active Learning by Introducing an Interactive Teaching Tool in a Data Structures and Algorithms
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2016 (English)In: Proceedings of the 47th ACM Technical Symposium on Computer Science Education (SIGCSE 2016), ACM Publications, 2016, 663-668 p.Conference paper, Published paper (Refereed)
Abstract [en]

Traditionally, theoretical foundations in data structures and algorithms (DSA) courses have been covered through lectures followed by tutorials, where students practise their understanding on pen-and-paper tasks. In this paper, we present findings from a pilot study on using the interactive e-book OpenDSA as the main material in a DSA course. The goal was to redesign an already existing course by building on active learning and continuous examination through the use of OpenDSA. In addition to presenting the study setting, we describe findings from four data sources: final exam, OpenDSA log data, pre and post questionnaires as well as an observation study. The results indicate that students performed better on the exam than during previous years. Students preferred OpenDSA over traditional textbooks and worked actively with the material, although a large proportion of them put off the work until the due date approaches.

Place, publisher, year, edition, pages
ACM Publications, 2016
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:liu:diva-122401 (URN)10.1145/2839509.2844653 (DOI)978-1-4503-3685-7 (ISBN)
Conference
47th ACM Technical Symposium on Computer Science Education (SIGCSE 2016), Memphis, Tennessee, USA, March 2-5, 2016
Available from: 2015-11-01 Created: 2015-11-01 Last updated: 2018-01-10
Lambrix, P., Wei-Kleiner, F. & Dragisic, Z. (2015). Completing the is-a structure in light-weight ontologies. Journal of Biomedical Semantics, 6, Article ID 12.
Open this publication in new window or tab >>Completing the is-a structure in light-weight ontologies
2015 (English)In: Journal of Biomedical Semantics, ISSN 2041-1480, E-ISSN 2041-1480, Vol. 6, 12Article in journal (Refereed) Published
Abstract [en]

 Background: With the increasing presence of biomedical data sources on the Internet more and more research effort is put into finding possible ways for integrating and searching such often heterogeneous sources. Ontologies are a key technology in this effort. However, developing ontologies is not an easy task and often the resulting ontologies are not complete. In addition to being problematic for the correct modelling of a domain, such incomplete ontologies, when used in semantically-enabled applications, can lead to valid conclusions being missed.

Results: We consider the problem of repairing missing is-a relations in ontologies. We formalize the problem as a generalized TBox abduction problem. Based on this abduction framework, we present complexity results for the existence, relevance and necessity decision problems for the generalized TBox abduction problem with and without some specific preference relations for ontologies that can be represented using a member of the EL family of description logics. Further, we present algorithms for finding solutions, a system as well as experiments.

Conclusions: Semantically-enabled applications need high quality ontologies and one key aspect is their completeness. We have introduced a framework and system that provides an environment for supporting domain experts to complete the is-a structure of ontologies. We have shown the usefulness of the approach in different experiments. For the two Anatomy ontologies from the Ontology Alignment Evaluation Initiative, we repaired 94 and 58 initial given missing is-a relations, respectively, and detected and repaired additionally, 47 and 10 missing is-a relations. In an experiment with BioTop without given missing is-a relations, we detected and repaired 40 new missing is-a relations.

National Category
Computer Sciences Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:liu:diva-116628 (URN)10.1186/s13326-015-0002-8 (DOI)000353197200001 ()25883780 (PubMedID)
Funder
Swedish e‐Science Research CenterCUGS (National Graduate School in Computer Science)
Available from: 2015-03-29 Created: 2015-03-29 Last updated: 2018-01-11
Dragisic, Z., Lambrix, P. & Blomqvist, E. (2015). Integrating Ontology Debugging and Matching into the eXtreme Design Methodology. In: Eva Blomqvist; Pascal Hitzler; Adila Krisnadhi; Tom Narock; Monika Solanki (Ed.), Proceedings of the 6th Workshop on Ontology and Semantic Web Patterns (WOP 2015): . Paper presented at 6th Workshop on Ontology and Semantic Web Patterns (WOP 2015), Bethlehem, Pensylvania, USA, October 11, 2015. Rheinisch-Westfaelische Technische Hochschule Aachen University.
Open this publication in new window or tab >>Integrating Ontology Debugging and Matching into the eXtreme Design Methodology
2015 (English)In: Proceedings of the 6th Workshop on Ontology and Semantic Web Patterns (WOP 2015) / [ed] Eva Blomqvist; Pascal Hitzler; Adila Krisnadhi; Tom Narock; Monika Solanki, Rheinisch-Westfaelische Technische Hochschule Aachen University , 2015Conference paper, Published paper (Refereed)
Abstract [en]

Ontology design patterns (ODPs) and related ontology development methodologies were designed as ways of sharing and reusing best practices in ontology engineering. However, while the use of these reduces the number of issues in the resulting ontologies defects can still be introduced into the ontology due to improper use or misinterpretation of the patterns. Thus, the quality of the developed ontologies is still a major concern. In this paper we address this issue by describing how ontology debugging and matching can be integrated in a state-of-the-art ontology development methodology based on ontology design patterns- the eXtreme Design methodology, and show the advantages in a case study based on a real world ontology.

Place, publisher, year, edition, pages
Rheinisch-Westfaelische Technische Hochschule Aachen University, 2015
Series
CEUR Workshop Proceedings, ISSN 1613-0073 ; 1461
National Category
Media and Communication Technology
Identifiers
urn:nbn:se:liu:diva-121794 (URN)
Conference
6th Workshop on Ontology and Semantic Web Patterns (WOP 2015), Bethlehem, Pensylvania, USA, October 11, 2015
Funder
EU, FP7, Seventh Framework Programme, FP7-IP-608142CUGS (National Graduate School in Computer Science)Swedish e‐Science Research Center
Available from: 2015-10-06 Created: 2015-10-06 Last updated: 2018-01-11Bibliographically approved
Lambrix, P., Hyvönen, E., Blomqvist, E., Presutti, V., Qi, G., Sattler, U., . . . Ghidini, C. (Eds.). (2015). Knowledge Engineering and Knowledge Management: EKAW 2014 Satellite Events, VISUAL, EKM1, and ARCOE-Logic, Linköping, Sweden, November 24-28, 2014. Revised Selected Papers. Paper presented at EKAW 2014 Satellite Events, VISUAL, EKM1, and ARCOE-Logic, Linköping, Sweden, November 24–28, 2014. Switzerland: Springer.
Open this publication in new window or tab >>Knowledge Engineering and Knowledge Management: EKAW 2014 Satellite Events, VISUAL, EKM1, and ARCOE-Logic, Linköping, Sweden, November 24-28, 2014. Revised Selected Papers
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2015 (English)Conference proceedings (editor) (Refereed)
Abstract [en]

This volume contains the Satellite Events proceedings of the 19th International Conference on Knowledge Engineering and Knowledge Management (EKAW 2014), held in Linköping, Sweden, during November 24–28, 2014. This was the first EKAW conference in a Nordic country. It was concerned with all aspects of eliciting, acquiring, modeling, and managing knowledge, the construction of knowledge-intensive systems and services for the Semantic Web, knowledge management, e-business, natural language processing, intelligent information integration, personal digital assistance systems, and a variety of other related topics. The special focus of EKAW2014 was Diversity.

Place, publisher, year, edition, pages
Switzerland: Springer, 2015. 234 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 8982
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-117408 (URN)10.1007/978-3-319-17966-7 (DOI)978-3-319-17965-0 (ISBN)978-3-319-17966-7 (ISBN)
Conference
EKAW 2014 Satellite Events, VISUAL, EKM1, and ARCOE-Logic, Linköping, Sweden, November 24–28, 2014
Available from: 2015-04-24 Created: 2015-04-24 Last updated: 2018-01-11Bibliographically approved
Chiatti, A., Dragisic, Z., Cerquitelli, T. & Lambrix, P. (2015). Reducing the search space in ontology alignment using clustering techniques and topic identification. In: Proceedings of the 8th International Conference on Knowledge Capture: . Paper presented at 8th International Conference on Knowledge Capture (pp. 21). New York: ACM Digital Library.
Open this publication in new window or tab >>Reducing the search space in ontology alignment using clustering techniques and topic identification
2015 (English)In: Proceedings of the 8th International Conference on Knowledge Capture, New York: ACM Digital Library, 2015, 21- p.Conference paper, Published paper (Refereed)
Abstract [en]

One of the current challenges in ontology alignment is scalability and one technique to deal with this issue is to reduce the search space for the generation of mapping suggestions. In this paper we develop a method to prune that search space by using clustering techniques and topic identification. Further, we provide experiments showing that we are able to generate partitions that allow for high quality alignments with a highly reduced effort for computation and validation of mapping suggestions for the parts of the ontologies in the partition. Other techniques will still be needed for finding mappings that are not in the partition.

Place, publisher, year, edition, pages
New York: ACM Digital Library, 2015
Keyword
Knowledge representation, data mining, ontology alignment
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-121838 (URN)10.1145/2815833.2816959 (DOI)978-1-4503-3849-3 (ISBN)
Conference
8th International Conference on Knowledge Capture
Funder
CUGS (National Graduate School in Computer Science)Swedish e‐Science Research CenterEU, FP7, Seventh Framework Programme, FP7-IP-608142
Available from: 2015-10-09 Created: 2015-10-09 Last updated: 2018-01-11
Ivanova, V., Lambrix, P. & Åberg, J. (2015). Requirements for and Evaluation of User Support for Large-Scale Ontology Alignment. In: The Semantic Web. Latest Advances and New Domains: 12th European Semantic Web Conference, ESWC 2015, Portoroz, Slovenia, May 31 -- June 4, 2015. Proceedings (pp. 3-20). Springer Science+Business Media B.V..
Open this publication in new window or tab >>Requirements for and Evaluation of User Support for Large-Scale Ontology Alignment
2015 (English)In: The Semantic Web. Latest Advances and New Domains: 12th European Semantic Web Conference, ESWC 2015, Portoroz, Slovenia, May 31 -- June 4, 2015. Proceedings, Springer Science+Business Media B.V., 2015, 3-20 p.Chapter in book (Refereed)
Abstract [en]

Currently one of the challenges for the ontology alignment community is the user involvement in the alignment process. At the same time, the focus of the community has shifted towards large-scale matching which introduces an additional dimension to this issue. This paper aims to provide a set of requirements that foster the user involvement for large-scale ontology alignment tasks.Further, we present and discuss the results of a literature study for 7 ontology alignments systems as well as a heuristic evaluation and an observational user study for 3 ontology alignment systems to reveal the coverage of the requirements in the systems and the support for the requirements in the user interfaces.

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2015
Series
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 9088
Keyword
knowledge representation, ontologies, ontology alignment, user interfaces
National Category
Computer Sciences Human Computer Interaction
Identifiers
urn:nbn:se:liu:diva-118273 (URN)10.1007/978-3-319-18818-8_1 (DOI)000362439200001 ()978-3-319-18817-1 (ISBN)978-3-319-18818-8 (ISBN)
Funder
CUGS (National Graduate School in Computer Science)Swedish e‐Science Research Center
Available from: 2015-05-25 Created: 2015-05-25 Last updated: 2018-01-11Bibliographically approved
Cheatham, M., Dragisic, Z., Euzenat, J., Faria, D., Ferrara, A., Flouris, G., . . . Zamazal, O. (2015). Results of the Ontology Alignment Evaluation Initiative 2015. In: Ontology Matching: . Paper presented at Ontology Matching (pp. 60-115). .
Open this publication in new window or tab >>Results of the Ontology Alignment Evaluation Initiative 2015
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2015 (English)In: Ontology Matching, 2015, 60-115 p.Conference paper, Published paper (Refereed)
Series
CEUR Workshop Proceedings, ISSN 1613-0073 ; 1545
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-123885 (URN)
Conference
Ontology Matching
Available from: 2016-01-12 Created: 2016-01-12 Last updated: 2018-01-10
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-9084-0470

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