---
_id: '63434'
author:
- first_name: Max
full_name: Hoffmann, Max
last_name: Hoffmann
citation:
ama: 'Hoffmann M. Using scriptwriting as a response format for interface tasks:
Exemplary analyses in the context of symmetry. In: Bosch M, Bolondi G, Carreira
S, Michael G, Camilla S, eds. Proceedings of the Fourteenth Congress of the
European Society for Research in Mathematics Education (CERME14). ; 2025.'
apa: 'Hoffmann, M. (2025). Using scriptwriting as a response format for interface
tasks: Exemplary analyses in the context of symmetry. In M. Bosch, G. Bolondi,
S. Carreira, G. Michael, & S. Camilla (Eds.), Proceedings of the Fourteenth
Congress of the European Society for Research in Mathematics Education (CERME14).'
bibtex: '@inproceedings{Hoffmann_2025, title={Using scriptwriting as a response
format for interface tasks: Exemplary analyses in the context of symmetry}, booktitle={Proceedings
of the Fourteenth Congress of the European Society for Research in Mathematics
Education (CERME14)}, author={Hoffmann, Max}, editor={Bosch, Marianna and Bolondi,
Giorgio and Carreira, Susana and Michael, Gaidoschik and Camilla, Spagnolo}, year={2025}
}'
chicago: 'Hoffmann, Max. “Using Scriptwriting as a Response Format for Interface
Tasks: Exemplary Analyses in the Context of Symmetry.” In Proceedings of the
Fourteenth Congress of the European Society for Research in Mathematics Education
(CERME14), edited by Marianna Bosch, Giorgio Bolondi, Susana Carreira, Gaidoschik
Michael, and Spagnolo Camilla, 2025.'
ieee: 'M. Hoffmann, “Using scriptwriting as a response format for interface tasks:
Exemplary analyses in the context of symmetry,” in Proceedings of the Fourteenth
Congress of the European Society for Research in Mathematics Education (CERME14),
2025.'
mla: 'Hoffmann, Max. “Using Scriptwriting as a Response Format for Interface Tasks:
Exemplary Analyses in the Context of Symmetry.” Proceedings of the Fourteenth
Congress of the European Society for Research in Mathematics Education (CERME14),
edited by Marianna Bosch et al., 2025.'
short: 'M. Hoffmann, in: M. Bosch, G. Bolondi, S. Carreira, G. Michael, S. Camilla
(Eds.), Proceedings of the Fourteenth Congress of the European Society for Research
in Mathematics Education (CERME14), 2025.'
date_created: 2026-01-04T21:26:04Z
date_updated: 2026-01-04T21:29:36Z
department:
- _id: '97'
- _id: '643'
editor:
- first_name: Marianna
full_name: Bosch, Marianna
last_name: Bosch
- first_name: Giorgio
full_name: Bolondi, Giorgio
last_name: Bolondi
- first_name: Susana
full_name: Carreira, Susana
last_name: Carreira
- first_name: Gaidoschik
full_name: Michael, Gaidoschik
last_name: Michael
- first_name: Spagnolo
full_name: Camilla, Spagnolo
last_name: Camilla
keyword:
- hoffmann
- reviewed
- proceedings
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://hal.science/CERME14/hal-05237875v1
oa: '1'
publication: Proceedings of the Fourteenth Congress of the European Society for Research
in Mathematics Education (CERME14)
quality_controlled: '1'
status: public
title: 'Using scriptwriting as a response format for interface tasks: Exemplary analyses
in the context of symmetry'
type: conference
user_id: '32202'
year: '2025'
...
---
_id: '55834'
abstract:
- lang: eng
text: Performance of music in the home was the means by which most works were received
before the advent of audio recordings and broadcasts, yet the notation sources
that form our primary record of this culture have not been the subject of comprehensive
or methodical study. Choices made by arrangers adapting music for domestic consumption
– of instrumentation, abbreviation, or simplification – reflect the musical life
of the 19th century, and can inform our understanding alongside contemporary accounts
such as newspapers, adverts, and diaries. This position paper gives the background,
motivation, and proposed approach of research currently being undertaken within
the Beethoven in the House project. This will include a study of Steiner editions
of Beethoven’s 7th and 8th Symphonies and Wellingtons Sieg, making a detailed
comparison between arrangements, systematically identifying a core common to multiple
versions, and asking if this reflects the stated values of the publisher. A second
survey will look for patterns across a larger sample of lesser-known and poorly
catalogued scores, collating emergent indicators of arrangers’ motivations within
a narrative of the domestic market – the music industry of its day. Both studies
will innovate digital methods which characterise arrangements as music encodings,
including ‘sparse’ approaches to notation and annotation.
author:
- first_name: Kevin R.
full_name: Page, Kevin R.
last_name: Page
- first_name: Johannes
full_name: Kepper, Johannes
id: '1684'
last_name: Kepper
orcid: 0000-0003-4891-260X
- first_name: Christine
full_name: Siegert, Christine
last_name: Siegert
- first_name: Andrew
full_name: Hankinson, Andrew
last_name: Hankinson
- first_name: David
full_name: Lewis, David
last_name: Lewis
citation:
ama: 'Page KR, Kepper J, Siegert C, Hankinson A, Lewis D. Beethoven in the House:
Digital Studies of Domestic Music Arrangements. In: Münnich S, Rizo D, eds. Music
Encoding Conference Proceedings 2021. Humanities Commons; 2022:117–123. doi:10.17613/389b-xx73'
apa: 'Page, K. R., Kepper, J., Siegert, C., Hankinson, A., & Lewis, D. (2022).
Beethoven in the House: Digital Studies of Domestic Music Arrangements. In S.
Münnich & D. Rizo (Eds.), Music Encoding Conference Proceedings 2021
(pp. 117–123). Humanities Commons. https://doi.org/10.17613/389b-xx73'
bibtex: '@inproceedings{Page_Kepper_Siegert_Hankinson_Lewis_2022, title={Beethoven
in the House: Digital Studies of Domestic Music Arrangements}, DOI={10.17613/389b-xx73},
booktitle={Music Encoding Conference Proceedings 2021}, publisher={Humanities
Commons}, author={Page, Kevin R. and Kepper, Johannes and Siegert, Christine and
Hankinson, Andrew and Lewis, David}, editor={Münnich, Stefan and Rizo, David},
year={2022}, pages={117–123} }'
chicago: 'Page, Kevin R., Johannes Kepper, Christine Siegert, Andrew Hankinson,
and David Lewis. “Beethoven in the House: Digital Studies of Domestic Music Arrangements.”
In Music Encoding Conference Proceedings 2021, edited by Stefan Münnich
and David Rizo, 117–123. Humanities Commons, 2022. https://doi.org/10.17613/389b-xx73.'
ieee: 'K. R. Page, J. Kepper, C. Siegert, A. Hankinson, and D. Lewis, “Beethoven
in the House: Digital Studies of Domestic Music Arrangements,” in Music Encoding
Conference Proceedings 2021, 2022, pp. 117–123, doi: 10.17613/389b-xx73.'
mla: 'Page, Kevin R., et al. “Beethoven in the House: Digital Studies of Domestic
Music Arrangements.” Music Encoding Conference Proceedings 2021, edited
by Stefan Münnich and David Rizo, Humanities Commons, 2022, pp. 117–123, doi:10.17613/389b-xx73.'
short: 'K.R. Page, J. Kepper, C. Siegert, A. Hankinson, D. Lewis, in: S. Münnich,
D. Rizo (Eds.), Music Encoding Conference Proceedings 2021, Humanities Commons,
2022, pp. 117–123.'
date_created: 2024-08-28T11:42:27Z
date_updated: 2024-08-28T14:10:51Z
department:
- _id: '874'
doi: 10.17613/389b-xx73
editor:
- first_name: Stefan
full_name: Münnich, Stefan
last_name: Münnich
- first_name: David
full_name: Rizo, David
last_name: Rizo
keyword:
- BitH
- mec-proceedings
- mec-proceedings-2021
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://hcommons.org/deposits/item/hc:45995/
oa: '1'
page: 117–123
publication: Music Encoding Conference Proceedings 2021
publication_identifier:
isbn:
- 978-84-13-02173-7
publication_status: published
publisher: Humanities Commons
status: public
title: 'Beethoven in the House: Digital Studies of Domestic Music Arrangements'
type: conference
user_id: '1684'
year: '2022'
...
---
_id: '55841'
abstract:
- lang: eng
text: For musicologists, the collation of multiple sources of the same work is a
frequent task. By comparing different witnesses, they seek to identify variation,
describe dependencies, and ultimately understand the genesis and transmission
of (musical) works. Obviously, the need for such comparison is independent from
the medium in which a musical work is manifested. In computing, comparing files
for difference is a common task, and the well-known Unix utility diff is almost
46 years old. However, diff, like many other such tools, operates on plain text.
While many music encoding formats based on plain text exist, formats used in the
field of Digital Humanities are typically based on XML. There are dedicated algorithms
for comparing XML as well, but they only focus on the syntax of XML, but not the
semantic structures modelled into such standards as MEI. MEI seeks to describe
musical structures, and the XML syntax is just a means to express those structures.
A diff tool for music should focus on comparing musical structures, but not the
specifics of their serialization into a file format. In {Beethovens Werkstatt},
a 16-year project focussed on exploring the concepts and requirements of digital
genetic editions of music, based on and arguing with examples from Ludwig van
Beethoven, a case-bound diff tool for music was developed. The following paper
discusses how that specific tool can be generalized, and which use cases such
a tool may support.
author:
- first_name: Kristin
full_name: Herold, Kristin
last_name: Herold
- first_name: Johannes
full_name: Kepper, Johannes
id: '1684'
last_name: Kepper
orcid: 0000-0003-4891-260X
- first_name: Ran
full_name: Mo, Ran
last_name: Mo
- first_name: Agnes Regina
full_name: Seipelt, Agnes Regina
id: '24239'
last_name: Seipelt
citation:
ama: 'Herold K, Kepper J, Mo R, Seipelt AR. MusicDiff – A Diff Tool for MEI. In:
De Luca E, Flanders J, eds. Music Encoding Conference Proceedings 2020.
Humanities Commons; 2020:59–66. doi:10.17613/ydbv-e158'
apa: Herold, K., Kepper, J., Mo, R., & Seipelt, A. R. (2020). MusicDiff – A
Diff Tool for MEI. In E. De Luca & J. Flanders (Eds.), Music Encoding Conference
Proceedings 2020 (pp. 59–66). Humanities Commons. https://doi.org/10.17613/ydbv-e158
bibtex: '@inproceedings{Herold_Kepper_Mo_Seipelt_2020, title={MusicDiff – A Diff
Tool for MEI}, DOI={10.17613/ydbv-e158},
booktitle={Music Encoding Conference Proceedings 2020}, publisher={Humanities
Commons}, author={Herold, Kristin and Kepper, Johannes and Mo, Ran and Seipelt,
Agnes Regina}, editor={De Luca, Elsa and Flanders, Julia}, year={2020}, pages={59–66}
}'
chicago: Herold, Kristin, Johannes Kepper, Ran Mo, and Agnes Regina Seipelt. “MusicDiff
– A Diff Tool for MEI.” In Music Encoding Conference Proceedings 2020,
edited by Elsa De Luca and Julia Flanders, 59–66. Humanities Commons, 2020. https://doi.org/10.17613/ydbv-e158.
ieee: 'K. Herold, J. Kepper, R. Mo, and A. R. Seipelt, “MusicDiff – A Diff Tool
for MEI,” in Music Encoding Conference Proceedings 2020, 2020, pp. 59–66,
doi: 10.17613/ydbv-e158.'
mla: Herold, Kristin, et al. “MusicDiff – A Diff Tool for MEI.” Music Encoding
Conference Proceedings 2020, edited by Elsa De Luca and Julia Flanders, Humanities
Commons, 2020, pp. 59–66, doi:10.17613/ydbv-e158.
short: 'K. Herold, J. Kepper, R. Mo, A.R. Seipelt, in: E. De Luca, J. Flanders (Eds.),
Music Encoding Conference Proceedings 2020, Humanities Commons, 2020, pp. 59–66.'
date_created: 2024-08-28T11:45:23Z
date_updated: 2024-08-28T14:04:19Z
department:
- _id: '874'
- _id: '538'
doi: 10.17613/ydbv-e158
editor:
- first_name: Elsa
full_name: De Luca, Elsa
last_name: De Luca
- first_name: Julia
full_name: Flanders, Julia
last_name: Flanders
keyword:
- mec-proceedings
- mec-proceedings-2020
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://hcommons.org/deposits/item/hc:31963/
oa: '1'
page: 59–66
project:
- _id: '743'
name: Beethovens Werkstatt - Genetische Textkritik und Digitale Musikedition
publication: Music Encoding Conference Proceedings 2020
publication_status: published
publisher: Humanities Commons
status: public
title: MusicDiff – A Diff Tool for MEI
type: conference
user_id: '1684'
year: '2020'
...
---
_id: '29009'
abstract:
- lang: eng
text: With the growth in number and variety of RDF datasets comes an in- creasing
need for both scalable and accurate solutions to support link discovery at instance
level within and across these datasets. In contrast to ontology matching, most
linking frameworks rely solely on string similarities to this end. The limited
use of semantic similarities when linking instances is partly due to the current
literature stating that they (1) do not improve the F-measure of instance linking
approaches and (2) are impractical to use because they lack time efficiency. We
revisit the combination of string and semantic similarities for linking instances.
Contrary to the literature, our results suggest that this combination can improve
the F-measure achieved by instance linking systems when the combination of the
measures is performed by a machine learning approach. To achieve this in- sight,
we had to address the scalability of semantic similarities. We hence present a
framework for the rapid computation of semantic similarities based on edge counting.
This runtime improvement allowed us to run an evaluation of 5 bench- mark datasets.
Our results suggest that combining string and semantic similarities can improve
the F-measure by up to 6% absolute.
author:
- first_name: Kleanthi
full_name: Georgala, Kleanthi
last_name: Georgala
- first_name: Michael
full_name: Röder, Michael
last_name: Röder
- first_name: Mohamed
full_name: Sherif, Mohamed
id: '67234'
last_name: Sherif
orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
citation:
ama: 'Georgala K, Röder M, Sherif M, Ngonga Ngomo A-C. Applying edge-counting semantic
similarities to Link Discovery: Scalability and Accuracy. In: Proceedings of
Ontology Matching Workshop 2020. ; 2020.'
apa: 'Georgala, K., Röder, M., Sherif, M., & Ngonga Ngomo, A.-C. (2020). Applying
edge-counting semantic similarities to Link Discovery: Scalability and Accuracy.
Proceedings of Ontology Matching Workshop 2020.'
bibtex: '@inproceedings{Georgala_Röder_Sherif_Ngonga Ngomo_2020, title={Applying
edge-counting semantic similarities to Link Discovery: Scalability and Accuracy},
booktitle={Proceedings of Ontology Matching Workshop 2020}, author={Georgala,
Kleanthi and Röder, Michael and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille},
year={2020} }'
chicago: 'Georgala, Kleanthi, Michael Röder, Mohamed Sherif, and Axel-Cyrille Ngonga
Ngomo. “Applying Edge-Counting Semantic Similarities to Link Discovery: Scalability
and Accuracy.” In Proceedings of Ontology Matching Workshop 2020, 2020.'
ieee: 'K. Georgala, M. Röder, M. Sherif, and A.-C. Ngonga Ngomo, “Applying edge-counting
semantic similarities to Link Discovery: Scalability and Accuracy,” 2020.'
mla: 'Georgala, Kleanthi, et al. “Applying Edge-Counting Semantic Similarities to
Link Discovery: Scalability and Accuracy.” Proceedings of Ontology Matching
Workshop 2020, 2020.'
short: 'K. Georgala, M. Röder, M. Sherif, A.-C. Ngonga Ngomo, in: Proceedings of
Ontology Matching Workshop 2020, 2020.'
date_created: 2021-12-17T09:53:49Z
date_updated: 2023-08-16T09:34:31Z
keyword:
- 2020 dice simba sherif hecate ngonga knowgraphs sys:relevantFor:limboproject limboproject
sys:relevantFor:infai sys:relevantFor:bis limes limbo opal roeder georgala
language:
- iso: eng
publication: Proceedings of Ontology Matching Workshop 2020
status: public
title: 'Applying edge-counting semantic similarities to Link Discovery: Scalability
and Accuracy'
type: conference
user_id: '67234'
year: '2020'
...
---
_id: '29010'
abstract:
- lang: eng
text: Link discovery plays a key role in the integration and use of data across
RDF knowledge graphs. Active learning approaches are a common family of solutions
to address the problem of learning how to compute links from users. So far, only
active learning from perfect oracles has been considered in the literature. However,
real oracles are often far from perfect (e.g., in crowdsourcing). We hence study
the problem of learning how to compute links across knowledge graphs from noisy
oracles, i.e., oracles that are not guaranteed to return correct classification
results. We present a novel approach for link discovery based on a probabilistic
model, with which we estimate the joint odds of the oracles’ guesses. We combine
this approach with an iterative learning approach based on refinements. The resulting
method, Ligon, is evaluated on 10 benchmark datasets. Our results suggest that
Ligon configured with 10 iterations and 10 training examples per iteration achieves
more than 95% of the F-measure achieved by state-of-the-art algorithms trained
with a perfect oracle. Moreover, Ligon outperforms batch learning approaches devised
to be trained with small amounts of training data by more than 40% F-measure on
average.
author:
- first_name: Mohamed
full_name: Sherif, Mohamed
id: '67234'
last_name: Sherif
orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Kevin
full_name: Dreßler}, Kevin
last_name: Dreßler}
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
citation:
ama: 'Sherif M, Dreßler} K, Ngonga Ngomo A-C. LIGON – Link Discovery with Noisy
Oracles. In: Proceedings of Ontology Matching Workshop 2020. ; 2020.'
apa: Sherif, M., Dreßler}, K., & Ngonga Ngomo, A.-C. (2020). LIGON – Link Discovery
with Noisy Oracles. Proceedings of Ontology Matching Workshop 2020.
bibtex: '@inproceedings{Sherif_Dreßler}_Ngonga Ngomo_2020, title={LIGON – Link Discovery
with Noisy Oracles}, booktitle={Proceedings of Ontology Matching Workshop 2020},
author={Sherif, Mohamed and Dreßler}, Kevin and Ngonga Ngomo, Axel-Cyrille}, year={2020}
}'
chicago: Sherif, Mohamed, Kevin Dreßler}, and Axel-Cyrille Ngonga Ngomo. “LIGON
– Link Discovery with Noisy Oracles.” In Proceedings of Ontology Matching Workshop
2020, 2020.
ieee: M. Sherif, K. Dreßler}, and A.-C. Ngonga Ngomo, “LIGON – Link Discovery with
Noisy Oracles,” 2020.
mla: Sherif, Mohamed, et al. “LIGON – Link Discovery with Noisy Oracles.” Proceedings
of Ontology Matching Workshop 2020, 2020.
short: 'M. Sherif, K. Dreßler}, A.-C. Ngonga Ngomo, in: Proceedings of Ontology
Matching Workshop 2020, 2020.'
date_created: 2021-12-17T09:54:05Z
date_updated: 2023-08-16T09:34:11Z
keyword:
- 2020 dice simba sherif ligon ngonga knowgraphs sys:relevantFor:limboproject limboproject
sys:relevantFor:infai sys:relevantFor:bis limes limbo opal kevin
language:
- iso: eng
publication: Proceedings of Ontology Matching Workshop 2020
status: public
title: LIGON – Link Discovery with Noisy Oracles
type: conference
user_id: '67234'
year: '2020'
...
---
_id: '29040'
author:
- first_name: Hamada Mohamed Abdelsamee
full_name: Zahera, Hamada Mohamed Abdelsamee
id: '72768'
last_name: Zahera
orcid: 0000-0003-0215-1278
- first_name: Mohamed
full_name: Sherif, Mohamed
id: '67234'
last_name: Sherif
orcid: https://orcid.org/0000-0002-9927-2203
citation:
ama: 'Zahera HMA, Sherif M. ProBERT: Product Data Classification with Fine-tuning
BERT Model. In: Proceedings of Mining the Web of HTML-Embedded Product Data
Workshop (MWPD2020). ; 2020.'
apa: 'Zahera, H. M. A., & Sherif, M. (2020). ProBERT: Product Data Classification
with Fine-tuning BERT Model. Proceedings of Mining the Web of HTML-Embedded
Product Data Workshop (MWPD2020).'
bibtex: '@inproceedings{Zahera_Sherif_2020, title={ProBERT: Product Data Classification
with Fine-tuning BERT Model}, booktitle={Proceedings of Mining the Web of HTML-embedded
Product Data Workshop (MWPD2020)}, author={Zahera, Hamada Mohamed Abdelsamee and
Sherif, Mohamed}, year={2020} }'
chicago: 'Zahera, Hamada Mohamed Abdelsamee, and Mohamed Sherif. “ProBERT: Product
Data Classification with Fine-Tuning BERT Model.” In Proceedings of Mining
the Web of HTML-Embedded Product Data Workshop (MWPD2020), 2020.'
ieee: 'H. M. A. Zahera and M. Sherif, “ProBERT: Product Data Classification with
Fine-tuning BERT Model,” 2020.'
mla: 'Zahera, Hamada Mohamed Abdelsamee, and Mohamed Sherif. “ProBERT: Product Data
Classification with Fine-Tuning BERT Model.” Proceedings of Mining the Web
of HTML-Embedded Product Data Workshop (MWPD2020), 2020.'
short: 'H.M.A. Zahera, M. Sherif, in: Proceedings of Mining the Web of HTML-Embedded
Product Data Workshop (MWPD2020), 2020.'
date_created: 2021-12-17T10:05:42Z
date_updated: 2023-08-16T10:06:10Z
keyword:
- 2020 dice zahera sherif knowgraphs sys:relevantFor:limboproject limboproject sys:relevantFor:infai
sys:relevantFor:bis limes limbo opal
language:
- iso: eng
publication: Proceedings of Mining the Web of HTML-embedded Product Data Workshop
(MWPD2020)
status: public
title: 'ProBERT: Product Data Classification with Fine-tuning BERT Model'
type: conference
user_id: '67234'
year: '2020'
...
---
_id: '29007'
abstract:
- lang: eng
text: Modern data-driven frameworks often have to process large amounts of data
periodically. Hence, they often operate under time or space constraints. This
also holds for Linked Data-driven frameworks when processing RDF data, in particular,
when they perform link discovery tasks. In this work, we present a novel approach
for link discovery under constraints pertaining to the expected recall of a link
discovery task. Given a link specification, the approach aims to find a subsumed
link specification that achieves a lower run time than the input specification
while abiding by a predefined constraint on the expected recall it has to achieve.
Our approach, dubbed LIGER, combines downward refinement oper- ators with monotonicity
assumptions to detect such specifications. We evaluate our approach on seven datasets.
Our results suggest that the different implemen- tations of LIGER can detect subsumed
specifications that abide by expected recall constraints efficiently, thus leading
to significantly shorter overall run times than our baseline.
author:
- first_name: Kleanthi
full_name: Georgala, Kleanthi
last_name: Georgala
- first_name: Mohamed
full_name: Sherif, Mohamed
id: '67234'
last_name: Sherif
orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
citation:
ama: 'Georgala K, Sherif M, Ngonga Ngomo A-C. LIGER – Link Discovery with Partial
Recall. In: Proceedings of Ontology Matching Workshop 2020. ; 2020.'
apa: Georgala, K., Sherif, M., & Ngonga Ngomo, A.-C. (2020). LIGER – Link Discovery
with Partial Recall. Proceedings of Ontology Matching Workshop 2020.
bibtex: '@inproceedings{Georgala_Sherif_Ngonga Ngomo_2020, title={LIGER – Link Discovery
with Partial Recall}, booktitle={Proceedings of Ontology Matching Workshop 2020},
author={Georgala, Kleanthi and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille},
year={2020} }'
chicago: Georgala, Kleanthi, Mohamed Sherif, and Axel-Cyrille Ngonga Ngomo. “LIGER
– Link Discovery with Partial Recall.” In Proceedings of Ontology Matching
Workshop 2020, 2020.
ieee: K. Georgala, M. Sherif, and A.-C. Ngonga Ngomo, “LIGER – Link Discovery with
Partial Recall,” 2020.
mla: Georgala, Kleanthi, et al. “LIGER – Link Discovery with Partial Recall.” Proceedings
of Ontology Matching Workshop 2020, 2020.
short: 'K. Georgala, M. Sherif, A.-C. Ngonga Ngomo, in: Proceedings of Ontology
Matching Workshop 2020, 2020.'
date_created: 2021-12-17T09:53:07Z
date_updated: 2023-08-16T10:27:11Z
keyword:
- 2020 dice simba sherif hecate ngonga knowgraphs sys:relevantFor:limboproject limboproject
sys:relevantFor:infai sys:relevantFor:bis limes limbo opal georgala
language:
- iso: eng
publication: Proceedings of Ontology Matching Workshop 2020
status: public
title: LIGER – Link Discovery with Partial Recall
type: conference
user_id: '67234'
year: '2020'
...
---
_id: '55849'
abstract:
- lang: eng
text: Conference proceedings of the Music Encoding Conferences 2015, 2016 and 2017
with Introduction by Giuliano Di Bacco
citation:
ama: Di Bacco G, Kepper J, Roland PD, eds. Music Encoding Conference Proceedings
2015, 2016 and 2017. Bavarian State Library (BSB); 2019. doi:10.15463/music-1
apa: Di Bacco, G., Kepper, J., & Roland, P. D. (Eds.). (2019). Music Encoding
Conference Proceedings 2015, 2016 and 2017. Bavarian State Library (BSB).
https://doi.org/10.15463/music-1
bibtex: '@book{Di Bacco_Kepper_Roland_2019, title={Music Encoding Conference Proceedings
2015, 2016 and 2017}, DOI={10.15463/music-1},
publisher={Bavarian State Library (BSB)}, year={2019} }'
chicago: Di Bacco, Giuliano, Johannes Kepper, and Perry D. Roland, eds. Music
Encoding Conference Proceedings 2015, 2016 and 2017. Bavarian State Library
(BSB), 2019. https://doi.org/10.15463/music-1.
ieee: G. Di Bacco, J. Kepper, and P. D. Roland, Eds., Music Encoding Conference
Proceedings 2015, 2016 and 2017. Bavarian State Library (BSB), 2019.
mla: Di Bacco, Giuliano, et al., editors. Music Encoding Conference Proceedings
2015, 2016 and 2017. Bavarian State Library (BSB), 2019, doi:10.15463/music-1.
short: G. Di Bacco, J. Kepper, P.D. Roland, eds., Music Encoding Conference Proceedings
2015, 2016 and 2017, Bavarian State Library (BSB), 2019.
date_created: 2024-08-28T11:48:09Z
date_updated: 2024-08-28T13:58:21Z
department:
- _id: '538'
- _id: '874'
doi: 10.15463/music-1
editor:
- first_name: Giuliano
full_name: Di Bacco, Giuliano
last_name: Di Bacco
- first_name: Johannes
full_name: Kepper, Johannes
id: '1684'
last_name: Kepper
orcid: 0000-0003-4891-260X
- first_name: Perry D.
full_name: Roland, Perry D.
last_name: Roland
keyword:
- mec-proceedings
- mec-proceedings-2016
- mec-proceedings-2015
- mec-proceedings-2017
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.15463/music-1
oa: '1'
publication_status: published
publisher: Bavarian State Library (BSB)
status: public
title: Music Encoding Conference Proceedings 2015, 2016 and 2017
type: book_editor
user_id: '1684'
year: '2019'
...
---
_id: '55837'
abstract:
- lang: eng
text: The Freischütz Digital project (FreiDi) was one of the pioneer projects employing
MEI in large scale. It did not only try to encode a huge quantity of music material,
it also sought to capture as many aspects of the available sources as possible,
effectively creating data of almost unrivaled richness. This paper discusses the
outcomes of and experiences made in the FreiDi project.
author:
- first_name: Johannes
full_name: Kepper, Johannes
id: '1684'
last_name: Kepper
orcid: 0000-0003-4891-260X
citation:
ama: 'Kepper J. Wie? Was? Entsetzen! Lessons Learned from the Freischütz Digital
Project. In: Di Bacco G, Kepper J, Roland P, eds. Music Encoding Conference
Proceedings 2015, 2016 and 2017. Bavarian State Library (BSB); 2019:95–105.
doi:10.15463/music-1'
apa: Kepper, J. (2019). Wie? Was? Entsetzen! Lessons Learned from the Freischütz
Digital Project. In G. Di Bacco, J. Kepper, & P. Roland (Eds.), Music Encoding
Conference Proceedings 2015, 2016 and 2017 (pp. 95–105). Bavarian State Library
(BSB). https://doi.org/10.15463/music-1
bibtex: '@inproceedings{Kepper_2019, title={Wie? Was? Entsetzen! Lessons Learned
from the Freischütz Digital Project}, DOI={10.15463/music-1},
booktitle={Music Encoding Conference Proceedings 2015, 2016 and 2017}, publisher={Bavarian
State Library (BSB)}, author={Kepper, Johannes}, editor={Di Bacco, Giuliano and
Kepper, Johannes and Roland, Perry}, year={2019}, pages={95–105} }'
chicago: Kepper, Johannes. “Wie? Was? Entsetzen! Lessons Learned from the Freischütz
Digital Project.” In Music Encoding Conference Proceedings 2015, 2016 and 2017,
edited by Giuliano Di Bacco, Johannes Kepper, and Perry Roland, 95–105. Bavarian
State Library (BSB), 2019. https://doi.org/10.15463/music-1.
ieee: 'J. Kepper, “Wie? Was? Entsetzen! Lessons Learned from the Freischütz Digital
Project,” in Music Encoding Conference Proceedings 2015, 2016 and 2017,
2019, pp. 95–105, doi: 10.15463/music-1.'
mla: Kepper, Johannes. “Wie? Was? Entsetzen! Lessons Learned from the Freischütz
Digital Project.” Music Encoding Conference Proceedings 2015, 2016 and 2017,
edited by Giuliano Di Bacco et al., Bavarian State Library (BSB), 2019, pp. 95–105,
doi:10.15463/music-1.
short: 'J. Kepper, in: G. Di Bacco, J. Kepper, P. Roland (Eds.), Music Encoding
Conference Proceedings 2015, 2016 and 2017, Bavarian State Library (BSB), 2019,
pp. 95–105.'
date_created: 2024-08-28T11:44:28Z
date_updated: 2024-08-28T14:06:38Z
department:
- _id: '538'
- _id: '874'
doi: 10.15463/music-1
editor:
- first_name: Giuliano
full_name: Di Bacco, Giuliano
last_name: Di Bacco
- first_name: Johannes
full_name: Kepper, Johannes
last_name: Kepper
- first_name: Perry
full_name: Roland, Perry
last_name: Roland
keyword:
- mec-proceedings
- mec-proceedings-2016
language:
- iso: eng
page: 95–105
publication: Music Encoding Conference Proceedings 2015, 2016 and 2017
publication_status: published
publisher: Bavarian State Library (BSB)
status: public
title: Wie? Was? Entsetzen! Lessons Learned from the Freischütz Digital Project
type: conference
user_id: '1684'
year: '2019'
...