Evidence map›Paper›PMID 30505251›Full record

ArticleSemantic web2018

Discovery of Emerging Design Patterns in Ontologies Using Tree Mining.

Agnieszka Ławrynowicz, Jedrzej Potoniec, Michał Robaczyk, Tania Tudorache

Abstract read
In one paragraph

Article in Semantic web, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Agnieszka ŁawrynowiczFaculty of Computing, Poznan University of Technology, ul. Piotrowo 3, 60-965 Poznan, Poland.
Jedrzej PotoniecFaculty of Computing, Poznan University of Technology, ul. Piotrowo 3, 60-965 Poznan, Poland.
Michał RobaczykFaculty of Computing, Poznan University of Technology, ul. Piotrowo 3, 60-965 Poznan, Poland.
Tania TudoracheStanford Center for Biomedical Informatics Research, Stanford University, 1265 Welch Road, Stanford, CA 94305, USA.

Funding

Collaborative Development of Biomedical Ontologies and TerminologiesR01GM086587 · NIGMS · STANFORD UNIVERSITY · PI MUSEN, MARK A · 2009 to 2016
$4.1M
Protege: A Knowledge-Engineering Environment for Advancing Biomedical SciencesR01GM121724 · NIGMS · STANFORD UNIVERSITY · PI MUSEN, MARK A · 2017 to 2020
$2.3M
Protege: An Ontology-Development Platform for Biomedical ScientistsR01GM103316 · NIGMS · STANFORD UNIVERSITY · PI MUSEN, MARK A · 2013 to 2016
$2.1M
NIGMS NIH HHS R01 GM086587NIGMS NIH HHS R01 GM103316NIGMS NIH HHS R01 GM121724
6 · The paper itself

Abstract

The research goal of this work is to investigate modeling patterns that recur in ontologies. Such patterns may originate from certain design solutions, and they may possibly indicate emerging ontology design patterns. We describe our tree-mining method for identifying the emerging design patterns. The method works in two steps: (1) we transform the ontology axioms in a tree shape in order to find axiom patterns; and then, (2) we use association analysis to mine co-occuring axiom patterns in order to extract emerging design patterns. We conduct an experimental study on a set of 331 ontologies from the BioPortal repository. We show that recurring axiom patterns appear across all individual ontologies, as well as across the whole set. In individual ontologies, we find frequent and non-trivial patterns with and without variables. Some of the former patterns have more than 300,000 occurrences. The longest pattern without a variable discovered from the whole ontology set has size 12, and it appears in 14 ontologies. To the best of our knowledge, this is the first method for automatic discovery of emerging design patterns in ontologies. Finally, we demonstrate that we are able to automatically detect patterns, for which we have manually confirmed that they are fragments of ontology design patterns described in the literature. Since our method is not specific to particular ontologies, we conclude that we should be able to discover new, emerging design patterns for arbitrary ontology sets.

Indexed as

BioPortalemerging design patternODPontologyontology fragmentontology reusepattern miningtree mining

Identifiers

PMID30505251
PMCPMC6261490

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.