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Article: A long-term spatiotemporal analysis of biocrusts across a diverse arid environment: The case of the Israeli-Egyptian sandfield

Noy, Klil / Ohana-Levi, Noa / Panov, Natalya / Silver, Micha / Karnieli, Arnon

Science of the total environment. 2021 June 20, v. 774

2021

Abstract: Spatiotemporal data can be analyzed using spatial, time-series, and machine learning algorithms to extract regional biocrust trends. Analyzing the spatial trends of biocrusts through time, using satellite imagery, may improve the quantification and ... ...

Abstract	Spatiotemporal data can be analyzed using spatial, time-series, and machine learning algorithms to extract regional biocrust trends. Analyzing the spatial trends of biocrusts through time, using satellite imagery, may improve the quantification and understanding of their change drivers. The current work strives to develop a unique framework for analyzing spatiotemporal trends of the spectral Crust Index (CI), thus identifying the drivers of the biocrusts' spatial and temporal patterns. To fulfill this goal, CI maps, derived from 31 annual Landsat images, were analyzed by applying advanced statistical and machine learning algorithms. A comprehensive overview of biocrusts' spatiotemporal patterns was achieved using an integrative approach, including a long-term analysis, using the Mann-Kendall (MK) statistical test, and a short-term analysis, using a rolling MK with a window size of five years. Additionally, temporal clustering, using the partition around medoids (PAM) algorithm, was applied to model the spatial multi-annual dynamics of the CI. A Granger Causality test was then applied to quantify the relations between CI dynamics and precipitation. The findings show that 88.7% of pixels experienced a significant negative change, and only 0.5% experienced a significant positive change. A strong association was found in temporal trends among all clusters (0.67 ≤ r ≤ 0.8), signifying a regional effect due to precipitation levels (p < 0.05 for most clusters). The biocrust dynamics were also locally affected by anthropogenic factors (0.58 > CI > 0.64 and 0.64 > CI > 0.71 for strongly and weakly affected regions, respectively). A spatiotemporal analysis of a series of spaceborne images may improve conservation management by evaluating biocrust development in drylands. The suggested framework may also by applied to various disciplines related to quantifying spatial and temporal trends.
Keywords	Landsat ; algorithms ; arid lands ; biological soil crusts ; dry environmental conditions ; remote sensing ; statistical analysis ; time series analysis
Language	English
Dates of publication	2021-0620
Publishing place	Elsevier B.V.
Document type	Article
Note	NAL-AP-2-clean
ZDB-ID	121506-1
ISSN	1879-1026 ; 0048-9697
ISSN (online)	1879-1026
ISSN	0048-9697
DOI	10.1016/j.scitotenv.2021.145154
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: A long-term spatiotemporal analysis of biocrusts across a diverse arid environment: The case of the Israeli-Egyptian sandfield.

Noy, Klil / Ohana-Levi, Noa / Panov, Natalya / Silver, Micha / Karnieli, Arnon

The Science of the total environment

2021 Volume 774, Page(s) 145154

Abstract	Spatiotemporal data can be analyzed using spatial, time-series, and machine learning algorithms to extract regional biocrust trends. Analyzing the spatial trends of biocrusts through time, using satellite imagery, may improve the quantification and understanding of their change drivers. The current work strives to develop a unique framework for analyzing spatiotemporal trends of the spectral Crust Index (CI), thus identifying the drivers of the biocrusts' spatial and temporal patterns. To fulfill this goal, CI maps, derived from 31 annual Landsat images, were analyzed by applying advanced statistical and machine learning algorithms. A comprehensive overview of biocrusts' spatiotemporal patterns was achieved using an integrative approach, including a long-term analysis, using the Mann-Kendall (MK) statistical test, and a short-term analysis, using a rolling MK with a window size of five years. Additionally, temporal clustering, using the partition around medoids (PAM) algorithm, was applied to model the spatial multi-annual dynamics of the CI. A Granger Causality test was then applied to quantify the relations between CI dynamics and precipitation. The findings show that 88.7% of pixels experienced a significant negative change, and only 0.5% experienced a significant positive change. A strong association was found in temporal trends among all clusters (0.67 ≤ r ≤ 0.8), signifying a regional effect due to precipitation levels (p < 0.05 for most clusters). The biocrust dynamics were also locally affected by anthropogenic factors (0.58 > CI > 0.64 and 0.64 > CI > 0.71 for strongly and weakly affected regions, respectively). A spatiotemporal analysis of a series of spaceborne images may improve conservation management by evaluating biocrust development in drylands. The suggested framework may also by applied to various disciplines related to quantifying spatial and temporal trends.
MeSH term(s)	Egypt ; Satellite Imagery ; Spatio-Temporal Analysis
Language	English
Publishing date	2021-02-06
Publishing country	Netherlands
Document type	Journal Article ; Review
ZDB-ID	121506-1
ISSN	1879-1026 ; 0048-9697
ISSN (online)	1879-1026
ISSN	0048-9697
DOI	10.1016/j.scitotenv.2021.145154
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Article ; Online: An empirically derived taxonomy of errors in SNOMED CT.

Mortensen, Jonathan M / Musen, Mark A / Noy, Natalya F

AMIA ... Annual Symposium proceedings. AMIA Symposium

2014 Volume 2014, Page(s) 899–906

Abstract: Ontologies underpin methods throughout biomedicine and biomedical informatics. However, as ontologies increase in size and complexity, so does the likelihood that they contain errors. Effective methods that identify errors are typically manual and expert- ...

Abstract	Ontologies underpin methods throughout biomedicine and biomedical informatics. However, as ontologies increase in size and complexity, so does the likelihood that they contain errors. Effective methods that identify errors are typically manual and expert-driven; however, automated methods are essential for the size of modern biomedical ontologies. The effect of ontology errors on their application is unclear, creating a challenge in differentiating salient, relevant errors with those that have no discernable effect. As a first step in understanding the challenge of identifying salient, common errors at a large scale, we asked 5 experts to verify a random subset of complex relations in the SNOMED CT CORE Problem List Subset. The experts found 39 errors that followed several common patterns. Initially, the experts disagreed about errors almost entirely, indicating that ontology verification is very difficult and requires many eyes on the task. It is clear that additional empirically-based, application-focused ontology verification method development is necessary. Toward that end, we developed a taxonomy that can serve as a checklist to consult during ontology quality assurance.
MeSH term(s)	Biological Ontologies ; Classification ; Natural Language Processing ; Systematized Nomenclature of Medicine
Language	English
Publishing date	2014
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ISSN	1942-597X
ISSN (online)	1942-597X
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: The CKC Challenge: Exploring Tools for Collaborative Knowledge Construction.

Noy, Natalya F / Chugh, Abhita / Alani, Harith

IEEE intelligent systems

2014 Volume 23, Issue 1, Page(s) 64–68

Abstract: The great success of Web 2.0 is mainly fuelled by an infrastructure that allows web users to create, share, tag, and connect content and knowledge easily. The tools for developing structured knowledge in this manner have started to appear as well. ... ...

Abstract	The great success of Web 2.0 is mainly fuelled by an infrastructure that allows web users to create, share, tag, and connect content and knowledge easily. The tools for developing structured knowledge in this manner have started to appear as well. However, there are few, if any, user studies that are aimed at understanding what users expect from such tools, what works and what doesn't. We organized the Collaborative Knowledge Construction (CKC) Challenge to assess the state of the art for the tools that support collaborative processes for creation of various forms of structured knowledge. The goal of the Challenge was to get users to try out different tools and to learn what users expect from such tools-features that users need, features that they like or dislike. The Challenge task was to construct structured knowledge for a portal that would provide information about research. The Challenge design contained several incentives for users to participate. Forty-nine users registered for the Challenge; thirty-three of them participated actively by using the tools. We collected extensive feedback from the users where they discussed their thoughts on all the tools that they tried. In this paper, we present the results of the Challenge, discuss the features that users expect from tools for collaborative knowledge constructions, the features on which Challenge participants disagreed, and the lessons that we learned.
Language	English
Publishing date	2014-02-10
Publishing country	United States
Document type	Journal Article
ISSN	1541-1672
ISSN	1541-1672
DOI	10.1109/MIS.2008.14
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Crowdsourcing the verification of relationships in biomedical ontologies.

Mortensen, Jonathan M / Musen, Mark A / Noy, Natalya F

AMIA ... Annual Symposium proceedings. AMIA Symposium

2013 Volume 2013, Page(s) 1020–1029

Abstract: Biomedical ontologies are often large and complex, making ontology development and maintenance a challenge. To address this challenge, scientists use automated techniques to alleviate the difficulty of ontology development. However, for many ontology- ... ...

Abstract	Biomedical ontologies are often large and complex, making ontology development and maintenance a challenge. To address this challenge, scientists use automated techniques to alleviate the difficulty of ontology development. However, for many ontology-engineering tasks, human judgment is still necessary. Microtask crowdsourcing, wherein human workers receive remuneration to complete simple, short tasks, is one method to obtain contributions by humans at a large scale. Previously, we developed and refined an effective method to verify ontology hierarchy using microtask crowdsourcing. In this work, we report on applying this method to find errors in the SNOMED CT CORE subset. By using crowdsourcing via Amazon Mechanical Turk with a Bayesian inference model, we correctly verified 86% of the relations from the CORE subset of SNOMED CT in which Rector and colleagues previously identified errors via manual inspection. Our results demonstrate that an ontology developer could deploy this method in order to audit large-scale ontologies quickly and relatively cheaply.
MeSH term(s)	Bayes Theorem ; Biological Ontologies ; Crowdsourcing ; Systematized Nomenclature of Medicine
Language	English
Publishing date	2013-11-16
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ISSN	1942-597X
ISSN (online)	1942-597X
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: BioPortal as a Dataset of Linked Biomedical Ontologies and Terminologies in RDF.

Salvadores, Manuel / Alexander, Paul R / Musen, Mark A / Noy, Natalya F

Semantic web

2014 Volume 4, Issue 3, Page(s) 277–284

Abstract: BioPortal is a repository of biomedical ontologies-the largest such repository, with more than 300 ontologies to date. This set includes ontologies that were developed in OWL, OBO and other formats, as well as a large number of medical terminologies that ...

Abstract	BioPortal is a repository of biomedical ontologies-the largest such repository, with more than 300 ontologies to date. This set includes ontologies that were developed in OWL, OBO and other formats, as well as a large number of medical terminologies that the US National Library of Medicine distributes in its own proprietary format. We have published the RDF version of all these ontologies at http://sparql.bioontology.org. This dataset contains 190M triples, representing both metadata and content for the 300 ontologies. We use the metadata that the ontology authors provide and simple RDFS reasoning in order to provide dataset users with uniform access to key properties of the ontologies, such as lexical properties for the class names and provenance data. The dataset also contains 9.8M cross-ontology mappings of different types, generated both manually and automatically, which come with their own metadata.
Language	English
Publishing date	2014-08-24
Publishing country	Netherlands
Document type	Journal Article
ISSN	1570-0844
ISSN	1570-0844
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Where to Publish and Find Ontologies? A Survey of Ontology Libraries.

d'Aquin, Mathieu / Noy, Natalya F

Web semantics (Online)

2012 Volume 11, Page(s) 96–111

Abstract: One of the key promises of the Semantic Web is its potential to enable and facilitate data interoperability. The ability of data providers and application developers to share and reuse ontologies is a critical component of this data interoperability: if ... ...

Abstract	One of the key promises of the Semantic Web is its potential to enable and facilitate data interoperability. The ability of data providers and application developers to share and reuse ontologies is a critical component of this data interoperability: if different applications and data sources use the same set of well defined terms for describing their domain and data, it will be much easier for them to "talk" to one another. Ontology libraries are the systems that collect ontologies from different sources and facilitate the tasks of finding, exploring, and using these ontologies. Thus ontology libraries can serve as a link in enabling diverse users and applications to discover, evaluate, use, and publish ontologies. In this paper, we provide a survey of the growing-and surprisingly diverse-landscape of ontology libraries. We highlight how the varying scope and intended use of the libraries a ects their features, content, and potential exploitation in applications. From reviewing eleven ontology libraries, we identify a core set of questions that ontology practitioners and users should consider in choosing an ontology library for finding ontologies or publishing their own. We also discuss the research challenges that emerge from this survey, for the developers of ontology libraries to address.
Language	English
Publishing date	2012-03-07
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	2160741-2
ISSN	1570-8268
ISSN	1570-8268
DOI	10.1016/j.websem.2011.08.005
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Translating the Foundational Model of Anatomy into OWL.

Noy, Natalya F / Rubin, Daniel L

Web semantics (Online)

2008 Volume 6, Issue 2, Page(s) 133–136

Abstract: The Foundational Model of Anatomy (FMA) represents the result of manual and disciplined modeling of the structural organization of the human body. It is a tremendous resource in bioinformatics that facilitates sharing of information among applications ... ...

Abstract	The Foundational Model of Anatomy (FMA) represents the result of manual and disciplined modeling of the structural organization of the human body. It is a tremendous resource in bioinformatics that facilitates sharing of information among applications that use anatomy knowledge. The FMA was developed in Protégé and the Protégé frames language is the canonical representation language for the FMA. We present a translation of the original Protégé frame representation of the FMA into OWL. Our effort is complementary to the earlier efforts to represent FMA in OWL and is focused on two main goals: (1) representing only the information that is explicitly present in the frames representation of the FMA or that can be directly inferred from the semantics of Protégé frames; (2) representing all the information that is present in the frames representation of the FMA, thus producing an OWL representation for the complete FMA. Our complete representation of the FMA in OWL consists of two components: an OWL DL component that contains the FMA constructs that are compatible with OWL DL; and an OWL Full component that imports the OWL DL component and adds the FMA constructs that OWL DL does not allow.
Language	English
Publishing date	2008-07-07
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	2160741-2
ISSN	1570-8268
ISSN	1570-8268
DOI	10.1016/j.websem.2007.12.001
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Reasoning based quality assurance of medical ontologies: a case study.

Horridge, Matthew / Parsia, Bijan / Noy, Natalya F / Musenm, Mark A

AMIA ... Annual Symposium proceedings. AMIA Symposium

2014 Volume 2014, Page(s) 671–680

Abstract: The World Health Organisation is using OWL as a key technology to develop ICD-11 - the next version of the well-known International Classification of Diseases. Besides providing better opportunities for data integration and linkages to other well-known ... ...

Abstract	The World Health Organisation is using OWL as a key technology to develop ICD-11 - the next version of the well-known International Classification of Diseases. Besides providing better opportunities for data integration and linkages to other well-known ontologies such as SNOMED-CT, one of the main promises of using OWL is that it will enable various forms of automated error checking. In this paper we investigate how automated OWL reasoning, along with a Justification Finding Service can be used as a Quality Assurance technique for the development of large and complex ontologies such as ICD-11. Using the International Classification of Traditional Medicine (ICTM) - Chapter 24 of ICD-11 - as a case study, and an expert panel of knowledge engineers, we reveal the kinds of problems that can occur, how they can be detected, and how they can be fixed. Specifically, we found that a logically inconsistent version of the ICTM ontology could be repaired using justifications (minimal entailing subsets of an ontology). Although over 600 justifications for the inconsistency were initially computed, we found that there were three main manageable patterns or categories of justifications involving TBox and ABox axioms. These categories represented meaningful domain errors to an expert panel of ICTM project knowledge engineers, who were able to use them to successfully determine the axioms that needed to be revised in order to fix the problem. All members of the expert panel agreed that the approach was useful for debugging and ensuring the quality of ICTM.
MeSH term(s)	International Classification of Diseases ; Programming Languages ; Quality Assurance, Health Care ; Vocabulary, Controlled
Language	English
Publishing date	2014-11-14
Publishing country	United States
Document type	Journal Article
ISSN	1942-597X
ISSN (online)	1942-597X
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: How orthogonal are the OBO Foundry ontologies?

Ghazvinian, Amir / Noy, Natalya F / Musen, Mark A

Journal of biomedical semantics

2011 Volume 2 Suppl 2, Page(s) S2

Abstract: Background: Ontologies in biomedicine facilitate information integration, data exchange, search and query of biomedical data, and other critical knowledge-intensive tasks. The OBO Foundry is a collaborative effort to establish a set of principles for ... ...

Abstract	Background: Ontologies in biomedicine facilitate information integration, data exchange, search and query of biomedical data, and other critical knowledge-intensive tasks. The OBO Foundry is a collaborative effort to establish a set of principles for ontology development with the eventual goal of creating a set of interoperable reference ontologies in the domain of biomedicine. One of the key requirements to achieve this goal is to ensure that ontology developers reuse term definitions that others have already created rather than create their own definitions, thereby making the ontologies orthogonal. Methods: We used a simple lexical algorithm to analyze the extent to which the set of OBO Foundry candidate ontologies identified from September 2009 to September 2010 conforms to this vision. Specifically, we analyzed (1) the level of explicit term reuse in this set of ontologies, (2) the level of overlap, where two ontologies define similar terms independently, and (3) how the levels of reuse and overlap changed during the course of this year. Results: We found that 30% of the ontologies reuse terms from other Foundry candidates and 96% of the candidate ontologies contain terms that overlap with terms from the other ontologies. We found that while term reuse increased among the ontologies between September 2009 and September 2010, the level of overlap among the ontologies remained relatively constant. Additionally, we analyzed the six ontologies announced as OBO Foundry members on March 5, 2010, and identified that the level of overlap was extremely low, but, notably, so was the level of term reuse. Conclusions: We have created a prototype web application that allows OBO Foundry ontology developers to see which classes from their ontologies overlap with classes from other ontologies in the OBO Foundry (http://obomap.bioontology.org). From our analysis, we conclude that while the OBO Foundry has made significant progress toward orthogonality during the period of this study through increased adoption of explicit term reuse, a large amount of overlap remains among these ontologies. Furthermore, the characteristics of the identified overlap, such as the terms it comprises and its distribution among the ontologies, indicate that the achieving orthogonality will be exceptionally difficult, if not impossible.
Language	English
Publishing date	2011-05-17
Publishing country	England
Document type	Journal Article
ZDB-ID	2548651-2
ISSN	2041-1480 ; 2041-1480
ISSN (online)	2041-1480
ISSN	2041-1480
DOI	10.1186/2041-1480-2-S2-S2
Database	MEDical Literature Analysis and Retrieval System OnLINE

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