=Paper=
{{Paper
|id=Vol-1933/poster-paper-10
|storemode=property
|title=What do Biodiversity Scholars Search for? Identifying High-Level Entities for Biological Metadata
|pdfUrl=https://ceur-ws.org/Vol-1933/poster-paper-10.pdf
|volume=Vol-1933
|authors=Felicitas Löffler,Claas-Thido Pfaff,Naouel Karam,David Fichtmüller,Friederike Klan
|dblpUrl=https://dblp.org/rec/conf/semweb/LofflerPKFK17
}}
==What do Biodiversity Scholars Search for? Identifying High-Level Entities for Biological Metadata==
https://ceur-ws.org/Vol-1933/poster-paper-10.pdf
What do Biodiversity Scholars Search for?
Identifying High-Level Entities for Biological Metadata
Felicitas Löffler1 , Claas-Thido Pfaff2 , Naouel Karam3 , David Fichtmüller4 , and
Friederike Klan1
1 Heinz-Nixdorf Endowed Chair for Distributed Information Systems, FSU Jena, Germany
{felicitas.loeffler,friederike.klan}@uni-jena.de
2 Systematic Botany and Functional Biodiversity Lab, University of Leipzig, Germany
{claas-thido.pfaff@uni-leipzig.de}
3 Institute of Computer Science, Freie Universität Berlin, Germany
{naouel.karam@fu-berlin.de}
4 Botanic Garden and Botanical Museum (BGBM), Freie Universität Berlin, Germany
{d.fichtmueller@bgbm.de}
Abstract. Research questions in biodiversity are as diverse and heterogeneous as
data are. Most metadata standards are mainly data-focused and pay little attention
to the search perspective. In this work, we introduce a method to analyze the actual
information need of biodiversity scholars based on two individual studies: (1) a
series of workshops with domain experts and (2) an analysis of research and search
questions collected in three different biodiversity projects. We finally present 12
high-level entities that appear in all kinds of biological data across the different
sources evaluated.
Keywords: biological data, life sciences, biodiversity, metadata, information retrieval
1 Introduction
In the last decade, we have witnessed an unprecedented increase of open data ranging
from species-related observations, digitized specimen collections to genome or environ-
mental data offered, e.g., through remote sensing. This opens up unforeseen opportunities
particularly for biodiversity research, which relies on cross-disciplinary data analysis
to elucidate the interplay between individuals and the conditions of the environment
they inhabit, both on the macroscopic and microscopic level. At the flip side of this
development, discovering and filtering these large volumes of multidisciplinary data
becomes a more and more time-consuming and demanding task [2]. Thus, there are two
big challenges: exploring effective retrieval mechanisms that support humans in finding
relevant data and creating proper and rich metadata in order to make data findable (FAIR
principles [6]).
The biodiversity community has responded to the latter requirement by developing
metadata standards for biological data, such as Darwin Core (DwC)5 , ABCD6 or EML7 .
5 Darwin Core, http://rs.tdwg.org/dwc/
6 ABCD, http://www.tdwg.org/activities/abcd/
7 EML, http://www.dcc.ac.uk/resources/metadata-standards/eml-ecological-metadata-language
At the same time, considerable effort has been put on the formalization of domain knowl-
edge in terms of vocabularies and ontologies. By referencing this formal knowledge,
data can be richly annotated and become machine-readable. In the last years, numerous
ontologies for specific biological domains have been created, e.g., the Gene Ontology
(GO)8 for genes, the Chemical Entities of Biological Interest (ChEBI) ontology for
chemical compounds, the Environmental Ontology (ENVO) 9 for environmental features
and materials, the Phenotype Quality Ontology (PATO) for phenotypes and the NCBI
Taxonomy10 for species. In addition, high-level ontologies with an emphasis on inter-
linking biological data from different sources have been developed, e.g., the Biological
Collections Ontology (BCO) [5], the Extensible Observation Ontology (OBOE) [3] or
the Semantic Sensor Network Ontology (SSN) [1].
In the search applications we are hosting within the biodiversity projects GFBio (The
German Federation for Biological Data)11 and AquaDiva12 , we observe that existing
metadata standards and ontologies often take a data-centric view. They provide means
to well-described biodiversity data, their characteristics, their origin and the process of
their creation. However, when searching for data, scholars often do not have specific data
in mind, but rather a research question they would like to answer. Hence, we argue that
when designing metadata standards and ontologies for biodiversity both perspectives
have to be considered, the requirements given by available datasets and the way scholars
are looking for data. The contribution of the paper is twofold: (1) We propose and apply
a method that combines the findings of two different and independent approaches to
identify high-level entities that are relevant for biodiversity researchers when searching
for data (Sects. 2.1 and 2.2). (2) As a first result, we present the findings of the two
individual approaches and propose a consolidated set of biological entities (Sect. 2.3). We
consider this as a first step towards enriched metadata with information that is relevant
to information seekers. It also serves as a prerequisite for increasing the findability of
biodiversity data.
2 Methodology
Our approach to analyze the search perspective comprises two independent studies:
Assuming that properly described data can be found more easily, the goal in dedicated
workshops with scholars was to define an annotation schema that can be used to richly
describe ecological data (Sect. 2.1). The second study is oriented to evaluation methods
in information retrieval and analyzes research and search questions collected in three
biodiversity projects (Sect. 2.2). In both approaches, the aim was to enhance search
applications and to detect high-level entities that can be either used as metadata fields
or that can be linked with ontologies. The first result of biological high-level entities is
presented in Sect. 2.3.
8 GO, http://www.geneontology.org/
9 ENVO, https://github.com/EnvironmentOntology/envo
10 NCBI Taxonomy, https://www.ncbi.nlm.nih.gov/taxonomy
11 GFBio, https://www.gfbio.org
12 AquaDiva, http://www.aquadiva.uni-jena.de
2.1 Workshops with domain experts
In close collaboration between GFBio and the German Centre for Integrative Biodiver-
sity Research Halle – Jena – Leipzig (iDiv)13 , we conducted ten workshops with 35
domain experts from ecology and adjacent disciplines to develop a metadata schema
and a controlled vocabulary, the Essential Annotation Schema for Ecology (EASE)14 .
This annotation framework aims at describing ecological data from a scholar’s search
perspective. Annotation in this context refers to metadata.
Two design principles have been formulated for the development: Parsimony: The
framework aims at being as simple as possible in structure and content. Optimization
here has to be done carefully to maintain a differentiated and consistent annotation. One
example: Larger time frames in ecology are referred to by a relative reference, (e.g.,
18 million years ago) or by named geological time periods. These periods are getting
more granular from eons to ages and are nested in each other. It could be argued to make
ages optional in the annotation which sacrifices some granularity but still maintains
a consistent larger temporal context. Comprehensiveness: The framework aims at a
certain comprehensiveness defining essential orthogonal dimensions of information
which allow ecological content to be described and located in the search space of
ecology. Comprehensiveness is not accomplished by using many different dimensions
and concepts but rather a few essential and complementary ones which also reflect the
mindset and questions of researchers when looking for data.
Based on these guidelines, 8 top level categories have been selected. During the work-
shops, the top level categories were substantiated in a top down approach with increasing
detail (~1600 concepts). Here, we relied on expert knowledge of the contributors but
also on other sources such as EML, ABCD and DwC, various topic specific textbooks
(e.g., related to organic and inorganic chemistry) and standardized vocabularies (e.g., the
World Reference Base for Soil15 , and The International Chronostratigraphic Chart16 ).
The top level categories are 1. Time (e.g., date, time, timezone), 2. Space (e.g.,
bounding box, coordinates, location names), 3. Sphere (e.g., pedo-, hydro-, atmosphere
aspects), 4. Biome (e.g., zones, water availability, land use), 5. Organism (species
classification), 6. Process (e.g., processes, objects and interactions), 7. Method (general
approach, setup of gradients), 8. Chemical (e.g., elements, compounds, functions). In
addition, the framework covers a set of general information to handle associations
between primary data and annotation (e.g., data format, contact person, download URL).
2.2 Research and search questions in the biodiversity domain
In information retrieval, a lot of research has been done towards a perfect ranking [4]
whereas little attention has been paid to a user’s actual information need. What research
questions are biodiversity scholars working on? What kind of data do they want to reuse?
Do the provided metadata actually reflect a researcher’s information need? Therefore,
13 iDiv, https://www.idiv.de/
14 EASE: https://github.com/cpfaff/ease
15 WRB, http://www.fao.org/soils-portal/soil-survey/soil-classification/world-reference-base/en/
16 ICS, http://www.stratigraphy.org/index.php/ics-chart-timescale
Table 1: Example questions gathered in three biodiversity projects
Do butterflies occur on How does agriculture affect the How old does Plantago
calcareous grassland? groundwater composition? lanceolata get?
Is there data on the influence of What are suitable methods to Do cities harbour a higher
geographic elevation on the characterize microbial soil biodiversity compared to
growth rate and plant processes by gas analytical agricultural areas?
development of Zea mays? techniques?
we collected 184 search and research questions from scholars who are involved in three
biodiversity projects in Germany: GFBio (73), AquaDiva (98) and iDiv (13). Examples
are presented in Table 1. We asked for full questions as well as keywords to get the
actual information need together with the search query. We left it to the scholars to either
provide search questions posed to a search interface or broader research questions they
are currently working on to get a wider spectrum of information needs.
We analyzed the questions manually and explored whether the noun entities could be
grouped into high-level categories, such as Organism or Environment. For instance, given
the question: Is there DNA data about Amphimonhystrella (Nematoda)? the noun entities
are ’DNA data’ and ’Amphimonhystrella (Nematoda)’. The latter one is an Organism
whereas ’DNA data’ points to a certain Data Type. Finally, we grouped the noun entities
into 13 categories presented in Table 2. Organism comprises all individual life forms
including plants, fungi, bacteria, animals and microorganisms. All species live in certain
Environments and have certain characteristics that are summarized with Quality and
Phenotype. Biological, chemical and physical Processes are re-occurring and transform
materials or organisms due to chemical reactions or other influencing factors. Events
are processes that appear only once at a specific time, such as environmental disasters.
Chemical compounds, rocks, sand and sediments can be grouped as Materials and
Substances. Anatomical Entities comprise the structure of organisms, e.g., body or plant
parts, organs, cells and genes. The term Method describe all operations and experiments
that have to be conducted to lead to a certain result. Outcomes of research methods
are delivered in Data Types. All kinds of geographic information is summarized with
Location and time data including geological eras are described with Time. Person and
Organization are either projects or authors of data. As reflected in the search questions,
scholars in biodiversity are highly interested in Human Intervention on landscape and
environment, e.g., fishery, agriculture.
2.3 Discussion
Table 3 constitutes a consolidation of the main entities identified by the previously
described processes. While there is a broad consensus on entities such as Organism,
Process, Method and Time, some wording and classification on others are different.
Space in EASE actually means location information and Sphere comprises altitude
indications that is covered under Environment in the search questions. In EASE, Biome
Table 2: Categories selected from search questions with examples below
Organism Environment Quality and Process Event
Phenotype
quercus, below 4000m, length, growth rate, climate change, Deepwater Horizon
cyclothone, ground water, city reproduction rate, nitrogen oil spill, ’Tree of
globigerina traits transformation the Year 2016’
bulloides
Material and Anatomical Entity Method Data Type Location
Substance
sediment, rock, DNA, proteome, lidar measurements, lidar data, sequence Germany, Atlantic
CO2 root observation, remote data Ocean
sensing
Time Person and Human
Organization Intervention
current, over time, Deep Sea Drilling agriculture, land
triassic Project, author’s or use, crop yield
collector’s names increase
contains subfields for Land use (Human Intervention in the search questions) and data
attributes are defined as Factor under Method (Quality and Phenotype in the search
questions). Chemical is grouped under Material & Substance in the search questions
that additionally covers soil, sediments and rocks. Anatomical Entity, Data Type and
Event only occur in the search questions.
Looking at potential linkage with existing ontologies, we finally selected 12 biologi-
cal high-level entities. We left out Sphere since ontologies such as ENVO already cover
environmental features and conditions and could be extended with altitude information.
Preliminary, we will leave Data Type out. It needs to be further discussed and investigated
whether it can be classified under other entities, e.g., Method.
3 Conclusion
We described and applied a methodology for identifying high-level entities in the biodi-
versity domain that reflect the scholars’ point of view. In our future work, we will link
the identified entities to existing ontologies. Our aim is to improve the indexing process
of search applications over research data by means of these 12 categories. We would
like to automatically extract information from metadata that is related to the entities. We
believe, that this will help to improve data retrieval methods in the biodiversity domain.
Acknowledgements
We would like to thank the numerous scientists from GFBio, AquaDiva and iDiv who
took part in the workshops and/or provided questions. This work was partially funded
by the Deutsche Forschungsgemeinschaft (DFG) within the scope of the GFBio and
AquaDiva projects.
Löffler, Pfaff, Karam et al.
Table 3: Consolidation of high-level entities
EASE Questions Consolidation
Organism Organism Organism
Process Process Process
Event Event
Environment Environment Environment
Method - Factor Quality & Phenotype Quality & Phenotype
Anatomical Entity Anatomical Entity
Chemical Material & Substance Material & Substance
Method Method Method
Data Type
Time Time Time
Space Location Location
Sphere
General Information Person & Organization Person & Organization
Biome - Land use Human Intervention Human Intervention
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