use non-breaking space in front of most citations

paper.tex

@@ -83,8 +83,8 @@ \section{Introduction}\label{sec:introduction}
 The Anthropological Notation Ontology (ANNO) is motivated by the need for consistent data recording, enhanced comparability, facilitated access to, and sustainable preservation of human skeletal remains for research and cultural heritage contexts.
 It provides a standardized, digital framework for documenting, analyzing, and presenting data from human skeletal remains, making information accessible and usable for future research.
 ANNO supports the use of digital 3D models of bones in 3D editors such as AnthroWorks3D, playing a crucial role in categorizing and analyzing data in a flexible yet standardized and machine-readable manner, thus ensuring interoperability across different systems and studies.
-Anthropological analysis of human skeletal remains is inherently comparative, heavily relying on reference collections \citep{spurensuche}.
-Several key challenges profoundly affect anthropological research \citep{aw3dcidoc}:
+Anthropological analysis of human skeletal remains is inherently comparative, heavily relying on reference collections~\citep{spurensuche}.
+Several key challenges profoundly affect anthropological research~\citep{aw3dcidoc}:
 
 \paragraph{Preservation}\label{sec:preserve}
 From the moment of excavation, skeletal remains are subject to wear and tear, diminishing their informational value.
@@ -105,7 +105,7 @@ \section{Introduction}\label{sec:introduction}
 Existing data recording systems in anthropology are often individualized, leading to compatibility issues that hinder comparative analysis and comprehensive intra- and interdisciplinary research~\cite{HeuschkelSchmiedelLabudde2024}.
 A digital solution combining ANNO, a standardized ontology for skeletal data, and AnthroWorks 3D, a 3D editor for creating and annotating digital bone models, provides unprocessed and undistorted information, making it comprehensible, reproducible, and sustainably documented.
 It also supports flexible and objective documentation of bone parts or fragments~\cite{aw3dcidoc}.
-This allows researchers to "surround themselves with every shred of information about a collection or an object,"~\cite[p.~148]{Palkovich.2001} with future possibilities such as integration into collection management systems and the application of AI analysis \cite{HeuschkelSchmiedelLabudde2024}. 
+This allows researchers to "surround themselves with every shred of information about a collection or an object,"~\cite[p.~148]{Palkovich.2001} with future possibilities such as integration into collection management systems and the application of AI analysis~\cite{HeuschkelSchmiedelLabudde2024}. 
 For instance, researchers comparing skeletal remains from different regions can use ANNO to standardize their annotations, ensuring that data from disparate sources is compatible.
 For instance, if two teams are studying bone wear patterns to infer lifestyle differences using different skeletal collections, ANNO allows them to document their findings in a uniform manner, facilitating direct comparisons and more robust conclusions.
 
@@ -376,8 +376,8 @@ \subsection{Methodology}\label{sec:methodology}
 The Anthropological Notation Ontology (ANNO) is motivated by the need for consistent data recording, enhanced comparability, facilitated access to, and sustainable preservation of human skeletal remains for research and cultural heritage contexts.
 It provides a standardized, digital framework for documenting, analyzing, and presenting data from human skeletal remains, making information accessible and usable for future research.
 ANNO supports the use of digital 3D models of bones in 3D editors such as AnthroWorks3D, playing a crucial role in categorizing and analyzing data in a flexible yet standardized and machine-readable manner, thus ensuring interoperability across different systems and studies.
-Anthropological analysis of human skeletal remains is inherently comparative, heavily relying on reference collections \citep{spurensuche}.
-Several key challenges profoundly affect anthropological research \citep{aw3dcidoc}:
+Anthropological analysis of human skeletal remains is inherently comparative, heavily relying on reference collections~\citep{spurensuche}.
+Several key challenges profoundly affect anthropological research~\citep{aw3dcidoc}:
 
 \paragraph{Preservation}\label{sec:preserve}
 From the moment of excavation, skeletal remains are subject to wear and tear, diminishing their informational value.
@@ -398,7 +398,7 @@ \subsection{Methodology}\label{sec:methodology}
 Existing data recording systems in anthropology are often individualized, leading to compatibility issues that hinder comparative analysis and comprehensive intra- and interdisciplinary research~\cite{HeuschkelSchmiedelLabudde2024}.
 A digital solution combining ANNO, a standardized ontology for skeletal data, and AnthroWorks 3D, a 3D editor for creating and annotating digital bone models, provides unprocessed and undistorted information, making it comprehensible, reproducible, and sustainably documented.
 It also supports flexible and objective documentation of bone parts or fragments~\cite{aw3dcidoc}.
-This allows researchers to "surround themselves with every shred of information about a collection or an object,"~\cite{Palkovich.2001} with future possibilities such as integration into collection management systems and the application of AI analysis \cite{HeuschkelSchmiedelLabudde2024}. 
+This allows researchers to "surround themselves with every shred of information about a collection or an object,"~\cite{Palkovich.2001} with future possibilities such as integration into collection management systems and the application of AI analysis~\cite{HeuschkelSchmiedelLabudde2024}. 
 For instance, researchers comparing skeletal remains from different regions can use ANNO to standardize their annotations, ensuring that data from disparate sources is compatible.
 For instance, if two teams are studying bone wear patterns to infer lifestyle differences using different skeletal collections, ANNO allows them to document their findings in a uniform manner, facilitating direct comparisons and more robust conclusions.
 
@@ -418,23 +418,23 @@ \subsection{Methodology}\label{sec:methodology}
 \end{figure}
 
 %\section{Methodology for developing ANNO}
-For the development of ANNO, we applied the onto-axiomatic method, a combination of the axiomatic method with a top-level ontology \citep{baumann2014, herre2010}.
-The axiomatic method comprises principles for developing theories or formal knowledge bases, which aim at the foundation, systematisation and formalisation of a knowledge domain \citep{baumann2014, herre2010}.
+For the development of ANNO, we applied the onto-axiomatic method, a combination of the axiomatic method with a top-level ontology~\citep{baumann2014, herre2010}.
+The axiomatic method comprises principles for developing theories or formal knowledge bases, which aim at the foundation, systematisation and formalisation of a knowledge domain~\citep{baumann2014, herre2010}.
 When knowledge is systematized, a set of categories is considered \emph{primitive} or \emph{basic}.
-Such categories are not explicitly defined, but implicitly described by axioms \citep{Hilbert1918}.
+Such categories are not explicitly defined, but implicitly described by axioms~\citep{Hilbert1918}.
 An example of a primitive category is \enquote{part}.
-New notions can be introduced by explicit definitions based on the primitive or already defined categories \citep{herre2010}.
+New notions can be introduced by explicit definitions based on the primitive or already defined categories~\citep{herre2010}.
 
 The considered axioms differ in their degree of abstraction.
 At the most general level of abstraction, they are provided by top-level ontologies, whose axioms and categories can be applied to most domains of the world.
-The onto-axiomatic method combines the axiomatic method with a top-level ontology, which is used to create more specialised core and domain-specific ontologies \citep{baumann2014}.
-Possible ways of discovering axioms in empirical domains are generalisation based on single cases and idealisation \citep{baumann2011}.
+The onto-axiomatic method combines the axiomatic method with a top-level ontology, which is used to create more specialised core and domain-specific ontologies~\citep{baumann2014}.
+Possible ways of discovering axioms in empirical domains are generalisation based on single cases and idealisation~\citep{baumann2011}.
 
-We built ANNO on the basis of an ontology development schema that includes three main steps: 1. Domain Specification, 2. Conceptualisation and 3. Axiomatization \citep{herre2010}, and used the General Formal Ontology (GFO) \citep{Loebe2022, Burek2020, herre2010} as a top-level ontology in the sense of the onto-axiomatic method.
+We built ANNO on the basis of an ontology development schema that includes three main steps: 1. Domain Specification, 2. Conceptualisation and 3. Axiomatization~\citep{herre2010}, and used the General Formal Ontology (GFO)~\citep{Loebe2022, Burek2020, herre2010} as a top-level ontology in the sense of the onto-axiomatic method.
 
 \paragraph{Domain specification}
 As part of the domain specification, the anthropology experts conducted an extensive review of existing literature and ontologies, analysing and classifying the relevant information.
-Together with ontologists, relevant use cases and competence questions\footnotemark{} \citep{XD2016, MOMo2023}, as well as views and classification principles of the objects in the anthropology domain \citep{herre2010} were discussed.
+Together with ontologists, relevant use cases and competence questions\footnotemark{}~\citep{XD2016, MOMo2023}, as well as views and classification principles of the objects in the anthropology domain~\citep{herre2010} were discussed.
 \footnotetext{queries that the ontology must be able to answer}
 %
 The following main use cases and competence questions (sub-items, selected examples) were defined:
@@ -466,23 +466,23 @@ \subsection{Methodology}\label{sec:methodology}
 The objects are classified according to their type and part-whole relationship.
 
 \paragraph{Conceptualisation}
-During the conceptualisation phase \citep{herre2010}, the core concepts (categories, classes) and relations were introduced that form ANNOdc (domain-core ontology) (\cref{fig:annodc}).
+During the conceptualisation phase~\citep{herre2010}, the core concepts (categories, classes) and relations were introduced that form ANNOdc (domain-core ontology) (\cref{fig:annodc}).
 The concepts were created by generalising and classifying the domain objects.
 To answer the competence questions of the first use case, for example, a distinction must be made between whole bones, bone parts and composite bone structures.
 Therefore, the corresponding concepts \enquote{Bone}, \enquote{Bone part} and \enquote{Bone compound} were introduced.
 Further concepts were defined to represent spatial relations and phenotypes.
 Furthermore, we identified relations (e.g., partOf, boundaryOf or locationOf) relevant to capture axioms covering the defined use cases, see \cref{fig:annodc}.
 
 \paragraph{Axiomatization}
-For the axiomatization and formal foundation of ANNO we utilised GFO as a top-level ontology and reused its categories, relations, axioms and modules (as a kind of Ontology Design Patterns \citep{ODP2005, XD2016, MOMo2023}).
-We instantiated specifically the GFO modules \enquote{Material objects}, \enquote{Attributives} and \enquote{Space} \citep{Burek2020, Loebe2021, Loebe2018} and adapted them according to the requirements of the use cases.
+For the axiomatization and formal foundation of ANNO we utilised GFO as a top-level ontology and reused its categories, relations, axioms and modules (as a kind of Ontology Design Patterns~\citep{ODP2005, XD2016, MOMo2023}).
+We instantiated specifically the GFO modules \enquote{Material objects}, \enquote{Attributives} and \enquote{Space}~\citep{Burek2020, Loebe2021, Loebe2018} and adapted them according to the requirements of the use cases.
 Domain concepts introduced in the conceptualisation phase were embedded in GFO, i.e., defined as subcategories (subclasses) of certain GFO categories (fig. 1).
 The required relations between the concepts were then either adopted from GFO or derived (specialised) from the corresponding GFO relation.
-For example, GFO specifies that material objects occupy space regions \citep{Loebe2021}.
+For example, GFO specifies that material objects occupy space regions~\citep{Loebe2021}.
 We integrated this pattern in ANNOdc by deriving the class \anno{AnatomicalStructure} from \aurl{gfo}{MaterialObject} and \anno{AnatomicalSpace} from \aurl{gfo}{SpaceRegion.}
 In this way, we were able to adopt the whole axiom including the \enquote{occupies} relation between the two ANNO classes.
-In a similar way, we applied the GFO pattern \enquote{point region is boundary of line region is boundary of surface region is boundary of space region} \citep{baumann2016} to the ANNO categories anatomical point, line, surface and space.
-According to the GFO relator \citep{Loebe2018} pattern, we defined the class \anno{RelativeAnatomicalLocation} including the two roles: target (\anno{locationOf}) and reference (\anno{relativeTo}) anatomical entity. % (chapter $\ldots$).
+In a similar way, we applied the GFO pattern \enquote{point region is boundary of line region is boundary of surface region is boundary of space region}~\citep{baumann2016} to the ANNO categories anatomical point, line, surface and space.
+According to the GFO relator~\citep{Loebe2018} pattern, we defined the class \anno{RelativeAnatomicalLocation} including the two roles: target (\anno{locationOf}) and reference (\anno{relativeTo}) anatomical entity. % (chapter $\ldots$).
 
 Further axioms were introduced to precisely define certain categories.
 In addition to an explicit textual definition of the category \enquote{Bone compound}, for example, we introduced the following axiom: