=Paper=
{{Paper
|id=Vol-1116/paper4
|storemode=property
|title=Capturing Intent and Rationale for Linked Science: Design Patterns as a Resource for Linked Laboratory Experiments
|pdfUrl=https://ceur-ws.org/Vol-1116/paper4.pdf
|volume=Vol-1116
|dblpUrl=https://dblp.org/rec/conf/semweb/McLeanGK13
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==Capturing Intent and Rationale for Linked Science: Design Patterns as a Resource for Linked Laboratory Experiments==
https://ceur-ws.org/Vol-1116/paper4.pdf
Capturing intent and rationale for Linked Science: design
patterns as a resource for linking laboratory experiments
Cameron McLean1, Mark Gahegan1, and Fabiana Kubke2
1
University of Auckland, Centre for eResearch & Department of Computer Science
{ca.mclean,m.gahegan}@auckland.ac.nz
2
University of Auckland, School of Medical Sciences
f.kubke@auckland.ac.nz
Abstract. The notion of design patterns, after architect Christopher Alexander,
provides a powerful way to capture and describe reusable design knowledge for
complex domains. In this position paper we present the idea of design patterns
for molecular biology experiments, and discuss how they may be utilized to
support experimental design reuse, reproducibility, and a platform for linking
experiments. Design patterns provide an alternate terminology and interpretive
framework that can capture expert experience and intent that is critical yet miss-
ing from current representations of lab methods that utilize web ontologies and
computational workflows. We outline an approach to making design patterns a
first class entity in support of linked experiments on the web and provide a
glimpse of potential applications of laboratory design pattern knowledge.
Keywords: Linked Science, design patterns, semantics, ontology, workflows.
1 Introduction
While there has been much focus on the description and linkage of scientific datasets
using web ontology languages, there has been less attention on how to describe and
then link the surrounding laboratory methods that are an important step in generating
such data. The use of biomedical ontologies1 to annotate laboratory methods descrip-
tions is a much needed and necessary first step to integrating laboratory experiments,
however, current ontologies alone cannot always provide sufficient knowledge to
support all the human reasoning and situated understanding one may need to act with
such knowledge [1]. Traditionally, ontology takes in its remit the specification of
domain semantics and hierarchical decomposition, while workflow representations
encode processes - each effectively supporting the “what” and “how” of experiments
respectively. Yet reusing an experimental design requires understanding of intent and
rationale in addition to merely procedural facets - especially where they are to be
executed in heterogeneous, non-computational (wet-lab) environments. This position
paper introduces the concept of design patterns for laboratory experiments which can
act as both container and notation to admit design rationale in a linked science setting.
1
e.g. http://www.bioontology.org/
2
For examples of lab patterns and use cases see http://goo.gl/D5RZsQ
2 Design patterns for laboratory experiments
Design patterns were first introduced in the domain of architecture by Christopher
Alexander [2], as a way of encapsulating experts’ knowledge and externalizing it to
enable the generalization and communication of design. As a container for
knowledge, patterns are realized as structured documents centered on problems, solu-
tions, and the invariant “forces” that exist in a specified context. Through the invari-
ant “forces”, patterns identify, name, and abstract common themes in good design
solutions that are gained from experience [3]. The pragmatic nature of design pat-
terns, and their focus on expressing experience (rather than just domain concepts as
for ontologies, or processes as for workflows) provides an architecture that can facili-
tate the adaption and reuse of laboratory experimental designs. Patterns provide a
shared vocabulary and extensionally defined examples of solutions to complex prob-
lems and relate them back to an explicit rationale of why they are good.
Fig1. Design patterns to capture laboratory knowledge.
Patterns not only give a set of concepts and vocabulary for a domain, but do so
in a way that tells us what to do.2 The problem/solution orientation of patterns gives
us metaphorical dials to the domain, and tells us how to control and operate effective-
ly with them. Unlike ontologies which aim to be capable of expressing any valid do-
main knowledge, patterns act more like recipes (yet more abstract and general than
typical workflow representations) and give us a map3 of the model space that tells us
what parts we can vary, and what should remain invariant to achieve the desired out-
come [4]. Considered as a form of knowledge management, we believe patterns offer
additional advantages and add powerful metadata alongside traditional ontological
and workflow approaches.
Other patterns exist in the context of the semantic web e.g. workflow4 or
ontology design patterns5, which aim to provide usually domain independent con-
structs for normalizing and specifying knowledge modeling problems. In contrast, our
notion of laboratory patterns as knowledge acquisition abstracts over laboratory pro-
cedures directly (cf. the modeling of them) to provide reusable design solutions for
scientific experiments anchored in domain context - they supply us with domain con-
cepts and relationships across diverse experiments gathered around a specified design
intent.
2
For examples of lab patterns and use cases see http://goo.gl/D5RZsQ
3
A map analogy of patterns at http://hillside.net/plop/2010/papers/kohls.pdf
4
http://www.workflowpatterns.com/
5
http://ontologydesignpatterns.org/wiki/Main_Page
While we accentuate differences in representational approaches here, in reali-
ty we recognize the boundaries between ontologies, workflows, and design patterns
are fuzzy as each tries to incorporate aspects of the other. For the purpose of discus-
sion we make some general distinctions between the traditional forms of the three in
Table 1 below.
Table 1. Some general distinctions between the traditional forms of workflows, ontologies, and
design patterns for representing knowledge.
Property Workflows Ontologies Design Patterns
Mode Descriptive Descriptive Prescriptive/Instructive
Degree of formality Formal Formal Typically not formal
Concepts defined Procedurally Intensionally Extensionally
Focus Specific procedure General Specific Problem
Utility Replication and automa- Model all feasible cases Understand, adapt and
tion of processes and compute inferences reuse design solutions
Formal semantics Yes Yes No
Models Processes Knowledge /Facts Implementations
3 Making design patterns and their vocabularies web
addressable entities.
The traditional form of design patterns are structured documents written in natural
language. Thus, in order to transform them into a resource for linked science, a mech-
anism for publishing patterns and their vocabulary as defined web addressable entities
is desired.
We view design patterns as data and ask how we may publish pattern
knowledge following linked data practices. To begin we have developed a method for
capturing pattern knowledge utilizing social methods adapted from other domains
where design patterns are valid entities. The structured documents that result from
“pattern mining” are collaboratively transferred to a semantic wiki based on the On-
toWiki Application framework [5]. OntoWiki and its extensions enable the direct
semantic content authoring of a knowledge base expressed in RDF, and provide for
simple human and machine accessible interfaces for publishing linked data. Patterns
entered by users become instances of a pattern model with defined syntax and seman-
tics for pattern elements such as title, problem description, forces, context etc. The
structure and URIs provided by the semantic wiki present an important first step in
extending the form of design patterns from paper to a web based resource and sup-
ports the reuse of pattern content. Additionally, the wiki captures provenance, enables
peer review, and serves attribution and credit for design pattern authors.
The challenges to this approach consist of specifying and refining the semantic
formalization of pattern level concepts and their relations using RDFS, OWL and
appropriate logics, and subsequently tailoring the OntoWiki Application framework.
This work is non-trivial as patterns have complex, interrelated internal and external
structures, and remains the current focus of our efforts.
4 A vision for the application of laboratory pattern knowledge
The annotation of lab procedural descriptions with vocabulary and context supplied
by patterns is an obvious use of patterns as metadata. Currently, this coupling must be
created manually due to the implicit nature of many pattern concepts, but the markup
of existing documents or the authoring of future ones can be facilitated by adapting
existing annotation tools such as Rightfield [6]. Laboratory methods and other data on
the web indexed to patterns can provide an additional handle to browse, search, or
filter methods at a granular level, across domains, and at the level of design intent –
one which current semantics do not adequately provide
Patterns name invariant forces that exist in recurring lab scenarios and provide
a valuable step towards the specification of minimal information reporting guidelines
for diverse laboratory processes. Indeed, the need for “high-level abstractions of the
components of experimental workflows” has been noted [7]. Furthermore, patterns
resemble a wet-lab equivalent of abstract computational workflows described by [8].
Our vision is the creation of a laboratory pattern catalogue and web resource,
providing scientists assistance in understanding, reusing, and adapting the diversity of
published laboratory methods to their own needs. Principal in our approach is the
publication of pattern content and vocabulary as linked data, such that it may be
available for use anywhere on the semantic web.
We believe the problem/solution orientation of design patterns fits well with
the cognitive processes of laboratory scientists when engaged with methods
knowledge. In combination with workflows and ontologies, the pragmatic aspects of
pattern knowledge can help provide a type of balancing – filling a representational
gap in our methods descriptions somewhere between axiomized ontologies and work-
flows that can improve the epistemological adequacy of our scientific record.
5 References
1. Pike, W., and Gahegan, M. Beyond ontologies: Toward situated representations of scien-
tific knowledge. Int. Journal of Human-Computer Studies, 65(7), 674-688. (2007)
2. Alexander, C. The timeless way of building. New York: Oxford University Press. (1979)
3. May, D., & Taylor, P. Knowledge management with patterns. Communications of the
ACM, 46(7), 94–99. (2003)
4. Gamma, E., Helm, R., Johnson, R., & Vlissides, J.. Design patterns: Abstraction and reuse
of object-oriented design. Springer Berlin Hiedelberg. (1993)
5. Heino, N., et al. Managing Web Content Using Linked Data Principles-Combining Seman-
tic Structure with Dynamic Content Syndication. Computer Software and Applications
Conference, IEEE 35th Annual. 245-250. (2011)
6. Wolstencroft, K., et al. RightField: embedding ontology annotation in spreadsheets. Bioin-
formatics 27(14), 2021-2022. (2011)
7. Taylor, C. F., et al. Promoting coherent minimum reporting guidelines for biological and
biomedical investigations: the MIBBI project. Nature biotechnology 26:8, 889-896. (2008)
8. Garijo, D., and Gil, Y. A new approach for publishing workflows: abstractions, standards,
and linked data. Proceedings of the 6th workshop on Workflows in support of large-scale
science. ACM. (2011)