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{{Paper
|id=Vol-2849/paper-16
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-2849/paper-16.pdf
|volume=Vol-2849
|dblpUrl=https://dblp.org/rec/conf/swat4ls/HalawaniFL19
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==None==
https://ceur-ws.org/Vol-2849/paper-16.pdf
Semi-Automated Data-Driven Methods to
Support Ontology Development
A Case Study on a Rehabilitation Therapy Ontology
Mohammad K. Halawani1,3[0000−0003−0730−5870] , Rob
Forsyth2[0000−0002−5657−4180] , and Phillip Lord1[0000−0002−4699−6769]
1
School of Computing, Newcastle University, UK
2
Institute of Neuroscience, Newcastle University, UK
3
Department of Information Systems, Umm Al-Qura University, Saudi Arabia
Ontology development is expensive and requires significant efforts from both
domain experts and ontologists. Automating the process usually produces unsat-
isfactory results and involves knowledge acquisition, which is intrinsically hard.
In this abstract, we are investigating semi-automated techniques for bootstrap-
ping and and supporting data-driven ontology development.
Rehabilitation therapies are hard to describe, measure and compare; unlike
pharmacologic therapies, they are not precisely defined. This brings an interest-
ing ontological challenge, because rehabilitation treatments are practice-based,
diverse and involve interactions between a therapist, a patient and their envi-
ronment. Therefore, we are using the domain of rehabilitation as a case study
to build a rehabilitation therapy ontology (RTO).
Here, we are proposing a pipeline for building semantic knowledge structures
to support developing ontologies from biomedical literature. The pipeline starts
with an initial small set of articles provided by experts in the domain. This
requires relatively little from the domain expert, beyond a set of references to
appropriate papers, something that most researchers will have through their
normal bibliography management facilities.
Fig. 1: Pipeline to support ontology development from literature.
Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
2 M. K. Halawani et al.
The initial set of articles does not cover the domain; therefore, we expand
this to a corpus of PubMed records that are relevant and cover the scope of the
initial set using live PubMed’s similar articles functionality and our pioneered
relative similarity measure [1], that retrieves articles related to the whole initial
set. In our case study , we were able to expand from initial set of 200 references,
provided from two experts in the domain of rehabilitation, to around 28,000
references using this technique.
Full texts of the identified records of the corpus are then retrieved and pass
through several text pre-processing and cleaning steps. For phrase detection,
then, we apply word2phrase which is based on words’ co-occurrences. Words
and phrases in the text are the terms of the corpus, but they are not represen-
tative of the domain. To determine semantically meaningful and domain-related
representative terminology, we apply the term frequency- inverse document fre-
quency (tf-idf ) technique. The result is a list of terms and phrases that are
ranked according to their representation of the domain. Domain experts can ar-
bitrarily threshold through the tf-idf scores to identify and extract top ranked
representative terms.
The list of extracted terms can neither represent the semantics of the terms
nor the relationships amongst them. Therefore, we develop a semantic knowledge
structure that represents those. To develop the knowledge structure, we facilitate
the list of extracted terms, their word embeddings from a trained word2vec [2]
model, and a Directed Acyclic Graph (DAG) based on their lexical similarities,
i.e. string-substring relationships. Semantic “subclass” relationships were found
amongst the terms using the word2vec analogy technique. These were confirmed
via the lexical DAG. Thus, we have a taxonomy-like knowledge structure based
on word2vec semantic relationships. To add more relationships to the structure
that are different from the “subclass” relationships, we can modify the word2vec
analogy questions.
We hope that the final structure can be used to bootstrap an ontology by
domain experts and curators rather than starting from scratch. This is similar to
scaffolding the mitochondrial disease ontology [3]; nevertheless rather than using
scaffolds from existing knowledge sources, here, we have generated the scaffolds
in a data-driven method. These scaffolds are initially linked to easily discover
semantic relations, and have a “todo” list ranked with their importance (i.e. the
ranked list of terms) for curators to bootstrap the ontology in order.
References
1. Halawani, M.K., Forsyth, R., Lord, P.: A literature based approach to define the
scope of biomedical ontologies: A case study on a rehabilitation therapy ontology.
arXiv preprint arXiv:1709.09450 (2017)
2. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed repre-
sentations of words and phrases and their compositionality. In: Advances in neural
information processing systems. pp. 3111–3119 (2013)
3. Warrender, J.D., Lord, P.: Scaffolding the mitochondrial disease ontology from ex-
tant knowledge sources. arXiv preprint arXiv:1505.04114 (2015)