The scale of the scholarly community complicates searches within scholarly databases,
necessitating keywords to index the topics of any given work. As a result, an author’s choice in
keywords affects the visibility of each publication; making the sum of these choices a key
representation of the author’s academic profile. As such the underlying network of investigators
are often viewed through the lens of their keyword networks. Current keyword networks connect
publications only if they use the exact same keyword, meaning uncontrolled keyword choice
prevents connections despite semantic similarity. Computational understanding of semantic
similarity has already been achieved through the process of word embedding, which transforms
words to numerical vectors with context-correlated values. The resulting vectors preserve semantic
relations and can be analyzed mathematically. Here we develop a model that uses embedded
keywords to construct a network which circumvents the limitations caused by uncontrolled
vocabulary. The model pipeline begins with a set of faculty, the publications and keywords of
which are retrieved by SCOPUS API. These keywords are processed and then embedded. This
work develops a novel method of network construction that leverages the interdisciplinarity of
each publication, resulting in a unique network construction for any given set of publications. Postconstruction the network is visualized and analyzed with topological data analysis (TDA). TDA is
used to calculate the connectivity and the holes within the network, referred to as the zero and first
homology. These homologies inform how each author connects and where publication data is
sparse. This platform has successfully modelled collaborations within the biomedical department
at Purdue University and provides insight into potential future collaborations.
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/9108482 |
| Date | 15 August 2019 |
| Creators | Lance C Novak (7043189) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/NETWORK_AND_TOPOLOGICAL_ANALYSIS_OF_SCHOLARLY_METADATA_A_PLATFORM_TO_MODEL_AND_PREDICT_COLLABORATION/9108482 |
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