Abstract
Linking an expert to his knowledge areas is still a challenging research problem. The task is usually divided into two steps: identifying the knowledge areas/topics in the text corpus and assign them to the experts. Common approaches for the expert profiling task are based on the Latent Dirichlet Allocation (LDA) algorithm. As a result, they require pre-defining the number of topics to be identified which is not ideal in most cases. Furthermore, LDA generates a list of independent topics without any kind of relationship between them. Expert profiles created using this kind of flat topic lists have been reported as highly redundant and many times either too specific or too general.
In this paper we propose a methodology that addresses these limitations by creating hierarchical expert profiles, where the knowledge areas of a researcher are mapped along different granularity levels, from broad areas to more specific ones. For the purpose, we explore the rich structure and semantics of Heterogeneous Information Networks (HINs). Our strategy is divided into two parts. First, we introduce a novel algorithm that can fully use the rich content of an HIN to create a topical hierarchy, by discovering overlapping communities and ranking the nodes inside each community. We then present a strategy to map the knowledge areas of an expert along all the levels of the hierarchy, exploiting the information we have about the expert to obtain an hierarchical profile of topics.
To test our proposed methodology, we used a computer science bibliographical dataset to create a star-schema HIN containing publications as star-nodes and authors, keywords and ISI fields as attribute-nodes. We use heterogeneous pointwise mutual information to demonstrate the quality and coherence of our created hierarchies. Furthermore, we use manually labelled data to serve as ground truth to evaluate our hierarchical expert profiles, showcasing how our strategy is capable of building accurate profiles.
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Notes
- 1.
- 2.
- 3.
Research areas created by the Institute for Scientific Information.
- 4.
For simplicity consider that the links have the same weight.
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As illustrated by Fig. 2.
- 6.
For clarification, an ’-’ symbol refers to a different level on the hierarchy.
- 7.
Through experimentation we determined that 4 was the number of levels that achieved the most comprehensible topical hierarchy.
- 8.
Following the idea of [21], we setted \(k=5\) for ISI fields since there are only 120 of them in the HIN. In these cases, the part \(\frac{1}{k^2}\) of the formula changes to \(\frac{1}{5k}\).
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Acknowledgements
This work is funded by the ERDF through the COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961, and by National Funds through the FCT as part of project UID/EEA/50014/2013. Jorge Silva is also supported by a FCT/MAP-i PhD research grant (PD/BD/128157/2016).
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Silva, J., Ribeiro, P., Silva, F. (2018). Hierarchical Expert Profiling Using Heterogeneous Information Networks. In: Soldatova, L., Vanschoren, J., Papadopoulos, G., Ceci, M. (eds) Discovery Science. DS 2018. Lecture Notes in Computer Science(), vol 11198. Springer, Cham. https://doi.org/10.1007/978-3-030-01771-2_22
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