An Academic Profile of IT Program Leadership in the U.S.

Martin, Nancy L.

doi:10.1145/3368308.3415368

Cited by 2 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Next, to take the neighbour information into consideration for an entity representation, for a specific head entity h * , the mean of description vectors of all neighbouring tail entities d t is used as initial vectors, denoted as h 0 * and vice versa, as shown in equation (3). Figure 3 shows an example about the definition of neighbours.…”

Section: Initial Valuesmentioning

confidence: 99%

“…To facilitate this task, an automatic approach to extract domain information is expected. Theoretically, an academic profile covers information from various aspects of a specific domain, including critical concepts [1], researchers’ background information [3], publications and citations of researchers [4] and so on. Existing profiling works mainly focus on explicit relationship exhibition, like collaboration and citation [5], and contain researchers’ publications as well as some bibliometric indicators.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic construction of academic profile: A case of information science domain

Geng

Chuai²,

Jin

2021

Journal of Information Science

View full text Add to dashboard Cite

To provide junior researchers with domain-specific concepts efficiently, an automatic approach for academic profiling is needed. First, to obtain personal records of a given scholar, typical supervised approaches often utilise structured data like infobox in Wikipedia as training dataset, but it may lead to a severe mis-labelling problem when they are utilised to train a model directly. To address this problem, a new relation embedding method is proposed for fine-grained entity typing, in which the initial vector of entities and a new penalty scheme are considered, based on the semantic distance of entities and relations. Also, to highlight critical concepts relevant to renowned scholars, scholars’ selective bibliographies which contain massive academic terms are analysed by a newly proposed extraction method based on logistic regression, AdaBoost algorithm and learning-to-rank techniques. It bridges the gap that conventional supervised methods only return binary classification results and fail to help researchers understand the relative importance of selected concepts. Categories of experiments on academic profiling and corresponding benchmark datasets demonstrate that proposed approaches outperform existing methods notably. The proposed techniques provide an automatic way for junior researchers to obtain organised knowledge in a specific domain, including scholars’ background information and domain-specific concepts.

show abstract

Section: Initial Valuesmentioning

confidence: 99%