CARP: Correlation Based Approach for Researcher Profiling

Noureddine, Hassan; Jarkass, Iman; Hazimeh, Hussein; Khaled, Omar Abou; Mugellini, Elena

doi:10.18293/seke2015-145

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…The set of profiles belong to researchers where each profile contains personal information and the academic work done by the researcher, having the following attributes: name, affiliation, e-mail, location, homepage, summary about his career and a list of all his publications. These profiles are generated in [20] through correlating the information extracted from heterogeneous sources, by taking advantage of data repetition in multiple sources and those existing in one source. On one hand, data is being validated and links between sources are created.…”

Section: Methodsmentioning

confidence: 99%

Towards a Multi-Feature Enabled Approach for Optimized Expert Seeking

Abdullah¹,

Noureddine²,

Makki³

et al. 2017

Computer Science &Amp; Information Technology (CS &Amp; IT)

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With the enormous growth of data, retrieving information from the Web became more desirable and even more challenging because of the Big Data issues (e.g. noise, corruption, bad quality…etc.). Expert seeking, defined as returning a ranked list of expert researchers given a topic, has been a real concern in the last 15 years. This kind of task comes in handy when building scientific committees, requiring to identify the scholars' experience to assign them the most suitable roles in addition to other factors as well. Due to the fact the Web is drowning with plenty of data, this opens up the opportunity to collect different kinds of expertise evidence. In this paper, we propose an expert seeking approach with specifying the most desirable features (i.e. criteria on which researcher's evaluation is done) along with their estimation techniques. We utilized some machine learning techniques in our system and we aim at verifying the effectiveness of incorporating influential features that go beyond publications.

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Section: Methodsmentioning

confidence: 99%

Towards a Multi-Feature Enabled Approach for Optimized Expert Seeking

Abdullah¹,

Noureddine²,

Makki³

et al. 2017

Computer Science &Amp; Information Technology (CS &Amp; IT)

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“…This approach of author entity resolution is the predecessor of our author entity matching and profiling approach (CARP) [11]. In CARP we correlate information from several web resources -DBLP one of them-to formulate researcher profiles.…”

Section: Proposed Methodsmentioning

confidence: 99%

Leveraging Co-authorship and Biographical Information for Author Ambiguity Resolution in DBLP

Hazimeh

Iman

Makki

et al. 2016

2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA)

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many authors can share the same name and this constitutes a serious problem that affects the relevancy of retrieval results and constitutes our motivation of finding such approach to cover this issue at the author names entity level. Solving such a problem may return with positive gain at the level of document retrieval, web search and the quality of data. This entity resolution task can be tackled as an unsupervised problem, where there are set of features that can be employed for the resolution job, or as supervised problem to compute the similarities among two citations and then classify if they are the same or not. Recent approaches usually utilize features such as: co-author, venue, topic similarity, affiliations and title of publications to deal with author ambiguity. In this paper, three attributes are used to treat this problem sequentially. The coauthorship firstly which is a well-known attribute, and then the topic and affiliation extracted from biographies, which can be found inside the publication, and this is our novelty frame in this paper.

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