CoSIT 2017

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112 Computer Science & Information Technology (CS & IT) INTENTIONAL BLANK
TOWARDS A MULTI-FEATURE ENABLED APPROACH FOR OPTIMIZED EXPERT SEEKING Mariam Abdullah 1 , Hassan Noureddine 1 , Jawad Makki 1 , Hussein Charara 1 , Hussein Hazimeh 2 , Omar Abou Khaled 2 , Elena Mugellini 2 1 Lebanese University, Beirut, Lebanon 2 HES-SO/FR, Fribourg, Switzerland ABSTRACT 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. KEYWORDS Entity retrieval, Expert seeking, Academia, Information extraction 1. INTRODUCTION Currently, the Web is in the state of always being active since lots of data are being uploaded constantly. As a result, this has caused the Web to witness so much interactions between different organizations (company, university…etc.) for they weren’t satisfied with their own knowledge. For this reason, they used the Web to connect to the outside world for development and improvement (socially, scientifically…etc.). However, despite the interactions’ usefulness, they couldn’t help but drown the Web with plenty of data, recognized as Big Data which is a very common term nowadays. Retrieving information from the Web is classified as non-trivial for this data is likely to contain noise with no guarantees of good quality. One of the things that’s been frequently searched for is experts; seeking for experts is defined as the task of taking the user’s query as input and generating a ranked list of expert researchers as output. The query denotes the topic of expertise and the generated list is sorted according to their expertise levels in what concerns the query topic. Fifteen years ago, the scientific community showed its interest in this Dhinaharan Nagamalai et al. (Eds) : CoSIT, SIGL, AIAPP, CYBI, CRIS, SEC, DMA - 2017 pp. 113– 125, 2017. © CS & IT-CSCP 2017 DOI : 10.5121/csit.2017.70412
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Computer Science & Information Tech
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Volume Editors Dhinaharan Nagamalai
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Organization General Chair Nataraja
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Technically Sponsored by Computer S
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Fourth International Conference on
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UNSUPERVISED DETECTION OF VIOLENT C
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INVESTIGATING BINARY STRING ENCODIN
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USE OF ADAPTIVE COLOURED PETRI NETW
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2.2. Extended Adaptive Decision Tab
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4. METHODOLOGY Computer Science & I
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A NOVEL ADAPTIVE - WAVELET BASED DE
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HANDWRITTEN CHARACTER RECOGNITION U
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DIVING PERFORMANCE ASSESSMENT BY ME
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4.4. Barycentre identification Comp
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IS AI IN JEOPARDY? THE NEED TO UNDE
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A STUDY ON THE MOTION CHANGE UNDER
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B) Experiment part Computer Science
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ADAPTIVE AUTOMATA FOR GRAMMAR BASED
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AUTHOR INDEX Abdullah A. Al-Shaher
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