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Zukerman, Ingrid; Litman, Diane

doi:10.1023/a:1011174108613

Cited by 59 publications

(3 citation statements)

References 78 publications

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“…More complex user information can be obtained from, e.g., context resources, such as affiliations a scholar is associated with, but also from content sources, for instance, a user's publications. Utilizing NLP techniques in user modeling has quite a long history (Zukerman & Litman, 2001) and has also become important in recent years in Information Retrieval (IR) for extracting named entities from scientific papers in order to profile scholars and to find an expert in a certain topic. Therefore, in the following subsections we focus on related work in expert profiling based on publications.…”

Section: Implicit Profile Generationmentioning

confidence: 99%

ScholarLens: extracting competences from research publications for the automatic generation of semantic user profiles

Sateli

Löffler

König‐Ries

et al. 2017

PeerJ Computer Science

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Motivation. Scientists increasingly rely on intelligent information systems to help them in their daily tasks, in particular for managing research objects, like publications or datasets. The relatively young research field of Semantic Publishing has been addressing the question how scientific applications can be improved through semantically rich representations of research objects, in order to facilitate their discovery and re-use. To complement the efforts in this area, we propose an automatic workflow to construct semantic user profiles of scholars, so that scholarly applications, like digital libraries or data repositories, can better understand their users' interests, tasks, and competences, by incorporating these user profiles in their design. To make the user profiles sharable across applications, we propose to build them based on standard semantic web technologies, in particular the Resource Description Framework (RDF) for representing user profiles and Linked Open Data (LOD) sources for representing competence topics. To avoid the cold start problem, we suggest to automatically populate these profiles by analyzing the publications (co-)authored by users, which we hypothesize reflect their research competences. Results. We developed a novel approach, ScholarLens, which can automatically generate semantic user profiles for authors of scholarly literature. For modeling the competences of scholarly users and groups, we surveyed a number of existing linked open data vocabularies. In accordance with the LOD best practices, we propose an RDF Schema (RDFS) based model for competence records that reuses existing vocabularies where appropriate. To automate the creation of semantic user profiles, we developed a complete, automated workflow that can generate semantic user profiles by analyzing full-text research articles through various natural language processing (NLP) techniques. In our method, we start by processing a set of research articles for a given user. Competences are derived by text mining the articles, including syntactic, semantic, and LOD entity linking steps. We then populate a knowledge base in RDF format with user profiles containing the extracted competences.We implemented our approach as an open source library and evaluated our system through two user studies, resulting in mean average precision (MAP) of up to 95%. As part of the evaluation, we also analyze the impact of semantic zoning of research articles on the accuracy of the resulting profiles. Finally, we demonstrate how these semantic user profiles can be applied in a number of use cases, including article ranking for personalized search and finding scientists competent in a topic -e.g., to find reviewers for a paper.

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Section: Implicit Profile Generationmentioning

confidence: 99%

ScholarLens: extracting competences from research publications for the automatic generation of semantic user profiles

Sateli

Löffler

König‐Ries

et al. 2017

PeerJ Computer Science

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“…Its personalization then involves the estimation of differences in learning styles, approaches to learning and levels of intellectual development of students [2]. The concept of learning style can be used in ITS to present information based on individual characteristics of learners [3] such as different objectives, preferences and knowledge [4] maintained in a multidimensional learner model [5].…”

Section: Learning Stylementioning

confidence: 99%

Design of an Intelligent Tutorial System based on Ontologies

Mhouti¹,

Nasseh²,

Erradi³

2014

IJCA

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Today, environments of e-learning aim increasingly to give the learner an active role in learning in order to build their knowledge. They allow thereby integrating a more learnercentered vision. However, learners show differences between learning modes that suits them best. The problem posed is how to propose to the learner a teaching sequence adequate to his/her profile.This work is at the heart of this issue. It is interested in contributing on ontologies and cognitive theories to describe the learning preferences within the Intelligent Tutorial Systems (ITS). The objective of this paper is to propose a model of ITS, based on ontologies and cognitive theories, for adapting the learning resources proposed to a learner according to his/her learning preferences. The determination of these preferences is done through the analysis of learner behavior relying on his/her indicator MBTI (Myers Briggs Type Indicator). The proposed model integrates the main functionalities of an ITS: profiling, updating of the profile, selection, adaptation and presentation of adequate resources.

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“…al. (57), to predict and eliminate secondary structures. The Visual OMP (56) can visually show the structure of each candidate probe so that users can easily reject probes with secondary structures.…”

Section: Microarray Probes and Probe Setsmentioning

confidence: 99%

Microarray probes and probe sets

Liu¹

2010

Front Biosci

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DNA microarrays have gained wide use in biomedical research by simultaneously monitoring the expression levels of a large number of genes. The successful implementation of DNA microarray technologies requires the development of methods and techniques for the fabrication of microarrays, the selection of probes to represent genes, the quantification of hybridization, and data analysis. In this paper, we concentrate on probes that are either spotted or synthesized on the glass slides through several aspects: sources of probes, the criteria for selecting probes, tools available for probe selections, and probes used in commercial microarray chips. We then provide a detailed review of one type of DNA microarray: Affymetrix GeneChips, discuss the need to re-annotate probes, review different methods for regrouping probes into probe sets, and compare various redefinitions through public available datasets.

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Untitled

Cited by 59 publications

References 78 publications

ScholarLens: extracting competences from research publications for the automatic generation of semantic user profiles

ScholarLens: extracting competences from research publications for the automatic generation of semantic user profiles

Design of an Intelligent Tutorial System based on Ontologies

Microarray probes and probe sets

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