Aggregated, interoperable and multi-domain user profiles for the social web

Orlandi, Fabrizio; Breslin, John G.; Passant, Alexandre

doi:10.1145/2362499.2362506

Cited by 64 publications

(79 citation statements)

References 11 publications

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“…The IntelLEO vocabularies are inter-linked with other user model ontologies, such as Friend of a Friend (FOAF) (http://www.foaf-project.org/). Thanks to the simplicity and inter-linking of FOAF to other Linked Open Vocabularies (LOVs), it has become very popular in recent years and is integrated in numerous personalized applications (Celma, 2006;Raad, Chbeir & Dipanda, 2010;Orlandi, Breslin & Passant, 2012). FOAF's RDF-based vocabulary provides for describing basic user information with predefined entities, such as name, email, homepage, and interests, as well as modeling both individuals and groups in social networks.…”

Section: Vocabularies For Competence Modelingmentioning

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: Vocabularies For Competence Modelingmentioning

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

View full text Add to dashboard Cite

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“…Considering the social network context, there are many efforts that cover the user modelling stages, either focusing on a single social network or integrating information from some of them. For example, [27] obtains Facebook data for recommendation processes; [28] retrieves user interactions from Facebook to produce behavioural patterns; [29] proposes a model to capture social relationships and status from different social media contexts; or [30] describes how to aggregate different user profiles from several social networks. The study presented in [31] includes an in depth comparison of the user modelling strategies in a multi-social context.…”

Section: Related Workmentioning

confidence: 99%

Creating and Modelling Personal Socio-Economic Networks in On-Line Banking

Miguel

Álamo

Yelmo

2015

Lecture Notes in Computer Science

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Abstract. The banking industry is observing how new competitors threaten its millennial business model by targeting unbanked people, offering new financial services to their customer base, and even enabling new channels for existing services and customers. The knowledge on users, their behaviour, and expectations become a key asset in this new context. Well aware of this situation, the Center for Open Middleware, a joint technology center created by Santander Bank and Universidad Politécnica de Madrid, has launched a set of initiatives to allow the experimental analysis and management of socio-economic information. PosdataP2P service is one of them, which seeks to model the economic ties between the holders of university smart cards, leveraging on the social networks the holders are subscribed to. In this paper we describe the design principles guiding the development of the system, its architecture and some implementation details.

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“…Users interact with each other by creating and sharing content and by expressing their interests on different social Websites [15]. Social-based users' profiles exploit those data in order to extract knowledge about the searcher's preferences and interests.…”

Section: Personalized Searchmentioning

confidence: 99%

Time-Sensitive User Profile for Optimizing Search Personlization

Kacem

Faiz

2014

User Modeling, Adaptation, and Personalization

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 15229The contribution was presented at UMAP 2014 : http://www.um.org/umap2014/umap2014/index.html Any correspondence concerning this service should be sent to the repository administrator: staff-oatao@listes-diff.inp-toulouse.fr Abstract. Thanks to social Web services, Web search engines have the opportunity to afford personalized search results that better fit the user's information needs and interests. To achieve this goal, many personalized search approaches explore user's social Web interactions to extract his preferences and interests, and use them to model his profile. In our approach, the user profile is implicitly represented as a vector of weighted terms which correspond to the user's interests extracted from his online social activities. As the user interests may change over time, we propose to weight profiles terms not only according to the content of these activities but also by considering the freshness. More precisely, the weights are adjusted with a temporal feature. In order to evaluate our approach, we model the user profile according to data collected from Twitter. Then, we rerank initial search results accurately to the user profile. Moreover, we proved the significance of adding a temporal feature by comparing our method with baselines models that does not consider the user profile dynamics.

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Aggregated, interoperable and multi-domain user profiles for the social web

Cited by 64 publications

References 11 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

Creating and Modelling Personal Socio-Economic Networks in On-Line Banking

Time-Sensitive User Profile for Optimizing Search Personlization

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