Semantic Search-by-Examples for Scientific Topic Corpus Expansion in Digital Libraries

Al-Natsheh, Hussein T.; Martinet, Lucie; Muhlenbach, Fabrice; Rico, Fabien; Zighed, Djamel A.

doi:10.1109/icdmw.2017.103

Cited by 11 publications

(6 citation statements)

References 28 publications

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“…Le modèle d'interrogation et de recommandation que nous avons développé est appelé SSbE pour "Semantic Search-by-Examples" Al-Natsheh, Martinet, Muhlenbach, Rico, et Zighed (2017). Son mode de fonctionnement est atypique car il n'exploite pas une requête avec des mots-clés saisis par l'utilisateur mais un ensemble d'exemples d'articles scientifiques fournis par celui-ci.…”

Section: Modèle De Recommandation Proposéunclassified

“…Cette évaluation humaine n'était pas une étape aisée car, comme les articles candidats ne présentaient pas explicitement la mention "mental rotation" en titre ou résumé, il arrivait fréquemment que les experts du domaine soient obligés de lire les articles dans leur intégralité avant de pouvoir se prononcer sur une évaluation de leur pertinence avec le sujet. Les résultats ont montré que, pour les premiers articles retournés par les deux méthodes, MLT fournissait une meilleure précision pour les premiers résultats, mais que les valeurs de précision dégringolaient vite dès que la méthode avait proposé plus de 10 articles candidats, alors que les résultats en précision de la méthode SSbE avaient une précision moyenne mais parvenaient à la conserver longtemps tout au long du déroulement de la liste des articles recommandés Al-Natsheh, Martinet, Muhlenbach, Rico, et Zighed (2017).…”

Section: Protocole Expérimental Et Cas D'étudeunclassified

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Combinaisons d’approches statistiques et sémantiques appliquées aux bibliothèques numériques scientifiques pour la promotion de la recherche pluridisciplinaire

Muhlenbach¹,

Al-Natsheh²

2020

ROISI

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La connaissance dans tous les domaines scientifiques est maintenant disponible dans les bibliothèques numériques. Le problème est que les articles appartenant à différentes communautés de recherche n'emploient pas le même vocabulaire pour parler du même sujet. L'accès aux documents pertinents avec des outils de recherche d'information, des moteurs de recherche ou des systèmes de recommandation d'articles scientifiques échouera si ces méthodes ne permettent pas d'intégrer cette variabilité linguistique. Dans ce travail, nous présentons des stratégies d'utilisation des technologies d'intelligence artificielle pour parvenir à étendre la recherche documentaire de manière appropriée afin d'apporter de la diversité dans les résultats recommandés et favoriser ainsi une recherche pluridisciplinaire. ABSTRACT. The knowledge of all science domains is now available on digital libraries. The problem is that the papers belonging to different research communities do not use the same vocabulary to talk about the same subject. Access to relevant documents with information retrieval tools, search engines or research-paper recommender systems will fail if these methods do not consider this linguistic variability. In this work, we present strategies for using artificial intelligence technologies to successfully expand the literature search to bring diversity to the recommended results, thereby promoting multidisciplinary research. MOTS-CLÉS. système de recommandation, bibliothèque numérique, recherche pluridisciplinaire, similarité sémantique, plongement lexical, épistémologie.

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Section: Modèle De Recommandation Proposéunclassified

Section: Protocole Expérimental Et Cas D'étudeunclassified

Combinaisons d’approches statistiques et sémantiques appliquées aux bibliothèques numériques scientifiques pour la promotion de la recherche pluridisciplinaire

Muhlenbach¹,

Al-Natsheh²

2020

ROISI

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“…As part of the projects funded by the CNRS and intended to benefit from the ISTEX platform, we have proposed to develop a research-paper recommender system whose purpose is to favor diversity. Instead of being focused on the sole criterion of accuracy of results, as most other systems do [25], we propose a system able to recommend scientific papers through a semantic similarity model [26] based on computational linguistics methods and word embedding techniques [27], as shown on Figure 3.…”

Section: Case Study 2: Research-paper Recommendermentioning

confidence: 99%

A Methodology for Ethics-by-Design AI Systems: Dealing with Human Value Conflicts

Muhlenbach¹

2020

Preprint

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The introduction of artificial intelligence into activities traditionally carried out by human beings produces brutal changes. This is not without consequences for human values. This paper is about designing and implementing models of ethical behaviors in AI-based systems, and more specifically it presents a methodology for designing systems that take ethical aspects into account at an early stage while finding an innovative solution to prevent human values from being affected. Two case studies where AI-based innovations complement economic and social proposals with this methodology are presented: one in the field of culture and operated by a private company, the other in the field of scientific research and supported by a state organization.

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“…Semantic Web techniques and natural language processing (NLP) provide computers with the ability to decipher the meaning of information and process phrases written or spoken in natural language [19]. The combination of these techniques can allow users to search for data using natural language sentences [20,21] in multiple languages, and they can also be used to find semantic similarities among disparate documents from different disciplines [22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

A Virtual Community for Disability Advocacy: Development of a Searchable Artificial Intelligence–Supported Platform

Morr¹,

Maret²,

Muhlenbach³

et al. 2021

JMIR Form Res

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Background The lack of availability of disability data has been identified as a major challenge hindering continuous disability equity monitoring. It is important to develop a platform that enables searching for disability data to expose systemic discrimination and social exclusion, which increase vulnerability to inequitable social conditions. Objective Our project aims to create an accessible and multilingual pilot disability website that structures and integrates data about people with disabilities and provides data for national and international disability advocacy communities. The platform will be endowed with a document upload function with hybrid (automated and manual) paragraph tagging, while the querying function will involve an intelligent natural language search in the supported languages. Methods We have designed and implemented a virtual community platform using Wikibase, Semantic Web, machine learning, and web programming tools to enable disability communities to upload and search for disability documents. The platform data model is based on an ontology we have designed following the United Nations Convention on the Rights of Persons with Disabilities (CRPD). The virtual community facilitates the uploading and sharing of validated information, and supports disability rights advocacy by enabling dissemination of knowledge. Results Using health informatics and artificial intelligence techniques (namely Semantic Web, machine learning, and natural language processing techniques), we were able to develop a pilot virtual community that supports disability rights advocacy by facilitating uploading, sharing, and accessing disability data. The system consists of a website on top of a Wikibase (a Semantic Web–based datastore). The virtual community accepts 4 types of users: information producers, information consumers, validators, and administrators. The virtual community enables the uploading of documents, semiautomatic tagging of their paragraphs with meaningful keywords, and validation of the process before uploading the data to the disability Wikibase. Once uploaded, public users (information consumers) can perform a semantic search using an intelligent and multilingual search engine (QAnswer). Further enhancements of the platform are planned. Conclusions The platform ontology is flexible and can accommodate advocacy reports and disability policy and legislation from specific jurisdictions, which can be accessed in relation to the CRPD articles. The platform ontology can be expanded to fit international contexts. The virtual community supports information upload and search. Semiautomatic tagging and intelligent multilingual semantic search using natural language are enabled using artificial intelligence techniques, namely Semantic Web, machine learning, and natural language processing.

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Semantic Search-by-Examples for Scientific Topic Corpus Expansion in Digital Libraries

Cited by 11 publications

References 28 publications

Combinaisons d’approches statistiques et sémantiques appliquées aux bibliothèques numériques scientifiques pour la promotion de la recherche pluridisciplinaire

Combinaisons d’approches statistiques et sémantiques appliquées aux bibliothèques numériques scientifiques pour la promotion de la recherche pluridisciplinaire

A Methodology for Ethics-by-Design AI Systems: Dealing with Human Value Conflicts

A Virtual Community for Disability Advocacy: Development of a Searchable Artificial Intelligence–Supported Platform

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