Gianluca Demartini, Ph.D.
School of Information Technology and Electrical Engineering,
University of Queensland
St Lucia
QLD 4072 Australia
Office: +61 7 336 58325
demartini@acm.org
Dr. Gianluca Demartini is an Associate Professor in Data Science at the University of Queensland, School of Electrical Engineering and Computer Science. His main research interests are Information Retrieval, Semantic Web, and Human Computation. His research has been supported by the Australian Research Council (ARC), the Swiss National Science Foundation (SNSF), the EU H2020 framework program, the UK Engineering and Physical Sciences Research Council (EPSRC), Facebook, Google, and the Wikimedia Foundation. He received Best Paper Awards at the ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR) in 2023, AAAI Conference on Human Computation and Crowdsourcing (HCOMP) in 2018 and at the European Conference on Information Retrieval (ECIR) in 2016, the Best Short Paper Award at ECIR in 2020 and the Best Demo Award at the International Semantic Web Conference (ISWC) in 2011. He has published more than 200 peer-reviewed scientific publications including papers at major venues such as WWW, ACM SIGIR, VLDBJ, ISWC, and ACM CHI. He has given several invited talks, tutorials, and keynotes at a number of academic conferences (e.g., ISWC, ICWSM, WebScience, and the RuSSIR Summer School), companies (e.g., Facebook), and Dagstuhl seminars. He is a senior member of the ACM since 2020, an ACM Distinguished Speaker since 2015, and has been a TEDx speaker in 2019. He serves as associate editor for the Transactions on Graph Data and Knowledge (TGDK) Journal and as an editorial board member for the Information Retrieval journal. He is a steering committee member for the AAAI HCOMP conference. He was PC Chair for the ACM Conference on Research and Development in Information Retrieval (SIGIR) in 2022. He was General co-Chair for the ACM International Conference on Information and Knowledge Management (CIKM) 2021. He was Crowdsourcing and Human Computation Track co-Chair at WWW 2018 and co-chair for the Human Computation and Crowdsourcing Track at ESWC 2015. He has been Senior Program Committee member for, among others, the ACM Conference on Research and Development in Information Retrieval (SIGIR), the ACM Web Search and Data Mining (WSDM) Conference, the International Joint Conference on Artificial Intelligence (IJCAI), the AAAI Conference on Human Computation and Crowdsourcing (HCOMP), and the International Conference on Web Engineering (ICWE). He co-organized several workshops and tutorials at international conferences as well as the Entity Ranking Track at the Initiative for the Evaluation of XML Retrieval in 2008 and 2009. Before joining the University of Queensland, he was Lecturer at the University of Sheffield in UK, post-doctoral researcher at the eXascale Infolab at the University of Fribourg in Switzerland, visiting researcher at UC Berkeley, junior researcher at the L3S Research Center in Germany, and intern at Yahoo! Research in Spain. In 2011, he obtained a Ph.D. in Computer Science at the Leibniz University of Hanover focusing on Semantic Search.
Demo
This page contains a collection of links to running prototypes related to my research work.
Unfortunately, not all demos are running all the time.
How Does the Crowd Impact the Model? A tool for raising awareness of social bias in crowdsourced training data
- https://recant.cyens.org.cy/
A demo to showcase the impact of human annotator on machine learning models, developed in the context of the DESCANT project.
Common Agreement Phi
- http://agreement-measure.sheffield.ac.uk/
A tool to compute inter-rater agreement levels also including a newly proposed agreement measure designed for crowdsourcing enviroments.
TRank: Ranking Entity Types Using the Web of Data
- http://trank.exascale.info
A REST API for entity annotation and entity type selection in text. You can provide a webpage URL or a piece of text to get the list of entities and their types contained in the text. The API can be used programmatically as well.
B-hist: Entity-Centric Search over Personal Web Browsing History
Video available.
- https://www.youtube.com/watch?v=YY9ZV7Ma-gs
Web Search is increasingly entity-centric; as many common queries target specific entities, search results are progressively augmented with semi-structured and multimedia information about entities. However, search over personal Web browsing history still revolves around keyword-search mostly. B-hist aims at providing Web users with an effective tool for searching and accessing information previously looked up on the Web by providing multiple ways to filter results using temporal ranges, session-based clustering, and entity-centric search
TAER: Time Aware Entity Retrieval
Not running anymore.
- http://godzilla.kbs.uni-hannover.de:8080/TAER
This system allows to search the New York Times corpus in the period 1987-2006.
The user can provide a keyword query and select one article to read. Entities in the article are ranked according to their relevance to the user query.
SERWi: Semantic Entity Retrieval in Wikipedia
Not running anymore.
- http://serwi.L3S.uni-hannover.de/
This demo allows to run Entity Retrieval queries on top of the Wikipedia snapshot used at INEX XER 2007 and 2008.
The user can provide a keyword query describing the type of entities she wants (e.g., "countries where I can pay in Euro") and expect a ranked list of entities rapresented by their Wikipedia page
URI Match: Matching Semantic Web URIs
Not running anymore.
- http://urimatch.L3S.uni-hannover.de/
This demo, given two URIs, will tell the user whether they refer to the same real world entity or not. The system does not use the entity description rather it is purely based on the comparison of the URIs.
Finding Experts on the Semantic Desktop
Video available.
- http://www.youtube.com/watch?v=V6lfvxq2Bvo
This is a video of a prototype system developed within the Nepomuk project. The system allows to search for experts given a keyword query describing the topic of expertise. The candidate experts to be ranked and expertise evidence is extracted from the user desktop content.
ARES: A Retrieval Engine based on Sentiments
Not running anymore due to search API being shut down.
- http://ares.L3S.uni-hannover.de/
This demo allows to search the Web using three different commercial search engines. Retrieved pages are then classified according to the sentiment they express about the user query. The user is able either to see the original ranked list of results or to filter/reorder the list according to her needs
Vizio: A tool for Explorative Search
Not running anymore.
- http://vizio.L3S.uni-hannover.de/
This system allows to search the New York Times corpus in the period 1987-2006.
The user can provide a keyword query and select among different result visualization types (i.e., map, timeline, related words, person and places, and list).
Beagle++: Leveraging Personal Metadata for Desktop Search.
Video available.
- https://www.youtube.com/watch?v=Ui4GDkcR7-U
Beagle++ is an extensions to the Beagle search tool for the personal information space. Beagle++ now makes that search semantic, features you never experienced before. As a prototype it reflects current research activities towards the Semantic Desktop.
