Chaired by: Lucie-Aimée Kaffee, Kemele M. Endris, Maria-Esther Vidal
Website: https://events.tib.eu/amar2019/
Recently, there has been a rapid growth in the amount of data available on the Web. Data is produced by different communities working in a wide range of domains, using several techniques. This way a large volume of data in different formats and languages is generated. Accessibility of such heterogeneous and multilingual data becomes an obstacle for reuse due to the incompatibility of data formats and the language gap [2]. This incompatibility of data formats impedes the accessibility of data sources to the right community. For instance, most of open domain question answering systems are developed to be effective when data is represented in RDF [1]. They can not operate with data in the very common CSV files or presented in unstructured formats. Usually, the data they draw from is in English rendering them unable to answer questions e.g. in Spanish. On the other hand, NLP applications in Spanish cannot make use of a knowledge graph in English. Different communities have different requirements in terms of data representation and modeling. It is crucial to make the data interoperable to make it accessible for a variety of applications.
Lucie-Aimée Kaffee is a PhD candidate at ECS, University of Southamp- ton and Research Associate at TIB, Hannover. Her research focus is mul- tilingual linked data. Lucie was the proceedings chair of ISWC 2018, and participated in the PC of ESWC 2019, ISWC 2019 and SEMATNiCS 2019 and of the workshops Wikidata Quality 2019 and Workshop on Contextu- alized Knowledge Graphs at ISWC 2018. She has organized Ladies that FOSS (2016), an event to enable a more diverse open source development community, Wikidata meetings in London (2018) and is part of the committee of WikidataCon 2019.
Kemele M. Endris is a PhD candidate at University of Bonn and Research Associate at Scientific Data Management group in Joint Lab L3S Research Center and Leibniz Information Center For Science and Technology University Library (TIB), Hannover, Germany. His research research interest includes Data integration and management, Big Data, and knowledge graph construc- tion from heterogeneous data sources. He participated in the PC of DILS 2018, MEPDaW 2016, MEPDaW 2019, ESWC 2019, CSCUBS 2015-2019 and sub-reviewer of MEPDaW 2015, ESWC 2016, and SIGMOD/PODS 2017. He was a member of sponsoring committee of CSCUBS 2016.
Maria-Esther Vidal is the head of the Scientific Data Management group at the Leibniz Information Centre For Science and Technology University Library (TIB), Germany, and a full professor (on-leave) at Universidad Simón Bolívar (USB), Venezuela. Maria-Esther has been visiting professor at the University of Maryland, College Park, as well as in several universities in Europe, e.g., University of Nantes, Karlsruhe Institute of Technology (KIT), Universidad Politécnica de Madrid, and Universidad Politécnica de Catalunya. She has published more than 130 peer-reviewed papers in Semantic Web, Databases, Bioinformatics, and Artificial Intelligence. She has also co- authored one monograph, and co-edited books and journal special issues. She is part of various editorial boards (e.g., JWS, JDIQ), and has been general chair, co-chair, senior member, and reviewer of several scientific events and journals (e.g., ESWC, AAAI, AMW, WWW, TheWebConf, KDE). Her interests include Big data, knowledge management and discovery, semantic web, and question answering over heterogeneous sources.
