Lahiru Hinguruduwa, Edgard Marx, Tommaso Soru, and Thomas Riechert: In: 2021 IEEE 15th International Conference on Semantic Computing (ICSC) Multilingual knowledge bases are the key for crosslingual and multilingual applications such as Question Answering, Machine Translation, and Search. They facilitate information access and provide valuable training corpora for multi- and cross-lingual methods and frameworks. In this work, we evaluate the performance of different language identification methods and frameworks over DBpedia. We show that these approaches perform poorly on rdfs:labels. link

Edgard Marx, André Valdestilhas, Hannah Beck, and Tommaso Soru: In: The Semantic Web: ESWC 2021 Satellite Events (Editors: Ruben Verborgh, Anastasia Dimou, Aidan Hogan, Claudia d'Amato, Ilaria Tiddi, Arne Bröring, Simon Mayer, Femke Ongenae, Riccardo Tommasini, and Mehwish Alam) Natural language interfaces are one of the most powerful technologies to enable content access. It is a diverse and thriving topic that tackles a multitude of challenges ranging from designing better ranking models to user interfaces. Developing or adapting search engines is a very time-demanding and resource-consuming task. We present SANTé, a semantic search framework that facilitates publishing, querying, and browsing RDF data sets. We show the different interfaces implemented by SANTé through guided steps from raw RDF data to the search result using keyword queries. We demonstrate how SANTé can be used to publish and consume RDF data. link

Tommaso Soru Anand Panchbhai and Edgard Marx: In: Second Iberoamerican Conference and First Indo-American Conference (KGSWC 2020), Mérida, Mexico, November 26–27 A booming amount of information is continuously added to the Internet as structured and unstructured data, feeding knowledge bases such as DBpedia and Wikidata with billions of statements describing millions of entities. The aim of Question Answering systems is to allow lay users to access such data using natural language without needing to write formal queries. However, users often submit questions that are complex and require a certain level of abstraction and reasoning to decompose them into basic graph patterns. In this short paper, we explore the use of architectures based on Neural Machine Translation called Neural SPARQL Machines to learn pattern compositions. We show that sequence-to-sequence models are a viable and promising option to transform long utterances into complex SPARQL queries. link

Tommaso Soru, André Valdestilhas, Edgard Marx, and Axel-Cyrille Ngonga Ngomo: In: CoRR link

Muhammad Saleem, Alexander Potocki, Tommaso Soru, Olaf Hartig, and Axel-Cyrille Ngonga Ngomo: In: Semantics link

Ann-Kathrin Hartmann, Edgard Marx, and Tommaso Soru: In: Workshop on Linked Data Management, co-located with the W3C WEBBR 2018 The role of Question Answering is central to the fulfillmentof the Semantic Web. Recently, several approaches relying on artificial neural networks have been proposed to tackle the problem of question answering over knowledge graphs. Such techniques are however known to be data-hungry and the creation of training sets requires a substantial manual effort. We thus introduce DBNQA, a comprehensive dataset of 894,499 pairs of questions and SPARQL queries based on templates which are specifically designed on the DBpedia knowledge base. We show howthe method used to generate our dataset can be easily reused for other purposes. We report the successful adoption of DBNQA in an experimental phase and present how it compares with existing question-answering corpora. link

Gustavo Correa Publio, Diego Esteves, Agnieszka Lawrynowicz, Pance Panov, Larisa Soldatova, Tommaso Soru, Joaquin Vanschoren, and Hamid Zafar: In: Poster, selected for the 2nd Reproducibility in Machine Learning Workshop at ICML 2018, Stockholm, Sweden The ML-Schema, proposed by the W3C Machine Learning Schema Community Group, is a top-level ontology that provides a set of classes, properties, and restrictions for representing and interchanging information on machine learning algorithms, datasets, and experiments. It can be easily extended and specialized and it is also mapped to other more domain-specific ontologies developed in the area of machine learning and data mining. In this paper we overview existing state-of-the-art machine learning interchange formats and present the first release of ML-Schema, a canonical format resulted of more than seven years of experience among different research institutions. We argue that exposing semantics of machine learning algorithms, models, and experiments through a canonical format may pave the way to better interpretability and to realistically achieve the full interoperability of experiments regardless of platform or adopted workflow solution. link

Tommaso Soru, Edgard Marx, André Valdestilhas, Diego Esteves, Diego Moussallem, and Gustavo Publio: In: ICML Workshop on Neural Abstract Machines & Program Induction (NAMPI v2) link

Carolin Gümpel, Ronny Zingler, Edgard Marx, and Tommaso Soru: In: Workshop on Linked Data Management co-located with the W3C WEBBR 2018 The Semantic Web community has successfully deployed a significant number of RDF datasets on the Web. However, for ordinary users, to access information available on this datasets, it is still a cumbersome and time-demanding task that requires the knowledge of description frameworks and the use of formal languages. We introduce the Neural SPARQL Machine (NSpM) Telegram Chatbot, a chatbot built on top the open-source Telegram platform that facilitates the natural language querying of RDF datasets through the NSpM architecture. We show the different APIs implemented by the proposed chatbot as well as the processing steps from a natural language query to its corresponding answer. Additionally, we demonstrate how to instantiate as well as how to query other RDF datasets. link

Andre Valdestilhas, Tommaso Soru, Markus Nentwig, Edgard Marx, Muhammad Saleem, and Axel-Cyrille Ngonga Ngomo: In: European Semantic Web Conference link