Diego Moussallem
Leipzig University
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Publication
Featured researches published by Diego Moussallem.
international conference on semantic systems | 2015
Diego Esteves; Diego Moussallem; Ciro Baron Neto; Tommaso Soru; Markus Ackermann; Jens Lehmann
Over the last decades many machine learning experiments have been published, giving benefit to the scientific progress. In order to compare machine-learning experiment results with each other and collaborate positively, they need to be performed thoroughly on the same computing environment, using the same sample datasets and algorithm configurations. Besides this, practical experience shows that scientists and engineers tend to have large output data in their experiments, which is both difficult to analyze and archive properly without provenance metadata. However, the Linked Data community still misses a lightweight specification for interchanging machine-learning metadata over different architectures to achieve a higher level of interoperability. In this paper, we address this gap by presenting a novel vocabulary dubbed MEX. We show that MEX provides a prompt method to describe experiments with a special focus on data provenance and fulfills the requirements for a long-term maintenance.
international conference on knowledge capture | 2017
Diego Moussallem; Ricardo Usbeck; Michael Röeder; Axel-Cyrille Ngonga Ngomo
Entity linking has recently been the subject of a significant body of research. Currently, the best performing approaches rely on trained mono-lingual models. Porting these approaches to other languages is consequently a difficult endeavor as it requires corresponding training data and retraining of the models. We address this drawback by presenting a novel multilingual, knowledge-base agnostic and deterministic approach to entity linking, dubbed MAG. MAG is based on a combination of context-based retrieval on structured knowledge bases and graph algorithms. We evaluate MAG on 23 data sets and in 7 languages. Our results show that the best approach trained on English datasets (PBOH) achieves a micro F-measure that is up to 4 times worse on datasets in other languages. MAG on the other hand achieves state-of-the-art performance on English datasets and reaches a micro F-measure that is up to 0.6 higher than that of PBOH on non-English languages.
international conference on semantic systems | 2016
Edgard Marx; Amrapali Zaveri; Diego Moussallem; Sandro Rautenberg
Many ranking methods have been proposed for RDF data. These methods often use the structure behind the data to measure its importance. Recently, some of these methods have started to explore information from other sources such as the Wikipedia page graph for better ranking RDF data. In this work, we propose DBtrends, a ranking function based on query logs. We extensively evaluate the application of different ranking functions for entities, classes, and properties across two different countries as well as their combination. Thereafter, we propose MIXED-RANK, a ranking function that combines DBtrends with the best-evaluated entity ranking function. We show that: (i) MIXED-RANK outperforms state-of-the-art entity ranking functions, and; (ii) query logs can be used to improve RDF ranking functions.
Proceedings of the International Conference on Web Intelligence | 2017
Timofey Ermilov; Diego Moussallem; Ricardo Usbeck; Axel-Cyrille Ngonga Ngomo
The availability of billions of facts represented in RDF on the Web provides novel opportunities for data discovery and access. In particular, keyword search and question answering approaches enable even lay people to access this data. However, the interpretation of the results of these systems, as well as the navigation through these results, remains challenging. In this paper, we present Genesis, a generic RDF data access interface. Genesis can be deployed on top of any knowledge base and search engine with minimal effort and allows for the representation of RDF data in a layperson-friendly way. This is facilitated by the modular architecture for reusable components underlying our framework. Currently, these include a generic search back-end, together with corresponding interactive user interface components based on a service for similar and related entities as well as verbalization services to bridge between RDF and natural language.
arXiv: Computation and Language | 2015
Diego Moussallem; Ricardo Choren
This paper introduces a novel approach to tackle the existing gap on message translations in dialogue systems. Currently, submitted messages to the dialogue systems are considered as isolated sentences. Thus, missing context information impede the disambiguation of homographs words in ambiguous sentences. Our approach solves this disambiguation problem by using concepts over existing ontologies.
european semantic web conference | 2018
Diego Moussallem; Ricardo Usbeck; Michael Röder; Axel-Cyrille Ngonga Ngomo
A plethora of Entity Linking (EL) approaches has recently been developed. While many claim to be multilingual, the MAG (Multilingual AGDISTIS) approach has been shown recently to outperform the state of the art in multilingual EL on 7 languages. With this demo, we extend MAG to support EL in 40 different languages, including especially low-resources languages such as Ukrainian, Greek, Hungarian, Croatian, Portuguese, Japanese and Korean. Our demo relies on online web services which allow for an easy access to our entity linking approaches and can disambiguate against DBpedia and Wikidata. During the demo, we will show how to use MAG by means of POST requests as well as using its user-friendly web interface. All data used in the demo is available at https://hobbitdata.informatik.uni-leipzig.de/agdistis/
Journal of Web Semantics | 2018
Diego Moussallem; Matthias Wauer; Axel-Cyrille Ngonga Ngomo
A large number of machine translation approaches have recently been developed to facilitate the fluid migration of content across languages. However, the literature suggests that many obstacles must still be dealt with to achieve better automatic translations. One of these obstacles is lexical and syntactic ambiguity. A promising way of overcoming this problem is using Semantic Web technologies. This article presents the results of a systematic review of machine translation approaches that rely on Semantic Web technologies for translating texts. Overall, our survey suggests that while Semantic Web technologies can enhance the quality of machine translation outputs for various problems, the combination of both is still in its infancy.
Proceedings of the International Conference on Web Intelligence | 2017
Diego Esteves; Diego Moussallem; Tommaso Soru; Ciro Baron Neto; Jens Lehmann; Axel-Cyrille Ngonga Ngomo; Julio Cesar Duarte
A choice of the best computational solution for a particular task is increasingly reliant on experimentation. Even though experiments are often described through text, tables, and figures, their descriptions are often incomplete or confusing. Thus, researchers often have to perform lengthy web searches for reproducing and understanding the results. In order to minimize this gap, vocabularies and ontologies have been proposed for representing data mining and machine learning (ML) experiments. However, we still lack proper tools to export properly these metadata. To this end, we present an open-source library dubbed LOG4MEX which aims at supporting the scientific community to fulfill this gap.
international conference on semantic systems | 2016
Diego Esteves; Pablo N. Mendes; Diego Moussallem; Julio Cesar Duarte; Amrapali Zaveri; Jens Lehmann
SEMANTiCS (Posters, Demos, SuCCESS) | 2016
Ciro Baron Neto; Diego Esteves; Tommaso Soru; Diego Moussallem; André Valdestilhas; Edgard Marx