Can predicate lexicalizations help in named entity disambiguation?

Paulheim H, Unger C. Can predicate lexicalizations help in named entity disambiguation? In: NLP & DBpedia 2015 : Proceedings of the Third NLP & DBpedia Workshop (NLP & DBpedia 2015) co-located with the 14th International Semantic Web Conference 2015 (ISWC 2015) Bethlehem, Pennsylvania, USA, October 11, 2015. CEUR Workshop Proceedings. Vol 1581. 2016: 92-97

Speeding Up Multilingual Grammar Development by Exploiting Linked Data to Generate Pre-terminal Rules

Walter S, Unger C, Cimiano P. Speeding Up Multilingual Grammar Development by Exploiting Linked Data to Generate Pre-terminal Rules. In: Natural Language Processing and Information Systems. Lecture Notes in Computer Science. Vol 8455. Springer; 2014: 242-245

DBlexipedia: A nucleus for a multilingual lexical Semantic Web

Walter S, Unger C, Cimiano P. DBlexipedia: A nucleus for a multilingual lexical Semantic Web. In: Proceedings of 3th International Workshop on NLP and DBpedia, co-located with the 14th International Semantic Web Conference (ISWC 2015), October 11-15, USA. 2015.A huge amount of datasets on the Semantic Web are linked to a few datahubs, the most prominent of which is DBpedia. What makes the exploitation of DBpedia challenging for natural language-based ap- plications, however, is that such NLP applications require knowledge about how the ontology elements are verbalized in natural language. In order to provide such knowledge at the required scale and thereby lever- age the use of DBpedia in different applications, we construct a lexicon for the DBpedia 2014 ontology by means of existing automatic methods for lexicon induction. It contains 11,998 lexical entries for 574 different properties in three languages: English, German, and Spanish. Just like DBpedia provides a hub for Semantic Web datasets, this lexicon can pro- vide a hub for the lexical Semantic Web, an ecosystem in which ontology lexica are published, linked, and re-used across applications

ATOLL - A framework for the automatic induction of ontology lexica

Walter S, Unger C, Cimiano P. ATOLL - A framework for the automatic induction of ontology lexica. Data & Knowledge Engineering. 2014;94:148-162.There is a range of large knowledge bases, such as Freebase and DBpedia, as well as linked data sets available on the web, but they typically lack lexical information stating how the properties and classes they comprise are realized lexically. Often only one label is attached, if at all, thus lacking rich linguistic information, e.g. about morphological forms, syntactic arguments or possible lexical variants and paraphrases. While ontology lexicon models like lemon allow for defining such linguistic information with respect to a given ontology, the cost involved in creating and maintaining such lexica is substantial, requiring a high manual effort. Towards lowering this effort we present ATOLL, a framework for the automatic induction of ontology lexica, based both on existing labels and dependency paths extracted from a text corpus. We instantiate ATOLL\ with respect to DBpedia\ as dataset and Wikipedia as corresponding corpus, and evaluate it by comparing the automatically generated lexicon with a manually constructed one. Our results clearly corroborate that our approach shows a high potential to be applied in a semi-automatic fashion in which a lexicon engineer can validate, reject or refine the automatically generated lexical entries, thus having a clear potential to contributing to the reduction the overall cost of creating ontology lexica

Automatic Acquisition of Adjective Lexicalizations of Restriction Classes: a Machine Learning Approach

Walter S, Unger C, Cimiano P. Automatic Acquisition of Adjective Lexicalizations of Restriction Classes: a Machine Learning Approach. Journal on Data Semantics. 2016;6(3):113-123

Exposing predictive analytics through natural language

van Grondelle J, Unger C, Smit F. Exposing predictive analytics through natural language

Correlation of crystal violet biofilm test results of Staphylococcus aureus clinical isolates with Raman spectroscopic read-out

Biofilm-related infections occur quite frequently in hospital settings and require rapid diagnostic identification as they are recalcitrant to antibiotic therapy and make special treatment necessary. One of the standard microbiological in vitro tests is the crystal violet test. It indirectly determines the amount of biofilm by measuring the optical density (OD) of the crystal violet-stained biofilm matrix and cells. However, this test is quite time-consuming, as it requires bacterial cultivation up to several days. In this study, we correlate fast Raman spectroscopic read-out of clinical Staphylococcus aureus isolates from 47 patients with different disease background with their biofilm-forming characteristics. Included were low (OD  20) biofilm performers as determined by the crystal violet test. Raman spectroscopic analysis of the bacteria revealed most spectral differences between high and low biofilm performers in the fingerprint region between 750 and 1150 cm−1. Using partial least square regression (PLSR) analysis on the Raman spectra involving the three categories of biofilm formation, it was possible to obtain a slight linear correlation of the Raman spectra with the biofilm OD values. The PLSR loading coefficient highlighted spectral differences between high and low biofilm performers for Raman bands that represent nucleic acids, carbohydrates, and proteins. Our results point to a possible application of Raman spectroscopy as a fast prediction tool for biofilm formation of bacterial strains directly after isolation from the infected patient. This could help clinicians make timely and adapted therapeutic decision in future

Evaluating question answering over linked data

Lopez V, Unger C, Cimiano P, Motta E. Evaluating question answering over linked data. Web Semantics Science Services And Agents On The World Wide Web. 2013;21:3-13.The availability of large amounts of open, distributed, and structured semantic data on the web has no precedent in the history of computer science. In recent years, there have been important advances in semantic search and question answering over RDF data. In particular, natural language interfaces to online semantic data have the advantage that they can exploit the expressive power of Semantic Web data models and query languages, while at the same time hiding their complexity from the user. However, despite the increasing interest in this area, there are no evaluations so far that systematically evaluate this kind of systems, in contrast to traditional question answering and search interfaces to document spaces. To address this gap, we have set up a series of evaluation challenges for question answering over linked data. The main goal of the challenge was to get insight into the strengths, capabilities, and current shortcomings of question answering systems as interfaces to query linked data sources, as well as benchmarking how these interaction paradigms can deal with the fact that the amount of RDF data available on the web is very large and heterogeneous with respect to the vocabularies and schemas used. Here, we report on the results from the first and second of such evaluation campaigns. We also discuss how the second evaluation addressed some of the issues and limitations which arose from the first one, as well as the open issues to be addressed in future competitions. (C) 2013 Elsevier B.V. All rights reserved

Weasel: a machine learning based approach to entity linking combining different features

Tristram F, Walter S, Cimiano P, Unger C. Weasel: a machine learning based approach to entity linking combining different features. In: Proceedings of 3th International Workshop on NLP and DBpedia, co-located with the 14th International Semantic Web Conference (ISWC 2015), October 11-15, USA. 2015.The task of entity linking consists in disambiguating named entities occurring in textual data by linking them to an identifier in a knowledge base that represents the real-world entity they denote. We present Weasel, a novel approach that is based on a combination of different features that is trained using a Support Vector Machine. We compare our approach to state-of-the-art tools such as FOX and DBpedia spotlight, showing that it outperforms both on the AIDA/CoNLL dataset and provides comparable results on the KORE50 dataset