DDIExtractor: A Web-based Java Tool for Extracting Drug-Drug Interactions from Biomedical Texts

Proceeding of: 16th International Conference on Applications of Natural Language to Information Systems, NLDB 201. Took place 2011, June 28-30, in Alicante, Spain. The event Web site is http://gplsi.dlsi.ua.es/congresos/nldb11/A drug-drug interaction (DDIs) occurs when one drug influences the level or activity of another drug. The detection of DDIs is an important research area in patient safety since these interactions can become very dangerous and increase health care costs. Although there are several databases and web tools providing information on DDIs to patients and health-care professionals, these resources are not comprehensive because many DDIs are only reported in the biomedical literature. This paper presents the first tool for detecting drug-drug interactions from biomedical texts called DDIExtractor. The tool allows users to search by keywords in the Medline 2010 baseline database and then detect drugs and DDIs in any retrieved document.This work is supported by the projects MA2VICMR (S2009/TIC-1542) and MULTIMEDICA (TIN2010-20644-C03-01).Publicad

Arabic Cooperative Answer Generation via Wikipedia Article Infoboxes

[EN] The typical question-answering system is facing many challenges related to the processing of questions and information resources in the extraction and generation of adequate answers. These challenges increase when the requested answer is cooperative and its language is Arabic. In this paper, we propose an original approach to generate cooperative answers for user-definitional questions designed to be integrated in a question-answering system. This approach is mainly based on the exploitation of the semi-structured Web knowledge which consists in using features derived from Wikipedia article infoboxes to generate cooperative answers. It is globally independent of a particular language, which gives it the ability to be integrated in any definitional question-answering system. We have chosen to integrate and experiment it in a definitional question-answering system dealing with the Arabic language entitled DefArabicQA. The results showed that this system has a significant impact on the approach efficiency regarding the improvement of the quality of the answer.The work of the third author was partially funded by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO) under the SomEMBED research project (TIN2015-71147-C2-1-P) and by the Generalitat Valenciana under the grant ALMAMATER (PrometeoII/2014/030).Trigui, O.; Belguith, L.; Rosso, P. (2017). Arabic Cooperative Answer Generation via Wikipedia Article Infoboxes. Research in Computing Science. 132:129-153. http://hdl.handle.net/10251/103731S12915313

Survey on Challenges of Question Answering in the Semantic Web

Höffner K, Walter S, Marx E, Usbeck R, Lehmann J, Ngomo A-CN. Survey on Challenges of Question Answering in the Semantic Web. Semantic Web Journal. 2017;8(6):895-920

Web 2.0, language resources and standards to automatically build a multilingual named entity lexicon

This paper proposes to advance in the current state-of-the-art of automatic Language Resource (LR) building by taking into consideration three elements: (i) the knowledge available in existing LRs, (ii) the vast amount of information available from the collaborative paradigm that has emerged from the Web 2.0 and (iii) the use of standards to improve interoperability. We present a case study in which a set of LRs for different languages (WordNet for English and Spanish and Parole-Simple-Clips for Italian) are extended with Named Entities (NE) by exploiting Wikipedia and the aforementioned LRs. The practical result is a multilingual NE lexicon connected to these LRs and to two ontologies: SUMO and SIMPLE. Furthermore, the paper addresses an important problem which affects the Computational Linguistics area in the present, interoperability, by making use of the ISO LMF standard to encode this lexicon. The different steps of the procedure (mapping, disambiguation, extraction, NE identification and postprocessing) are comprehensively explained and evaluated. The resulting resource contains 974,567, 137,583 and 125,806 NEs for English, Spanish and Italian respectively. Finally, in order to check the usefulness of the constructed resource, we apply it into a state-of-the-art Question Answering system and evaluate its impact; the NE lexicon improves the system’s accuracy by 28.1%. Compared to previous approaches to build NE repositories, the current proposal represents a step forward in terms of automation, language independence, amount of NEs acquired and richness of the information represented

HAWK - Hybrid Question Answering over Linked Data

Usbeck R, Ngonga Ngomo A-C, Bühmann L, Unger C. HAWK - Hybrid Question Answering over Linked Data. In: 12th Extended Semantic Web Conference, Portoroz, Slovenia, 31st May - 4th June 2015. 2015

Main Concepts, State of the Art and Future Research Questions in Sentiment Analysis.

This article has multiple objectives. First of all, the fundamental concepts and challenges of the research field known as Sentiment Analysis (SA) are presented. Secondly, a summary of a chronological account of the research performed in SA is provided as well as some bibliometric indicators that shed some light on the most frequently used techniques for addressing the central aspects of SA. The geographical locations of where the research took place are also given. In closing, it is argued that there is no hard evidence that fuzzy sets or hybrid approaches encompassing unsupervised learning, fuzzy sets and a solid psychological background of emotions could not be at least as effective as supervised learning techniques

Ontology evolution: a process-centric survey

Ontology evolution aims at maintaining an ontology up to date with respect to changes in the domain that it models or novel requirements of information systems that it enables. The recent industrial adoption of Semantic Web techniques, which rely on ontologies, has led to the increased importance of the ontology evolution research. Typical approaches to ontology evolution are designed as multiple-stage processes combining techniques from a variety of fields (e.g., natural language processing and reasoning). However, the few existing surveys on this topic lack an in-depth analysis of the various stages of the ontology evolution process. This survey extends the literature by adopting a process-centric view of ontology evolution. Accordingly, we first provide an overall process model synthesized from an overview of the existing models in the literature. Then we survey the major approaches to each of the steps in this process and conclude on future challenges for techniques aiming to solve that particular stage

Entity-centric knowledge discovery for idiosyncratic domains

Technical and scientific knowledge is produced at an ever-accelerating pace, leading to increasing issues when trying to automatically organize or process it, e.g., when searching for relevant prior work. Knowledge can today be produced both in unstructured (plain text) and structured (metadata or linked data) forms. However, unstructured content is still themost dominant formused to represent scientific knowledge. In order to facilitate the extraction and discovery of relevant content, new automated and scalable methods for processing, structuring and organizing scientific knowledge are called for. In this context, a number of applications are emerging, ranging fromNamed Entity Recognition (NER) and Entity Linking tools for scientific papers to specific platforms leveraging information extraction techniques to organize scientific knowledge. In this thesis, we tackle the tasks of Entity Recognition, Disambiguation and Linking in idiosyncratic domains with an emphasis on scientific literature. Furthermore, we study the related task of co-reference resolution with a specific focus on named entities. We start by exploring Named Entity Recognition, a task that aims to identify the boundaries of named entities in textual contents. We propose a newmethod to generate candidate named entities based on n-gram collocation statistics and design several entity recognition features to further classify them. In addition, we show how the use of external knowledge bases (either domain-specific like DBLP or generic like DBPedia) can be leveraged to improve the effectiveness of NER for idiosyncratic domains. Subsequently, we move to Entity Disambiguation, which is typically performed after entity recognition in order to link an entity to a knowledge base. We propose novel semi-supervised methods for word disambiguation leveraging the structure of a community-based ontology of scientific concepts. Our approach exploits the graph structure that connects different terms and their definitions to automatically identify the correct sense that was originally picked by the authors of a scientific publication. We then turn to co-reference resolution, a task aiming at identifying entities that appear using various forms throughout the text. We propose an approach to type entities leveraging an inverted index built on top of a knowledge base, and to subsequently re-assign entities based on the semantic relatedness of the introduced types. Finally, we describe an application which goal is to help researchers discover and manage scientific publications. We focus on the problem of selecting relevant tags to organize collections of research papers in that context. We experimentally demonstrate that the use of a community-authored ontology together with information about the position of the concepts in the documents allows to significantly increase the precision of tag selection over standard methods