Multiple terminologies : an obstacle to information retrieval

An issue currently at the forefront of digital library research is the prevalence of disparate terminologies and the associated limitations imposed on user searching. It is thought that semantic interoperability is achievable by improving the compatibility between terminologies and classification schemes, enabling users to search multiple resources simultaneously and improve retrieval effectiveness through the use of associated terms drawn from several schemes. This article considers the terminology issue before outlining various proposed methods of tackling it, with a particular focus on terminology mapping

A Semantic Collaboration Method Based on Uniform Knowledge Graph

The Semantic Internet of Things is the extension of the Internet of Things and the Semantic Web, which aims to build an interoperable collaborative system to solve the heterogeneous problems in the Internet of Things. However, the Semantic Internet of Things has the characteristics of both the Internet of Things and the Semantic Web environment, and the corresponding semantic data presents many new data features. In this study, we analyze the characteristics of semantic data and propose the concept of a uniform knowledge graph, allowing us to be applied to the environment of the Semantic Internet of Things better. Here, we design a semantic collaboration method based on a uniform knowledge graph. It can take the uniform knowledge graph as the form of knowledge organization and representation, and provide a useful data basis for semantic collaboration by constructing semantic links to complete semantic relation between different data sets, to achieve the semantic collaboration in the Semantic Internet of Things. Our experiments show that the proposed method can analyze and understand the semantics of user requirements better and provide more satisfactory outcomes

Topic modeling for entity linking using keyphrase

This paper proposes an Entity Linking system that applies a topic modeling ranking. We apply a novel approach in order to provide new relevant elements to the model. These elements are keyphrases related to the queries and gathered from a huge Wikipedia-based knowledge resourcePeer ReviewedPostprint (author’s final draft

Mejorando la Ciencia Abierta Usando Datos Abiertos Enlazados: Caso de Uso CONICET Digital

Los servicios de publicación científica están cambiando drásticamente, los investigadores demandan servicios de búsqueda inteligentes para descubrir y relacionar publicaciones científicas. Los editores deben incorporar información semántica para organizar mejor sus activos digitales y hacer que las publicaciones sean más visibles. En este documento, presentamos el trabajo en curso para publicar un subconjunto de publicaciones científicas de CONICET Digital como datos abiertos enlazados. El objetivo de este trabajo es mejorar la recuperación y la reutilización de datos a través de tecnologías de Web Semántica y Datos Enlazados en el dominio de las publicaciones científicas. Para lograr estos objetivos, se han tenido en cuenta los estándares de la Web Semántica y los esquemas RDF (Dublín Core, FOAF, VoID, etc.). El proceso de conversión y publicación se basa en las pautas metodológicas para publicar datos vinculados de gobierno. También describimos como estos datos se pueden vincular a otros conjuntos de datos como DBLP, Wikidata y DBPedia. Finalmente, mostramos algunos ejemplos de consultas que responden a preguntas que inicialmente no permite CONICET Digital.Scientific publication services are changing drastically, researchers demand intelligent search services to discover and relate scientific publications. Publishersneed to incorporate semantic information to better organize their digital assets and make publications more discoverable. In this paper, we present the on-going work to publish a subset of scientific publications of CONICET Digital as Linked Open Data. The objective of this work is to improve the recovery andreuse of data through Semantic Web technologies and Linked Data in the domain of scientific publications.To achieve these goals, Semantic Web standards and reference RDF schema?s have been taken into account (Dublin Core, FOAF, VoID, etc.). The conversion and publication process is guided by the methodological guidelines for publishing government linked data. We also outline how these data can be linked to other datasets DBLP, WIKIDATA and DBPEDIA on the web of data. Finally, we show some examples of queries that answer questions that initially CONICET Digital does not allowFil: Zárate, Marcos Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Centro para el Estudio de Sistemas Marinos; ArgentinaFil: Carlos Buckle. Universidad Nacional de la Patagonia "San Juan Bosco"; ArgentinaFil: Mazzanti, Renato. Universidad Nacional de la Patagonia "San Juan Bosco"; ArgentinaFil: Samec, Gustavo Daniel. Universidad Nacional de la Patagonia "San Juan Bosco"; Argentin

Semantic Interpretation of User Queries for Question Answering on Interlinked Data

The Web of Data contains a wealth of knowledge belonging to a large number of domains. Retrieving data from such precious interlinked knowledge bases is an issue. By taking the structure of data into account, it is expected that upcoming generation of search engines is approaching to question answering systems, which directly answer user questions. But developing a question answering over these interlinked data sources is still challenging because of two inherent characteristics: First, different datasets employ heterogeneous schemas and each one may only contain a part of the answer for a certain question. Second, constructing a federated formal query across different datasets requires exploiting links between these datasets on both the schema and instance levels. In this respect, several challenges such as resource disambiguation, vocabulary mismatch, inference, link traversal are raised. In this dissertation, we address these challenges in order to build a question answering system for Linked Data. We present our question answering system Sina, which transforms user-supplied queries (i.e. either natural language queries or keyword queries) into conjunctive SPARQL queries over a set of interlinked data sources. The contributions of this work are as follows: 1. A novel approach for determining the most suitable resources for a user-supplied query from different datasets (disambiguation approach). We employed a Hidden Markov Model, whose parameters were bootstrapped with different distribution functions. 2. A novel method for constructing federated formal queries using the disambiguated resources and leveraging the linking structure of the underlying datasets. This approach essentially relies on a combination of domain and range inference as well as a link traversal method for constructing a connected graph, which ultimately renders a corresponding SPARQL query. 3. Regarding the problem of vocabulary mismatch, our contribution is divided into two parts, First, we introduce a number of new query expansion features based on semantic and linguistic inferencing over Linked Data. We evaluate the effectiveness of each feature individually as well as their combinations, employing Support Vector Machines and Decision Trees. Second, we propose a novel method for automatic query expansion, which employs a Hidden Markov Model to obtain the optimal tuples of derived words. 4. We provide two benchmarks for two different tasks to the community of question answering systems. The first one is used for the task of question answering on interlinked datasets (i.e. federated queries over Linked Data). The second one is used for the vocabulary mismatch task. We evaluate the accuracy of our approach using measures like mean reciprocal rank, precision, recall, and F-measure on three interlinked life-science datasets as well as DBpedia. The results of our accuracy evaluation demonstrate the effectiveness of our approach. Moreover, we study the runtime of our approach in its sequential as well as parallel implementations and draw conclusions on the scalability of our approach on Linked Data

On the evaluation and improvement of arabic wordnet coverage and usability

The final publication is available at Springer via http://dx.doi.org/10.1007/s10579-013-9237-0[EN] Built on the basis of the methods developed for Princeton WordNet and EuroWordNet, Arabic WordNet (AWN) has been an interesting project which combines WordNet structure compliance with Arabic particularities. In this paper, some AWN shortcomings related to coverage and usability are addressed. The use of AWN in question/answering (Q/A) helped us to deeply evaluate the resource from an experience-based perspective. Accordingly, an enrichment of AWN was built by semi-automatically extending its content. Indeed, existing approaches and/or resources developed for other languages were adapted and used for AWN. The experiments conducted in Arabic Q/A have shown an improvement of both AWN coverage as well as usability. Concerning coverage, a great amount of named entities extracted from YAGO were connected with corresponding AWN synsets. Also, a significant number of new verbs and nouns (including Broken Plural forms) were added. In terms of usability, thanks to the use of AWN, the performance for the AWN-based Q/A application registered an overall improvement with respect to the following three measures: accuracy (+9.27 % improvement), mean reciprocal rank (+3.6 improvement) and number of answered questions (+12.79 % improvement).The work presented in Sect. 2.2 was done in the framework of the bilateral Spain-Morocco AECID-PCI C/026728/09 research project. The research of the two first authors is done in the framework of the PROGRAMME D'URGENCE project (grant no. 03/2010). The research of the third author is done in the framework of WIQEI IRSES project (grant no. 269180) within the FP 7 Marie Curie People, DIANA-APPLICATIONS-Finding Hidden Knowledge in Texts: Applications (TIN2012-38603-C02-01) research project and VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems. We would like to thank Manuel Montes-y-Gomez (INAOE-Puebla, Mexico) and Sandra Garcia-Blasco (Bitsnbrain, Spain) for their feedback on the work presented in Sect. 2.4. We would like finally to thank Violetta Cavalli-Sforza (Al Akhawayn University in Ifrane, Morocco) for having reviewed the linguistic level of the entire document.Abouenour, L.; Bouzoubaa, K.; Rosso, P. (2013). On the evaluation and improvement of arabic wordnet coverage and usability. Language Resources and Evaluation. 47(3):891-917. https://doi.org/10.1007/s10579-013-9237-0S891917473Abbès, R., Dichy, J., & Hassoun, M. (2004). The architecture of a standard Arabic lexical database: Some figures, ratios and categories from the DIINAR.1 source program. In Workshop on computational approaches to Arabic script-based languages, Coling 2004. Geneva, Switzerland.Abouenour, L., Bouzoubaa, K., & Rosso, P. (2009a). Structure-based evaluation of an Arabic semantic query expansion using the JIRS passage retrieval system. 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The devices, experimental scaffolds, and biomaterials ontology (DEB): a tool for mapping, annotation, and analysis of biomaterials' data

The size and complexity of the biomaterials literature makes systematic data analysis an excruciating manual task. A practical solution is creating databases and information resources. Implant design and biomaterials research can greatly benefit from an open database for systematic data retrieval. Ontologies are pivotal to knowledge base creation, serving to represent and organize domain knowledge. To name but two examples, GO, the gene ontology, and CheBI, Chemical Entities of Biological Interest ontology and their associated databases are central resources to their respective research communities. The creation of the devices, experimental scaffolds, and biomaterials ontology (DEB), an open resource for organizing information about biomaterials, their design, manufacture, and biological testing, is described. It is developed using text analysis for identifying ontology terms from a biomaterials gold standard corpus, systematically curated to represent the domain's lexicon. Topics covered are validated by members of the biomaterials research community. The ontology may be used for searching terms, performing annotations for machine learning applications, standardized meta-data indexing, and other cross-disciplinary data exploitation. The input of the biomaterials community to this effort to create data-driven open-access research tools is encouraged and welcomed.Preprin

Bridging the gap between social tagging and semantic annotation: E.D. the Entity Describer

Semantic annotation enables the development of efficient computational methods for analyzing and interacting with information, thus maximizing its value. With the already substantial and constantly expanding data generation capacity of the life sciences as well as the concomitant increase in the knowledge distributed in scientific articles, new ways to produce semantic annotations of this information are crucial. While automated techniques certainly facilitate the process, manual annotation remains the gold standard in most domains. In this manuscript, we describe a prototype mass-collaborative semantic annotation system that, by distributing the annotation workload across the broad community of biomedical researchers, may help to produce the volume of meaningful annotations needed by modern biomedical science. We present E.D., the Entity Describer, a mashup of the Connotea social tagging system, an index of semantic web-accessible controlled vocabularies, and a new public RDF database for storing social semantic annotations