1,424 research outputs found
Mining Meaning from Wikipedia
Wikipedia is a goldmine of information; not just for its many readers, but
also for the growing community of researchers who recognize it as a resource of
exceptional scale and utility. It represents a vast investment of manual effort
and judgment: a huge, constantly evolving tapestry of concepts and relations
that is being applied to a host of tasks.
This article provides a comprehensive description of this work. It focuses on
research that extracts and makes use of the concepts, relations, facts and
descriptions found in Wikipedia, and organizes the work into four broad
categories: applying Wikipedia to natural language processing; using it to
facilitate information retrieval and information extraction; and as a resource
for ontology building. The article addresses how Wikipedia is being used as is,
how it is being improved and adapted, and how it is being combined with other
structures to create entirely new resources. We identify the research groups
and individuals involved, and how their work has developed in the last few
years. We provide a comprehensive list of the open-source software they have
produced.Comment: An extensive survey of re-using information in Wikipedia in natural
language processing, information retrieval and extraction and ontology
building. Accepted for publication in International Journal of Human-Computer
Studie
Towards a Universal Wordnet by Learning from Combined Evidenc
Lexical databases are invaluable sources of knowledge about words and their meanings, with numerous applications in areas like NLP, IR, and AI. We propose a methodology for the automatic construction of a large-scale multilingual lexical database where words of many languages are hierarchically organized in terms of their meanings and their semantic relations to other words. This resource is bootstrapped from WordNet, a well-known English-language resource. Our approach extends WordNet with around 1.5 million meaning links for 800,000 words in over 200 languages, drawing on evidence extracted from a variety of resources including existing (monolingual) wordnets, (mostly bilingual) translation dictionaries, and parallel corpora. Graph-based scoring functions and statistical learning techniques are used to iteratively integrate this information and build an output graph. Experiments show that this wordnet has a high level of precision and coverage, and that it can be useful in applied tasks such as cross-lingual text classification
Challenges to knowledge representation in multilingual contexts
To meet the increasing demands of the complex inter-organizational processes and the demand for
continuous innovation and internationalization, it is evident that new forms of organisation are
being adopted, fostering more intensive collaboration processes and sharing of resources, in what
can be called collaborative networks (Camarinha-Matos, 2006:03). Information and knowledge are
crucial resources in collaborative networks, being their management fundamental processes to
optimize.
Knowledge organisation and collaboration systems are thus important instruments for the success of
collaborative networks of organisations having been researched in the last decade in the areas of
computer science, information science, management sciences, terminology and linguistics.
Nevertheless, research in this area didn’t give much attention to multilingual contexts of
collaboration, which pose specific and challenging problems. It is then clear that access to and
representation of knowledge will happen more and more on a multilingual setting which implies the
overcoming of difficulties inherent to the presence of multiple languages, through the use of
processes like localization of ontologies.
Although localization, like other processes that involve multilingualism, is a rather well-developed
practice and its methodologies and tools fruitfully employed by the language industry in the
development and adaptation of multilingual content, it has not yet been sufficiently explored as an
element of support to the development of knowledge representations - in particular ontologies -
expressed in more than one language. Multilingual knowledge representation is then an open
research area calling for cross-contributions from knowledge engineering, terminology, ontology
engineering, cognitive sciences, computational linguistics, natural language processing, and
management sciences.
This workshop joined researchers interested in multilingual knowledge representation, in a
multidisciplinary environment to debate the possibilities of cross-fertilization between knowledge
engineering, terminology, ontology engineering, cognitive sciences, computational linguistics,
natural language processing, and management sciences applied to contexts where multilingualism
continuously creates new and demanding challenges to current knowledge representation methods
and techniques.
In this workshop six papers dealing with different approaches to multilingual knowledge
representation are presented, most of them describing tools, approaches and results obtained in the
development of ongoing projects.
In the first case, Andrés Domínguez Burgos, Koen Kerremansa and Rita Temmerman present a
software module that is part of a workbench for terminological and ontological mining,
Termontospider, a wiki crawler that aims at optimally traverse Wikipedia in search of domainspecific
texts for extracting terminological and ontological information. The crawler is part of a tool
suite for automatically developing multilingual termontological databases, i.e. ontologicallyunderpinned
multilingual terminological databases. In this paper the authors describe the basic principles
behind the crawler and summarized the research setting in which the tool is currently tested.
In the second paper, Fumiko Kano presents a work comparing four feature-based similarity
measures derived from cognitive sciences. The purpose of the comparative analysis presented by the author is to verify the potentially most effective model that can be applied for mapping independent ontologies in a culturally influenced domain. For that, datasets based on standardized
pre-defined feature dimensions and values, which are obtainable from the UNESCO Institute for
Statistics (UIS) have been used for the comparative analysis of the similarity measures. The purpose
of the comparison is to verify the similarity measures based on the objectively developed datasets.
According to the author the results demonstrate that the Bayesian Model of Generalization provides
for the most effective cognitive model for identifying the most similar corresponding concepts
existing for a targeted socio-cultural community.
In another presentation, Thierry Declerck, Hans-Ulrich Krieger and Dagmar Gromann present an
ongoing work and propose an approach to automatic extraction of information from multilingual
financial Web resources, to provide candidate terms for building ontology elements or instances of
ontology concepts. The authors present a complementary approach to the direct
localization/translation of ontology labels, by acquiring terminologies through the access and
harvesting of multilingual Web presences of structured information providers in the field of finance,
leading to both the detection of candidate terms in various multilingual sources in the financial
domain that can be used not only as labels of ontology classes and properties but also for the
possible generation of (multilingual) domain ontologies themselves.
In the next paper, Manuel Silva, António Lucas Soares and Rute Costa claim that despite the
availability of tools, resources and techniques aimed at the construction of ontological artifacts,
developing a shared conceptualization of a given reality still raises questions about the principles
and methods that support the initial phases of conceptualization. These questions become, according
to the authors, more complex when the conceptualization occurs in a multilingual setting. To tackle
these issues the authors present a collaborative platform – conceptME - where terminological and
knowledge representation processes support domain experts throughout a conceptualization
framework, allowing the inclusion of multilingual data as a way to promote knowledge sharing and
enhance conceptualization and support a multilingual ontology specification.
In another presentation Frieda Steurs and Hendrik J. Kockaert present us TermWise, a large project
dealing with legal terminology and phraseology for the Belgian public services, i.e. the translation
office of the ministry of justice, a project which aims at developing an advanced tool including
expert knowledge in the algorithms that extract specialized language from textual data (legal
documents) and whose outcome is a knowledge database including Dutch/French equivalents for
legal concepts, enriched with the phraseology related to the terms under discussion.
Finally, Deborah Grbac, Luca Losito, Andrea Sada and Paolo Sirito report on the preliminary
results of a pilot project currently ongoing at UCSC Central Library, where they propose to adapt to
subject librarians, employed in large and multilingual Academic Institutions, the model used by
translators working within European Union Institutions. The authors are using User Experience
(UX) Analysis in order to provide subject librarians with a visual support, by means of “ontology
tables” depicting conceptual linking and connections of words with concepts presented according to
their semantic and linguistic meaning.
The organizers hope that the selection of papers presented here will be of interest to a broad audience, and will be a starting point for further discussion and cooperation
Proceedings of the First Workshop on Computing News Storylines (CNewsStory 2015)
This volume contains the proceedings of the 1st Workshop on Computing News Storylines (CNewsStory
2015) held in conjunction with the 53rd Annual Meeting of the Association for Computational
Linguistics and the 7th International Joint Conference on Natural Language Processing (ACL-IJCNLP
2015) at the China National Convention Center in Beijing, on July 31st 2015.
Narratives are at the heart of information sharing. Ever since people began to share their experiences,
they have connected them to form narratives. The study od storytelling and the field of literary theory
called narratology have developed complex frameworks and models related to various aspects of
narrative such as plots structures, narrative embeddings, characters’ perspectives, reader response, point
of view, narrative voice, narrative goals, and many others. These notions from narratology have been
applied mainly in Artificial Intelligence and to model formal semantic approaches to narratives (e.g.
Plot Units developed by Lehnert (1981)). In recent years, computational narratology has qualified as an
autonomous field of study and research. Narrative has been the focus of a number of workshops and
conferences (AAAI Symposia, Interactive Storytelling Conference (ICIDS), Computational Models of
Narrative). Furthermore, reference annotation schemes for narratives have been proposed (NarrativeML
by Mani (2013)).
The workshop aimed at bringing together researchers from different communities working on
representing and extracting narrative structures in news, a text genre which is highly used in NLP
but which has received little attention with respect to narrative structure, representation and analysis.
Currently, advances in NLP technology have made it feasible to look beyond scenario-driven, atomic
extraction of events from single documents and work towards extracting story structures from multiple
documents, while these documents are published over time as news streams. Policy makers, NGOs,
information specialists (such as journalists and librarians) and others are increasingly in need of tools
that support them in finding salient stories in large amounts of information to more effectively implement
policies, monitor actions of “big players” in the society and check facts. Their tasks often revolve around
reconstructing cases either with respect to specific entities (e.g. person or organizations) or events (e.g.
hurricane Katrina). Storylines represent explanatory schemas that enable us to make better selections
of relevant information but also projections to the future. They form a valuable potential for exploiting
news data in an innovative way.JRC.G.2-Global security and crisis managemen
Liage de données RDF : évaluation d'approches interlingues
The Semantic Web extends the Web by publishing structured and interlinked data using RDF.An RDF data set is a graph where resources are nodes labelled in natural languages. One of the key challenges of linked data is to be able to discover links across RDF data sets. Given two data sets, equivalent resources should be identified and linked by owl:sameAs links. This problem is particularly difficult when resources are described in different natural languages.This thesis investigates the effectiveness of linguistic resources for interlinking RDF data sets. For this purpose, we introduce a general framework in which each RDF resource is represented as a virtual document containing text information of neighboring nodes. The context of a resource are the labels of the neighboring nodes. Once virtual documents are created, they are projected in the same space in order to be compared. This can be achieved by using machine translation or multilingual lexical resources. Once documents are in the same space, similarity measures to find identical resources are applied. Similarity between elements of this space is taken for similarity between RDF resources.We performed evaluation of cross-lingual techniques within the proposed framework. We experimentally evaluate different methods for linking RDF data. In particular, two strategies are explored: applying machine translation or using references to multilingual resources. Overall, evaluation shows the effectiveness of cross-lingual string-based approaches for linking RDF resources expressed in different languages. The methods have been evaluated on resources in English, Chinese, French and German. The best performance (over 0.90 F-measure) was obtained by the machine translation approach. This shows that the similarity-based method can be successfully applied on RDF resources independently of their type (named entities or thesauri concepts). The best experimental results involving just a pair of languages demonstrated the usefulness of such techniques for interlinking RDF resources cross-lingually.Le Web des données étend le Web en publiant des données structurées et liées en RDF. Un jeu de données RDF est un graphe orienté où les ressources peuvent être des sommets étiquetées dans des langues naturelles. Un des principaux défis est de découvrir les liens entre jeux de données RDF. Étant donnés deux jeux de données, cela consiste à trouver les ressources équivalentes et les lier avec des liens owl:sameAs. Ce problème est particulièrement difficile lorsque les ressources sont décrites dans différentes langues naturelles.Cette thèse étudie l'efficacité des ressources linguistiques pour le liage des données exprimées dans différentes langues. Chaque ressource RDF est représentée comme un document virtuel contenant les informations textuelles des sommets voisins. Les étiquettes des sommets voisins constituent le contexte d'une ressource. Une fois que les documents sont créés, ils sont projetés dans un même espace afin d'être comparés. Ceci peut être réalisé à l'aide de la traduction automatique ou de ressources lexicales multilingues. Une fois que les documents sont dans le même espace, des mesures de similarité sont appliquées afin de trouver les ressources identiques. La similarité entre les documents est prise pour la similarité entre les ressources RDF.Nous évaluons expérimentalement différentes méthodes pour lier les données RDF. En particulier, deux stratégies sont explorées: l'application de la traduction automatique et l'usage des banques de données terminologiques et lexicales multilingues. Dans l'ensemble, l'évaluation montre l'efficacité de ce type d'approches. Les méthodes ont été évaluées sur les ressources en anglais, chinois, français, et allemand. Les meilleurs résultats (F-mesure > 0.90) ont été obtenus par la traduction automatique. L'évaluation montre que la méthode basée sur la similarité peut être appliquée avec succès sur les ressources RDF indépendamment de leur type (entités nommées ou concepts de dictionnaires)
Applying Wikipedia to Interactive Information Retrieval
There are many opportunities to improve the interactivity of information retrieval systems beyond the ubiquitous search box. One idea is to use knowledge bases—e.g. controlled vocabularies, classification schemes, thesauri and ontologies—to organize, describe and navigate the information space. These resources are popular in libraries and specialist collections, but have proven too expensive and narrow to be applied to everyday webscale search. Wikipedia has the potential to bring structured knowledge into more widespread use. This online, collaboratively generated encyclopaedia is one of the largest and most consulted reference works in existence. It is broader, deeper and more agile than the knowledge bases put forward to assist retrieval in the past. Rendering this resource machine-readable is a challenging task that has captured the interest of many researchers. Many see it as a key step required to break the knowledge acquisition bottleneck that crippled previous efforts. This thesis claims that the roadblock can be sidestepped: Wikipedia can be applied effectively to open-domain information retrieval with minimal natural language processing or information extraction. The key is to focus on gathering and applying human-readable rather than machine-readable knowledge. To demonstrate this claim, the thesis tackles three separate problems: extracting knowledge from Wikipedia; connecting it to textual documents; and applying it to the retrieval process. First, we demonstrate that a large thesaurus-like structure can be obtained directly from Wikipedia, and that accurate measures of semantic relatedness can be efficiently mined from it. Second, we show that Wikipedia provides the necessary features and training data for existing data mining techniques to accurately detect and disambiguate topics when they are mentioned in plain text. Third, we provide two systems and user studies that demonstrate the utility of the Wikipedia-derived knowledge base for interactive information retrieval
Entity-Oriented Search
This open access book covers all facets of entity-oriented search—where “search” can be interpreted in the broadest sense of information access—from a unified point of view, and provides a coherent and comprehensive overview of the state of the art. It represents the first synthesis of research in this broad and rapidly developing area. Selected topics are discussed in-depth, the goal being to establish fundamental techniques and methods as a basis for future research and development. Additional topics are treated at a survey level only, containing numerous pointers to the relevant literature. A roadmap for future research, based on open issues and challenges identified along the way, rounds out the book. The book is divided into three main parts, sandwiched between introductory and concluding chapters. The first two chapters introduce readers to the basic concepts, provide an overview of entity-oriented search tasks, and present the various types and sources of data that will be used throughout the book. Part I deals with the core task of entity ranking: given a textual query, possibly enriched with additional elements or structural hints, return a ranked list of entities. This core task is examined in a number of different variants, using both structured and unstructured data collections, and numerous query formulations. In turn, Part II is devoted to the role of entities in bridging unstructured and structured data. Part III explores how entities can enable search engines to understand the concepts, meaning, and intent behind the query that the user enters into the search box, and how they can provide rich and focused responses (as opposed to merely a list of documents)—a process known as semantic search. The final chapter concludes the book by discussing the limitations of current approaches, and suggesting directions for future research. Researchers and graduate students are the primary target audience of this book. A general background in information retrieval is sufficient to follow the material, including an understanding of basic probability and statistics concepts as well as a basic knowledge of machine learning concepts and supervised learning algorithms
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