10,738 research outputs found

    Context and Keyword Extraction in Plain Text Using a Graph Representation

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    Document indexation is an essential task achieved by archivists or automatic indexing tools. To retrieve relevant documents to a query, keywords describing this document have to be carefully chosen. Archivists have to find out the right topic of a document before starting to extract the keywords. For an archivist indexing specialized documents, experience plays an important role. But indexing documents on different topics is much harder. This article proposes an innovative method for an indexing support system. This system takes as input an ontology and a plain text document and provides as output contextualized keywords of the document. The method has been evaluated by exploiting Wikipedia's category links as a termino-ontological resources

    From Word to Sense Embeddings: A Survey on Vector Representations of Meaning

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    Over the past years, distributed semantic representations have proved to be effective and flexible keepers of prior knowledge to be integrated into downstream applications. This survey focuses on the representation of meaning. We start from the theoretical background behind word vector space models and highlight one of their major limitations: the meaning conflation deficiency, which arises from representing a word with all its possible meanings as a single vector. Then, we explain how this deficiency can be addressed through a transition from the word level to the more fine-grained level of word senses (in its broader acceptation) as a method for modelling unambiguous lexical meaning. We present a comprehensive overview of the wide range of techniques in the two main branches of sense representation, i.e., unsupervised and knowledge-based. Finally, this survey covers the main evaluation procedures and applications for this type of representation, and provides an analysis of four of its important aspects: interpretability, sense granularity, adaptability to different domains and compositionality.Comment: 46 pages, 8 figures. Published in Journal of Artificial Intelligence Researc

    Exploiting the user interaction context for automatic task detection

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    Detecting the task a user is performing on her computer desktop is important for providing her with contextualized and personalized support. Some recent approaches propose to perform automatic user task detection by means of classifiers using captured user context data. In this paper we improve on that by using an ontology-based user interaction context model that can be automatically populated by (i) capturing simple user interaction events on the computer desktop and (ii) applying rule-based and information extraction mechanisms. We present evaluation results from a large user study we have carried out in a knowledge-intensive business environment, showing that our ontology-based approach provides new contextual features yielding good task detection performance. We also argue that good results can be achieved by training task classifiers `online' on user context data gathered in laboratory settings. Finally, we isolate a combination of contextual features that present a significantly better discriminative power than classical ones

    Hypotheses, evidence and relationships: The HypER approach for representing scientific knowledge claims

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    Biological knowledge is increasingly represented as a collection of (entity-relationship-entity) triplets. These are queried, mined, appended to papers, and published. However, this representation ignores the argumentation contained within a paper and the relationships between hypotheses, claims and evidence put forth in the article. In this paper, we propose an alternate view of the research article as a network of 'hypotheses and evidence'. Our knowledge representation focuses on scientific discourse as a rhetorical activity, which leads to a different direction in the development of tools and processes for modeling this discourse. We propose to extract knowledge from the article to allow the construction of a system where a specific scientific claim is connected, through trails of meaningful relationships, to experimental evidence. We discuss some current efforts and future plans in this area

    Ontology for Representing Human Needs

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    Need satisfaction plays a fundamental role in human well-being. Hence understanding citizens' needs is crucial for developing a successful social and economic policy. This notwithstanding, the concept of need has not yet found its place in information systems and online tools. Furthermore, assessing needs itself remains a labor-intensive, mostly offline activity, where only a limited support by computational tools is available. In this paper, we make the first step towards employing need management in the design of information systems supporting participation and participatory innovation by proposing OpeNeeD, a family of ontologies for representing human needs data. As a proof of concept, OpeNeeD has been used to represent, enrich and query the results of a needs assessment study in a local citizen community in one of the Vienna districts. The proposed ontology will facilitate such studies and enable the representation of citizens' needs as Linked Data, fostering its co-creation and incentivizing the use of Open Data and services based on it

    Peer - Mediated Distributed Knowledge Management

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    Distributed Knowledge Management is an approach to knowledge management based on the principle that the multiplicity (and heterogeneity) of perspectives within complex organizations is not be viewed as an obstacle to knowledge exploitation, but rather as an opportunity that can foster innovation and creativity. Despite a wide agreement on this principle, most current KM systems are based on the idea that all perspectival aspects of knowledge should be eliminated in favor of an objective and general representation of knowledge. In this paper we propose a peer-to-peer architecture (called KEx), which embodies the principle above in a quite straightforward way: (i) each peer (called a K-peer) provides all the services needed to create and organize "local" knowledge from an individual's or a group's perspective, and (ii) social structures and protocols of meaning negotiation are introduced to achieve semantic coordination among autonomous peers (e.g., when searching documents from other K-peers). A first version of the system, called KEx, is imple-mented as a knowledge exchange level on top of JXTA

    An evaluation of pedagogically informed parameterised questions for self assessment

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    Self-assessment is a crucial component of learning. Learners can learn by asking themselves questions and attempting to answer them. However, creating effective questions is time-consuming because it may require considerable resources and the skill of critical thinking. Questions need careful construction to accurately represent the intended learning outcome and the subject matter involved. There are very few systems currently available which generate questions automatically, and these are confined to specific domains. This paper presents a system for automatically generating questions from a competency framework, based on a sound pedagogical and technological approach. This makes it possible to guide learners in developing questions for themselves, and to provide authoring templates which speed the creation of new questions for self-assessment. This novel design and implementation involves an ontological database that represents the intended learning outcome to be assessed across a number of dimensions, including level of cognitive ability and subject matter. The system generates a list of all the questions that are possible from a given learning outcome, which may then be used to test for understanding, and so could determine the degree to which learners actually acquire the desired knowledge. The way in which the system has been designed and evaluated is discussed, along with its educational benefits
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