480 research outputs found

    Topic modeling for entity linking using keyphrase

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    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

    A Survey of Location Prediction on Twitter

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    Locations, e.g., countries, states, cities, and point-of-interests, are central to news, emergency events, and people's daily lives. Automatic identification of locations associated with or mentioned in documents has been explored for decades. As one of the most popular online social network platforms, Twitter has attracted a large number of users who send millions of tweets on daily basis. Due to the world-wide coverage of its users and real-time freshness of tweets, location prediction on Twitter has gained significant attention in recent years. Research efforts are spent on dealing with new challenges and opportunities brought by the noisy, short, and context-rich nature of tweets. In this survey, we aim at offering an overall picture of location prediction on Twitter. Specifically, we concentrate on the prediction of user home locations, tweet locations, and mentioned locations. We first define the three tasks and review the evaluation metrics. By summarizing Twitter network, tweet content, and tweet context as potential inputs, we then structurally highlight how the problems depend on these inputs. Each dependency is illustrated by a comprehensive review of the corresponding strategies adopted in state-of-the-art approaches. In addition, we also briefly review two related problems, i.e., semantic location prediction and point-of-interest recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur

    Peirce, meaning and the semantic web

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    The so-called ‘Semantic Web’ is phase II of Tim Berners-Lee’s original vision for the WWW, whereby resources would no longer be indexed merely ‘syntactically’, via opaque character-strings, but via their meanings. We argue that one roadblock to Semantic Web development has been researchers’ adherence to a Cartesian, ‘private’ account of meaning, which has been dominant for the last 400 years, and which understands the meanings of signs as what their producers intend them to mean. It thus strives to build ‘silos of meaning’ which explicitly and antecedently determine what signs on the Web will mean in all possible situations. By contrast, the field is moving forward insofar as it embraces Peirce’s ‘public’, evolutionary account of meaning, according to which the meaning of signs just is the way they are interpreted and used to produce further signs. Given the extreme interconnectivity of the Web, it is argued that silos of meaning are unnecessary as plentiful machine-understandable data about the meaning of Web resources exists already in the form of those resources themselves, for applications that are able to leverage it, and it is Peirce’s account of meaning which can best make sense of the recent explosion in ‘user-defined content’ on the Web, and its relevance to achieving Semantic Web goals

    Live Social Semantics

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    Social interactions are one of the key factors to the success of conferences and similar community gatherings. This paper describes a novel application that integrates data from the semantic web, online social networks, and a real-world contact sensing platform. This application was successfully deployed at ESWC09, and actively used by 139 people. Personal profiles of the participants were automatically generated using several Web~2.0 systems and semantic academic data sources, and integrated in real-time with face-to-face contact networks derived from wearable sensors. Integration of all these heterogeneous data layers made it possible to offer various services to conference attendees to enhance their social experience such as visualisation of contact data, and a site to explore and connect with other participants. This paper describes the architecture of the application, the services we provided, and the results we achieved in this deployment

    Semantic Web gets into collaborative tagging

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    Collaborative tagging is a new content sharing and organization trend, mainly diffused over the Web, which has attracted growing attention during the last years. It refers to the process by which many users add metadata in the form of keywords to shared content. Today many different collaborative tagging systems are available on the Web, enabling users to add descriptive keywords to different types of Internet resources (web pages, photos, videos, etc.). The great number of advantages offered by the availability of collaboratively tagged resources in terms of their organization and shared information is underlined by their growing adoption, also in non-technical communities of users. In spite of this, analyzing the current structure and usage patterns of collaborative tagging systems, we can discover many important aspects which still need to be improved so as to bring tagging systems to their full potential. In particular, problems related to synonymy, polysemy, different lexical forms, different spellings and misspelling errors, but also the lack of accurancy caused by different levels of precision and distinct kinds of tag-to-resource association represent a great limit, causing inconsistencies among the terms used in the tagging process and thus reducing the efficiency of content search and the effectiveness of the tag space structuring and organization. This kind of problems is mainly caused by the lack of semantic information inclusion in the tagging process. Considering the increasing attention focused on the Semantic Web, we propose a new model of tagging system, based on semantic keywords. We let the users easily define the meaning of their tags, referencing some sort of social ontology. As social ontology we explore the adequacy of the support offered by the entries of Wikipedia andWordNet. Finally we present SemKey, a tool that allows users to tag in a semantic context, providing an evaluation of the system proposed in comparison with classical tagging tools
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