Reasoning & Querying – State of the Art

Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF

WISER: A Semantic Approach for Expert Finding in Academia based on Entity Linking

We present WISER, a new semantic search engine for expert finding in academia. Our system is unsupervised and it jointly combines classical language modeling techniques, based on text evidences, with the Wikipedia Knowledge Graph, via entity linking. WISER indexes each academic author through a novel profiling technique which models her expertise with a small, labeled and weighted graph drawn from Wikipedia. Nodes in this graph are the Wikipedia entities mentioned in the author's publications, whereas the weighted edges express the semantic relatedness among these entities computed via textual and graph-based relatedness functions. Every node is also labeled with a relevance score which models the pertinence of the corresponding entity to author's expertise, and is computed by means of a proper random-walk calculation over that graph; and with a latent vector representation which is learned via entity and other kinds of structural embeddings derived from Wikipedia. At query time, experts are retrieved by combining classic document-centric approaches, which exploit the occurrences of query terms in the author's documents, with a novel set of profile-centric scoring strategies, which compute the semantic relatedness between the author's expertise and the query topic via the above graph-based profiles. The effectiveness of our system is established over a large-scale experimental test on a standard dataset for this task. We show that WISER achieves better performance than all the other competitors, thus proving the effectiveness of modelling author's profile via our "semantic" graph of entities. Finally, we comment on the use of WISER for indexing and profiling the whole research community within the University of Pisa, and its application to technology transfer in our University

EGovernance implementation model: Case study of the federal government agencies of Pakistan

Copyright @ 2013 EMCIS.This study examines the eGovernance potential to bring about structural changes in the way in which government agencies operate, interact and communicate internally and externally with its citizens. Public sector needs to adopt eGovernance that is focused on the citizen satisfaction. In many developed countries, the types and quality of public services provided by a Government to its citizens have evolved over time, due to their change in thinking regarding the role of Government, from being a traditional government to that of being a modern service provider. Thus, this study will focus on the complaint management information systems of the selected federal government agencies in Pakistan. Authors were able to derive the implementation model of eGovernance only after doing analysis of all of the data obtained from questionnaires, interviews and observations at the federal government level in Pakistan. Authors found that the overall outcome of the validation process indicated that the model is highly satisfactory to improve the overall eGovernance system to provide modern services to its citizens

Emergent Capabilities for Collaborative Teams in the Evolving Web Environment

This paper reports on our investigation of the latest advances for the Social Web, Web 2.0 and the Linked Data Web. These advances are discussed in terms of the latest capabilities that are available (or being made available) on the Web at the time of writing this paper. Such capabilities can be of significant benefit to teams, especially those comprised of multinational, geographically-dispersed team members. The specific context of coalition members in a rapidly formed diverse military context such as disaster relief or humanitarian aid is considered, where close working between non-government organisations and non-military teams will help to achieve results as quickly and efficiently as possible. The heterogeneity one finds in such teams, coupled with a lack of dedicated private network infrastructure, poses a number of challenges for collaboration, and the current paper represents an attempt to assess whether nascent Web-based capabilities can support such teams in terms of both their collaborative activities and their access to (and sharing of) information resources

From Personalization to Adaptivity: Creating Immersive Visits through Interactive Digital Storytelling at the Acropolis Museum

Storytelling has recently become a popular way to guide museum visitors, replacing traditional exhibit-centric descriptions by story-centric cohesive narrations with references to the exhibits and multimedia content. This work presents the fundamental elements of the CHESS project approach, the goal of which is to provide adaptive, personalized, interactive storytelling for museum visits. We shortly present the CHESS project and its background, we detail the proposed storytelling and user models, we describe the provided functionality and we outline the main tools and mechanisms employed. Finally, we present the preliminary results of a recent evaluation study that are informing several directions for future work

Stigmergic hyperlink's contributes to web search

Stigmergic hyperlinks are hyperlinks with a "heart beat": if used they stay healthy and online; if neglected, they fade, eventually getting replaced. Their life attribute is a relative usage measure that regular hyperlinks do not provide, hence PageRank-like measures have historically been well informed about the structure of webs of documents, but unaware of what users effectively do with the links. This paper elaborates on how to input the users’ perspective into Google’s original, structure centric, PageRank metric. The discussion then bridges to the Deep Web, some search challenges, and how stigmergic hyperlinks could help decentralize the search experience, facilitating user generated search solutions and supporting new related business models.info:eu-repo/semantics/publishedVersio

A Survey of Location Prediction on Twitter

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