Addressing the Challenges of Semantic Citizen-Sensing

Preprin

Enhanced Trustworthy and High-Quality Information Retrieval System for Web Search Engines

The WWW is the most important source of information. But, there is no guarantee for information correctness and lots of conflicting information is retrieved by the search engines and the quality of provided information also varies from low quality to high quality. We provide enhanced trustworthiness in both specific (entity) and broad (content) queries in web searching. The filtering of trustworthiness is based on 5 factors – Provenance, Authority, Age, Popularity, and Related Links. The trustworthiness is calculated based on these 5 factors and it is stored thereby increasing the performance in retrieving trustworthy websites. The calculated trustworthiness is stored only for static websites. Quality is provided based on policies selected by the user. Quality based ranking of retrieved trusted information is provided using WIQA (Web Information Quality Assessment) Framework

Computing word-of-mouth trust relationships in social networks from Semantic Web and Web 2.0 data sources

Social networks can serve as both a rich source of new information and as a filter to identify the information most relevant to our specific needs. In this paper we present a methodology and algorithms that, by exploiting existing Semantic Web and Web2.0 data sources, help individuals identify who in their social network knows what, and who is the most trustworthy source of information on that topic. Our approach improves upon previous work in a number of ways, such as incorporating topic-specific rather than global trust metrics. This is achieved by generating topic experience profiles for each network member, based on data from Revyu and del.icio.us, to indicate who knows what. Identification of the most trustworthy sources is enabled by a rich trust model of information and recommendation seeking in social networks. Reviews and ratings created on Revyu provide source data for algorithms that generate topic expertise and person to person affinity metrics. Combining these metrics, we are implementing a user-oriented application for searching and automated ranking of information sources within social networks

Closing the loop: assisting archival appraisal and information retrieval in one sweep

In this article, we examine the similarities between the concept of appraisal, a process that takes place within the archives, and the concept of relevance judgement, a process fundamental to the evaluation of information retrieval systems. More specifically, we revisit selection criteria proposed as result of archival research, and work within the digital curation communities, and, compare them to relevance criteria as discussed within information retrieval's literature based discovery. We illustrate how closely these criteria relate to each other and discuss how understanding the relationships between the these disciplines could form a basis for proposing automated selection for archival processes and initiating multi-objective learning with respect to information retrieval

Representing Dataset Quality Metadata using Multi-Dimensional Views

Data quality is commonly defined as fitness for use. The problem of identifying quality of data is faced by many data consumers. Data publishers often do not have the means to identify quality problems in their data. To make the task for both stakeholders easier, we have developed the Dataset Quality Ontology (daQ). daQ is a core vocabulary for representing the results of quality benchmarking of a linked dataset. It represents quality metadata as multi-dimensional and statistical observations using the Data Cube vocabulary. Quality metadata are organised as a self-contained graph, which can, e.g., be embedded into linked open datasets. We discuss the design considerations, give examples for extending daQ by custom quality metrics, and present use cases such as analysing data versions, browsing datasets by quality, and link identification. We finally discuss how data cube visualisation tools enable data publishers and consumers to analyse better the quality of their data.Comment: Preprint of a paper submitted to the forthcoming SEMANTiCS 2014, 4-5 September 2014, Leipzig, German

Trusted Tiny Things : Making Devices in Smart Cities More Transparent

Postprin

Metadata enrichment for digital heritage: users as co-creators

This paper espouses the concept of metadata enrichment through an expert and user-focused approach to metadata creation and management. To this end, it is argued the Web 2.0 paradigm enables users to be proactive metadata creators. As Shirky (2008, p.47) argues Web 2.0’s social tools enable “action by loosely structured groups, operating without managerial direction and outside the profit motive”. Lagoze (2010, p. 37) advises, “the participatory nature of Web 2.0 should not be dismissed as just a popular phenomenon [or fad]”. Carletti (2016) proposes a participatory digital cultural heritage approach where Web 2.0 approaches such as crowdsourcing can be sued to enrich digital cultural objects. It is argued that “heritage crowdsourcing, community-centred projects or other forms of public participation”. On the other hand, the new collaborative approaches of Web 2.0 neither negate nor replace contemporary standards-based metadata approaches. Hence, this paper proposes a mixed metadata approach where user created metadata augments expert-created metadata and vice versa. The metadata creation process no longer remains to be the sole prerogative of the metadata expert. The Web 2.0 collaborative environment would now allow users to participate in both adding and re-using metadata. The case of expert-created (standards-based, top-down) and user-generated metadata (socially-constructed, bottom-up) approach to metadata are complementary rather than mutually-exclusive. The two approaches are often mistakenly considered as dichotomies, albeit incorrectly (Gruber, 2007; Wright, 2007) . This paper espouses the importance of enriching digital information objects with descriptions pertaining the about-ness of information objects. Such richness and diversity of description, it is argued, could chiefly be achieved by involving users in the metadata creation process. This paper presents the importance of the paradigm of metadata enriching and metadata filtering for the cultural heritage domain. Metadata enriching states that a priori metadata that is instantiated and granularly structured by metadata experts is continually enriched through socially-constructed (post-hoc) metadata, whereby users are pro-actively engaged in co-creating metadata. The principle also states that metadata that is enriched is also contextually and semantically linked and openly accessible. In addition, metadata filtering states that metadata resulting from implementing the principle of enriching should be displayed for users in line with their needs and convenience. In both enriching and filtering, users should be considered as prosumers, resulting in what is called collective metadata intelligence

Using Semantic Web Technologies to Support Enhanced Situation Awareness

The AKTiveSA project is using Semantic Web technologies to support information fusion and enhanced situational awareness in a simulated humanitarian relief scenario. We have developed an application that shows how situational awareness can be supported during humanitarian relief situations; often occurring alongside military conflict. Semantic Web technologies provide new opportunities for harvesting information from numerous, disparate and often heterogeneous information sources and can be used to better support complex knowledge fusion