355 research outputs found
Language technologies and the evolution of the semantic web
The availability of huge amounts of semantic markup on the Web promises to enable a quantum leap in the level of support available to Web users for locating, aggregating, sharing, interpreting and customizing information. While we cannot claim that a large scale Semantic Web already exists, a number of applications have been produced, which generate and exploit semantic markup, to provide advanced search and querying functionalities, and to allow the visualization and management of heterogeneous, distributed data. While these tools provide evidence of the feasibility and tremendous potential value of the enterprise, they all suffer from major limitations, to do primarily with the limited degree of scale and heterogeneity of the semantic data they use. Nevertheless, we argue that we are at a key point in the brief history of the Semantic Web and that the very latest demonstrators already give us a glimpse of what future applications will look like. In this paper, we describe the already visible effects of these changes by analyzing the evolution of Semantic Web tools from smart databases towards applications that harness collective intelligence. We also point out that language technology plays an important role in making this evolution sustainable and we highlight the need for improved support, especially in the area of large-scale linguistic resources
Teachers\u27 Confidence and Preparedness for Teaching Mobile Learners
Mobile devices, such as tablets, laptops, and Smart phones have changed the landscape of education requiring teachers to integrate technology in the classroom. The integration of mobile devices in the classroom is referred to as mobile learning, and requires teachers to be confident and prepared in their ability to teach mobile learners. This study was an attempt to explore and examine teachers’ confidence and preparedness in teaching mobile learners and close some of the gaps in the research. A quantitative method of investigation and analysis was used for this study to draw conclusions relative not only to teachers’ confidence and to preparedness, but to examine the possibility of any correlation between the two. Additionally, the study explored the differences in teachers’ confidence and preparedness based on whether or not a school provided mobile devices on a 1:1 student basis. In general, the results revealed high levels of teacher confidence, but no correlation between confidence and preparedness. The results also showed no significant differences in confidence and preparedness for teachers teaching in schools with mobile devices provided on a 1:1 student basis and those schools not providing mobile devices on a 1:1 student basis
A framework for community detection in heterogeneous multi-relational networks
There has been a surge of interest in community detection in homogeneous
single-relational networks which contain only one type of nodes and edges.
However, many real-world systems are naturally described as heterogeneous
multi-relational networks which contain multiple types of nodes and edges. In
this paper, we propose a new method for detecting communities in such networks.
Our method is based on optimizing the composite modularity, which is a new
modularity proposed for evaluating partitions of a heterogeneous
multi-relational network into communities. Our method is parameter-free,
scalable, and suitable for various networks with general structure. We
demonstrate that it outperforms the state-of-the-art techniques in detecting
pre-planted communities in synthetic networks. Applied to a real-world Digg
network, it successfully detects meaningful communities.Comment: 27 pages, 10 figure
Recommended from our members
What can be done with the Semantic Web? An overview of Watson-based applications
Thanks to the huge efforts deployed in the community for creating, building and generating semantic information for the Semantic Web, large amounts of machine processable knowledge are now openly available. Watson is an infrastructure component for the Semantic Web, a gateway that provides the necessary functions to support applications in using the Semantic Web. In this paper, we describe a number of applications relying on Watson, with the purpose of demonstrating what can be achieved with the Semantic Web nowadays and what sort of new, smart and useful features can be derived from the exploitation of this large, distributed and heterogeneous base of semantic information
Mining for Social Serendipity
A common social problem at an event in which people do not personally know all of the other participants is the natural tendency for cliques to form and for discussions to mainly happen between people who already know each other. This limits the possibility for people to make interesting new acquaintances and acts as a retarding force in the creation of new links in the social web. Encouraging users to socialize with people they don't know by revealing to them hidden surprising links could help to improve the diversity of interactions at an event. The goal of this paper is to propose a method for detecting "surprising" relationships between people attending an event. By "surprising" relationship we mean those relationships that are not known a priori, and that imply shared information not directly related with the local context of the event (location, interests, contacts) at which the meeting takes place. To demonstrate and test our concept we used the Flickr community. We focused on a community of users associated with a social event (a computer science conference) and represented in Flickr by means of a photo pool devoted to the event. We use Flickr metadata (tags) to mine for user similarity not related to the context of the event, as represented in the corresponding Flickr group. For example, we look for two group members who have been in the same highly specific place (identified by means of geo-tagged photos), but are not friends of each other and share no other common interests or, social neighborhood
Content Evaluation of Jawaharlal Nehru University and Banaras Hindu University Library Websites in India
The study evaluates the content of Jawaharlal Nehru University (JNU) and Banaras Hindu University (BHU) library websites using qualitative (11 checkpoints) and quantitative (170 checkpoints) evaluation. The qualitative parts covered 11 features which belong to the homepages of the websites, which helps as recording devices of the descriptive information, moreover, quantitative part of the checklists covered 170 dichotomous question affiliated to the different aspect of the features such as; multimedia, general information, services, resources, my library features, web2.0/library2.0 features, currency accuracy and relevance, organization and structure features, links and maintenance features, user-interface features, search features and informative feedback and support features. A quantitative 5-points rating scales was executed to provide a numerical rating for each feature and rank them on the bases of numerical facts. The study has shown that the library websites are lagging behind to take full advantage of advance web2.0 features. Findings show that the JNU library website is scored 128 out of 170 (75.29%), which ranked above average, whereas BHU library website has ranked average by scoring 74 out of 170 (43.52%) features. This research is one of the unique studies should help the website developers in both the Universities to improve the quality of library websites. The study attempts to show certain features in both the libraries that need enhancement to make them user-friendly and improve user engagement. The study can serve as a benchmark for other library websites for evaluating the progress of their websites. Moreover, it can also help in discovering the nature of library websites in the era of ICT
Semantic Web: Who is who in the field – A bibliometric analysis
The Semantic Web (SW) is one of the main efforts aiming to enhance human and machine interaction by representing data in an understandable way for machines to mediate data and services. It is a fast-moving and multidisciplinary field. This study conducts a thorough bibliometric analysis of the field by collecting data from Web of Science (WOS) and Scopus for the period of 1960-2009. It utilizes a total of 44,157 papers with 651,673 citations from Scopus, and 22,951 papers with 571,911 citations from WOS. Based on these papers and citations, it evaluates the research performance of the SW by identifying the most productive players, major scholarly communication media, highly cited authors, influential papers and emerging stars
RichTags: A Social Semantic Tagging System
Social tagging systems allow users associating arbitrary keywords (or tags, or labels) to resources they want to save for future recall. Such saved items are called posts or bookmarks and usually constitute shared information in social tagging systems (although access control mechanisms might be applied as well). This means that users of a social tagging system can save and share their bookmarks with each other. The term social stresses the fact that much of the usefulness of the system relies on the data the users submit and share with each other. As a member of this category of tools, RichTags aims to overcome some weaknesses of the conventional social tagging systems (folksonomies) by utilizing Semantic Web technologies. The defining characteristic of the system is that the tags constitute an ontology of meaningful concepts, which is collectively managed by the users of the system. Hence, the approach is called social semantic tagging. It overcomes the polysemy, the synonymy, and the basic level variation problems encountered in the conventional systems. As well, it offers higher precision and recall. Current realisation of semantic tagging basically concerns an effort to automatically derive semantics out of folksonomies without affecting the mechanism of tagging applied in them. In contrast, RichTags’s approach for semantic tagging is a social process relied on the collective intelligence of the users instead of automation methods. The later means that the users collectively expand the tag vocabulary throughout the tagging task, while consistency mechanisms are applied to keep the vocabulary consistent during this expansion. The basic factor that differentiates RichTags from existing proposals for the enhancement of tags with meaning is that the primary mechanism relies on human collective intelligence and not on automation methods. However, this does not mean that the proposed automation techniques could not be combined with RichTags; contrariwise they could be very useful to speed up the production of the initial set of semantic tags in the vocabulary. Finally, RichTags is not limited to enriching the tags with meaning as current efforts primarily aim to; instead it utilizes this semantic information to improve the tagging and the exploration tasks of tagging systems
Web3Recommend: Decentralised recommendations with trust and relevance
Web3Recommend is a decentralized Social Recommender System implementation
that enables Web3 Platforms on Android to generate recommendations that balance
trust and relevance. Generating recommendations in decentralized networks is a
non-trivial problem because these networks lack a global perspective due to the
absence of a central authority. Further, decentralized networks are prone to
Sybil Attacks in which a single malicious user can generate multiple fake or
Sybil identities. Web3Recommend relies on a novel graph-based content
recommendation design inspired by GraphJet, a recommendation system used in
Twitter enhanced with MeritRank, a decentralized reputation scheme that
provides Sybil-resistance to the system. By adding MeritRank's decay parameters
to the vanilla Social Recommender Systems' personalized SALSA graph algorithm,
we can provide theoretical guarantees against Sybil Attacks in the generated
recommendations. Similar to GraphJet, we focus on generating real-time
recommendations by only acting on recent interactions in the social network,
allowing us to cater temporally contextual recommendations while keeping a
tight bound on the memory usage in resource-constrained devices, allowing for a
seamless user experience. As a proof-of-concept, we integrate our system with
MusicDAO, an open-source Web3 music-sharing platform, to generate personalized,
real-time recommendations. Thus, we provide the first Sybil-resistant Social
Recommender System, allowing real-time recommendations beyond classic
user-based collaborative filtering. The system is also rigorously tested with
extensive unit and integration tests. Further, our experiments demonstrate the
trust-relevance balance of recommendations against multiple adversarial
strategies in a test network generated using data from real music platforms
- …