Group items tagged

Collective Knowledge Systems: Where the Social Web meets the Semantic Web

What can happen if we combine the best ideas from the Social Web and Semantic Web?

The Vision of Collective Intelligence

Despite these individual contributions (which we will revisit in more detail in Section 2), to fully understand tagging systems we believe a holistic approach is necessary. Walker [24] describes tagging as “feral hypertext”, a structure out of control, where the same tag is assigned to different resources with different semantic senses, and thus associates otherwise unrelated resources. However, by considering the entire model, computer systems could make inferences that “domesticate” (to use Walker’s terms) these “feral” tags. For example, tag semantics and synonyms could potentially be inferred by analyzing the structure of the social network, and identifying certain portions of the network that use certain tags for the same resource, or related resources, interchangeably. These tags may be synonymous

Different designs and user incentives can have a major influence on the usefulness of information for various purposes and applications, and in a reciprocal fashion, on how users appropriate and utilize these systems. The design of the system may solicit tagging useful for discovery, retrieval, remembrance, social interaction, or possibly, all of the above

Other likely explanations for the observed correlation between social connection and common tag usage may be found in the descriptive categories of sociolinguistics which studies how different geographic and social formations structure the coherence and diffusion of semantic and syntactic structures in various ”lects” within a larger sociolinguistic system. Some of these example lects include: dialect (a lect used by a geographicallydefined community); sociolect (a lect used by a socially defined community); ethnolect (a lect spoken by a particular ethnic group); ecolect (a lect spoken within a household or family); and idiolect (a lect particular to a certain person). If we conceptualize social tagging systems within the theoretical frame of sociolinguistics, these and other “lects” seem especially applicable to understanding and classifying the apparent isomorphism between social and linguistic structures we observed in Flickr. The structures, changes, and diffusion within and amongst various “lects” in social tagging systems will likely have similar patterns to those found in social network analyses and in sociolinguistic language maps.

I want to convince you that many of the ways we're attempting to apply categorization to the electronic world are actually a bad fit, because we've adopted habits of mind that are left over from earlier strategies.

Social Media Content Ideas: A Perpetual Idea System

Apple says it can measure the distance to surrounding objects up to 5 meters away and operates "at the photon level at nano-second speeds."

Among Apple's existing features, LiDAR will have the biggest impact on augmented reality (AR) and Apple's own ARKit framework. Apple says the new LiDAR Scanner will allow for instant object placement, indicating that users wouldn't need to "scan" their environment before an AR app loads.

Apple also published a research paper in 2017 detailing LiDAR-based 3D object recognition systems for self-driving cars. Essentially, the system leverages the depth mapping of LiDAR and combines it with neural networks to vastly improve the ability of a self-driving car to "see" its environment and potential hazards.

Les folksonomies peuvent constituer une alternative aux moteurs de recherche en permettant la construction de parcours et la mise en réseau d'informations mais aussi de personnes.

Pour beaucoup d’usagers, la recherche d’information est devenue synonyme de moteur de recherche voire de  « googlisation ». Cependant il existe désormais des alternatives à ce quasi monopole via notamment les folksonomies, mot composé par Thomas Vander Wal à partir de folk et de taxonomy et qui définit la possibilité offerte à l’usager d’ajouter des mots-clés à des ressources. Leurs stratégies de recherche diffèrent de la traditionnelle médiation des moteurs. Nous nous sommes interrogés sur la pertinence des folksonomies et leur intérêt réel dans le cas de la recherche d’information. En effet, la démarche « folksonomique » diffère de la simple requête et suppose d’autres habiletés. Cette analyse s’inscrit dans nos recherches sur les habiletés informationnelles ( information literacy ) Nos travaux sur ces nouveaux modes de partage et recherche d’informations s’appuient sur de nombreux tests des différentes plateformes permettant l’intégration de mots-clés qualifiés de tags. Nous nous sommes tout particulièrement ici appuyés sur les systèmes de partages de signets parfois appelés « marque-pages sociaux », voire signets sociaux (social bookmarks). Nous avons pu d’ailleurs constater que les différents sites d’intégration de favoris ont veillé à l’interopérabilité de leur système le plus souvent en utilisant le format xml. Une possibilité qui n’était pas offerte sur tous les sites il y a encore un an

Faut-il pour autant voir dans ces systèmes un concurrent  potentiel des moteurs ? Nous songeons plutôt à les considérer comme des alternatives au sens de cheminements de recherche différents qui nécessitent une construction et non une logique de push

the majority of users still seem to prefer using public tools such as Facebook and LinkedIn than their corporate network. 

If you follow some of these tips however you can go some way to ensuring your internal network achieves the results you want.

4. Integrate with existing systems