Web 3.0 and Crowdservicing

The World Wide Web (WWW) has undergone significant evolution in the past decade. The emerging web 3.0 is characterized by the vision of achieving a balanced integration of services provided by machines and human agents. This is also the logic of ‘crowdservicing’ which has led to the creation of platforms on which new applications and even enterprises can be created, and complex, web-scale problem solving endeavors undertaken by flexibly connecting billions of loosely coupled computational agents or web services as well as human, service provider agents. In this paper, we build on research and development in the growing area of crowdsourcing to develop the concept of crowdservicing. We also present a novel crowdservicing application prototype, OntoAssist, to facilitate ontology evolution as an illustration of the concept. OntoAssist integrates the computational features of an existing search engine with the human computation provided by the crowd of users to find desirable search results

Ontological Services Using Crowdsourcing

This paper develops a service for ontology evolution based on crowdsourcing. The approach is demonstrated using OntoAssist, a specially designed semantic search service that is capable of capturing and disambiguating user’s search intent as well as automatically enabling ontology evolution. Successful and consistent ontology evolution often requires large amount of input data to specify new terms or changes in relationships. These inputs typically come mainly from domain experts or ontology professionals, which makes it hard to keep up with the change of open, dynamic World Wide Web environment. By integrating OntoAssist with an existing search engine, we show that users’ search intent can be disambiguated and aggregated to help to evolve underlying ontology. The disambiguation feature helps the users to find desirable search results. OntoAssist has been implemented and tested by Turkers from Amazon Mechanical Turk in a live demonstration site. Promising results and analysis are reported

Improving on popularity as a proxy for generality when building tag hierarchies from Folksonomies 6th International Conference, SocInfo 2014

Building taxonomies for Web content manually is costly and time-consuming. An alternative is to allow users to create folksonomies: collective social classifications. However, folksonomies have inconsistent structures and their use for searching and browsing is limited. Approaches have been proposed for acquiring implicit hierarchical structures from folksonomies, but these approaches suffer from the “generality-popularity” problem, in that they assume that popularity is a proxy for generality (that high level taxonomic terms will occur more often than low level ones). In this paper we test this assumption, and propose an improved approach (based on the Heymann-Benz algorithm) for tackling this problem by direction checking relations against a corpus of text. Our results show that popularity works as a proxy for generality in at most 77% of cases, but that this can be improved to 81% using our approach. This improvement will translate to higher quality tag hierarchy structures