Collaborative tagging represents for the Web a potential way for organizing and sharing information
and for heightening the capabilities of existing search engines. However, because of the
lack of automatic methodologies for generating the tags and supporting the tagging activity,
many resources on the Web are deficient in tag information, and recommending opportune tags
is both a current open issue and an exciting challenge. This paper approaches the problem by
applying a combined set of techniques and tools (that uses tags, domain ontologies, keyphrase extraction
methods) thereby generating tags automatically. The proposed approach is implemented
in the PIRATES (Personalized Intelligent tag Recommender and Annotator TEStbed) framework,
a prototype system for personalized content retrieval, annotation, and classification. A case
study application is developed using a domain ontology for software engineering
Achieving a quality software system requires UML designers to have a good understanding of both design patterns and antipatterns. Unfortunately, UML models for real systems tend to be huge and hard to manage, especially for models automatically generated from source code. Thus, it would be advisable to have tools to automatically identify particular instances of patterns. So, a formal language to express them is needed. However, a textual formalization of such a language is barely usable by UML practitioners. In this paper we propose a visual notation obtained by adding to UML as few graphical elements as possible in order to express both patterns and antipatterns (with the needed formality). As such additions are really few and intuitive, we believe that This approach has low cognitive load so is easily usable by practitioners but is still rigorous enough for implementation. This notation will be exploited by a GUI front-end for a prototypical tool (that we have recently developed) which is able to discover (anti)patterns in models
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.