Automatic classification of web pages into bookmark categories

We describe a technique to automatically classify a web page into an existing bookmark category whenever a user decides to bookmark a page. HyperBK compares a bag-of-words representation of the page to descriptions of categories in the user’s bookmark file. Unlike default web browser dialogs in which the user may be presented with the category into which he or she saved the last bookmarked file, HyperBK also offers the category most similar to the page being bookmarked. The user can opt to save the page to the last category used; create a new category; or save the page elsewhere. In an evaluation, the user’s preferred category was offered on average 67% of the time.peer-reviewe

Comparing title only and full text indexing to classify web pages into bookmark categories

Web browser bookmark files are used to retain and organise records of web sites that the user would like to revisit. However, bookmark files tend to be under-utilised, as time and effort is needed to keep them organised. We use two methods to index and automatically classify documents referred to in 80 bookmark files, based on document title-only and full-text indexing, respectively. We evaluate the indexing methods by selecting a bookmark entry to classify from a bookmark file, and recreating the bookmark file so that it contains only entries created before the selected bookmark entry. Classification based on full-text indexing generally outperforms that based on document title only indexing. The ability to recommend the correct category at rank 1 using full-text indexing ranges from 20% to 41%, depending on the number of category members. However, combining the approaches results in a increase to 37% — 59%, but we would need to recommend up to two categories to users. By recommending up to 10 categories, this increases to 58% — 80%.peer-reviewe

Exploring The Value Of Folksonomies For Creating Semantic Metadata

Finding good keywords to describe resources is an on-going problem: typically we select such words manually from a thesaurus of terms, or they are created using automatic keyword extraction techniques. Folksonomies are an increasingly well populated source of unstructured tags describing web resources. This paper explores the value of the folksonomy tags as potential source of keyword metadata by examining the relationship between folksonomies, community produced annotations, and keywords extracted by machines. The experiment has been carried-out in two ways: subjectively, by asking two human indexers to evaluate the quality of the generated keywords from both systems; and automatically, by measuring the percentage of overlap between the folksonomy set and machine generated keywords set. The results of this experiment show that the folksonomy tags agree more closely with the human generated keywords than those automatically generated. The results also showed that the trained indexers preferred the semantics of folksonomy tags compared to keywords extracted automatically. These results can be considered as evidence for the strong relationship of folksonomies to the human indexer’s mindset, demonstrating that folksonomies used in the del.icio.us bookmarking service are a potential source for generating semantic metadata to annotate web resources

Tagging, Folksonomy & Co - Renaissance of Manual Indexing?

This paper gives an overview of current trends in manual indexing on the Web. Along with a general rise of user generated content there are more and more tagging systems that allow users to annotate digital resources with tags (keywords) and share their annotations with other users. Tagging is frequently seen in contrast to traditional knowledge organization systems or as something completely new. This paper shows that tagging should better be seen as a popular form of manual indexing on the Web. Difference between controlled and free indexing blurs with sufficient feedback mechanisms. A revised typology of tagging systems is presented that includes different user roles and knowledge organization systems with hierarchical relationships and vocabulary control. A detailed bibliography of current research in collaborative tagging is included.Comment: Preprint. 12 pages, 1 figure, 54 reference

User-Centered Navigation Re-Design for Web-Based Information Systems

Navigation design for web-based information systems (e.g. e-commerce sites, intranet solutions) that ignores user-participation reduces the system’s value and can even lead to system failure. In this paper we introduce a user-centered, explorative approach to re-designing navigation structures of web-based information systems, and describe how it can be implemented in order to provide flexibility and reduce maintenance costs. We conclude with lessons learned from the navigation redesign project at the Vienna University of Economics and Business Administration

Measuring vertex centrality in co-occurrence graphs for online social tag recommendation

Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) Proceedings of ECML PKDD (The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases) Discovery Challenge 2009, Bled, Slovenia, September 7, 2009.We present a social tag recommendation model for collaborative bookmarking systems. This model receives as input a bookmark of a web page or scientific publication, and automatically suggests a set of social tags useful for annotating the bookmarked document. Analysing and processing the bookmark textual contents - document title, URL, abstract and descriptions - we extract a set of keywords, forming a query that is launched against an index, and retrieves a number of similar tagged bookmarks. Afterwards, we take the social tags of these bookmarks, and build their global co-occurrence sub-graph. The tags (vertices) of this reduced graph that have the highest vertex centrality constitute our recommendations, whThis research was supported by the European Commission under contracts FP6-027122-SALERO, FP6-033715-MIAUCE and FP6-045032 SEMEDIA. The expressed content is the view of the authors but not necessarily the view of SALERO, MIAUCE and SEMEDIA projects as a whol