Classification of Web page content is essential to many tasks in Web information retrieval such as maintaining Web directories and focused crawling. The uncontrolled nature of Web content presents additional challenges to Web page classification as compared to traditional text classification, but the interconnected nature of hypertext also provides features that can assist the process.As we review work in Web page classification, we note the importance of these Web-specific features and algorithms, describe state-of-the-art practices, and track the underlying assumptions behind the use of information from neighboring pages.

Web page classification: Features and algorithms

Tagging systems have become major infrastructures on the Web. They allow users to create tags that annotate and categorize content and share them with other users, very helpful in particular for searching multimedia content. However, as tagging is not constrained by a controlled vocabulary and annotation guidelines, tags tend to be noisy and sparse. Especially new resources annotated by only a few users have often rather idiosyncratic tags that do not reflect a common perspective useful for search. In this paper we introduce an approach based on Latent Dirichlet Allocation (LDA) for recommending tags of resources in order to improve search. Resources annotated by many users and thus equipped with a fairly stable and complete tag set are used to elicit latent topics to which new resources with only a few tags are mapped. Based on this, other tags belonging to a topic can be recommended for the new resource. Our evaluation shows that the approach achieves significantly better precision and recall than the use of association rules, suggested in previous work, and also recommends more specific tags. Moreover, extending resources with these recommended tags significantly improves search for new resources.

Latent dirichlet allocation for tag recommendation

This paper connects two research areas: automatic tagging on the web and statistical keyphrase extraction. First, we analyze the quality of tags in a collaboratively created folksonomy using traditional evaluation techniques. Next, we demonstrate how documents can be tagged automatically with a state-of-the-art keyphrase extraction algorithm, and further improve performance in this new domain using a new algorithm, "Maui", that utilizes semantic information extracted from Wikipedia. Maui outperforms existing approaches and extracts tags that are competitive with those assigned by the best performing human taggers.

https://www.cs.waikato.ac.nz/ml/publications/2009/maui_emnlp2009_1dataset.pdf

Human-competitive tagging using automatic keyphrase extraction

As a social service in Web 2.0, folksonomy provides the users the ability to save and organize their bookmarks online with "social annotations" or "tags". Social annotations are high quality descriptors of the web pages' topics as well as good indicators of web users' interests. We propose a personalized search framework to utilize folksonomy for personalized search. Specifically, three properties of folksonomy, namely the categorization, keyword, and structure property, are explored. In the framework, the rank of a web page is decided not only by the term matching between the query and the web page's content but also by the topic matching between the user's interests and the web page's topics. In the evaluation, we propose an automatic evaluation framework based on folksonomy data, which is able to help lighten the common high cost in personalized search evaluations. A series of experiments are conducted using two heterogeneous data sets, one crawled from Del.icio.us and the other from Dogear. Extensive experimental results show that our personalized search approach can significantly improve the search quality.

Exploring folksonomy for personalized search

Tags are user-generated labels for entities. Existing research on tag recommendation either focuses on improving its accuracy or on automating the process, while ignoring the efficiency issue. We propose a highly-automated novel framework for real-time tag recommendation. The tagged training documents are treated as triplets of (words, docs, tags), and represented in two bipartite graphs, which are partitioned into clusters by Spectral Recursive Embedding (SRE). Tags in each topical cluster are ranked by our novel ranking algorithm. A two-way Poisson Mixture Model (PMM) is proposed to model the document distribution into mixture components within each cluster and aggregate words into word clusters simultaneously. A new document is classified by the mixture model based on its posterior probabilities so that tags are recommended according to their ranks. Experiments on large-scale tagging datasets of scientific documents (CiteULike) and web pages del.icio.us) indicate that our framework is capable of making tag recommendation efficiently and effectively. The average tagging time for testing a document is around 1 second, with over 88% test documents correctly labeled with the top nine tags we suggested.

https://clgiles.ist.psu.edu/pubs/SIGIR2008.pdf

Real-time automatic tag recommendation

The success of the Semantic Web depends on the availability of Web pages annotated with metadata. Free form metadata or tags, as used in social bookmarking and folksonomies, have become more and more popular and successful. Such tags are relevant keywords associated with or assigned to a piece of information (e.g., a Web page), describing the item and enabling keyword-based classification. In this paper we propose P-TAG, a method which automatically generates personalized tags for Web pages. Upon browsing a Web page, P-TAG produces keywords relevant both to its textual content, but also to the data residing on the surfer's Desktop, thus expressing a personalized viewpoint. Empirical evaluations with several algorithms pursuing this approach showed very promising results. We are therefore very confident that such a user oriented automatic tagging approach can provide large scale personalized metadata annotations as an important step towards realizing the Semantic Web.

/pdf/p-tag-large-scale-automatic-generation-of-personalized-597hnq5k7b.pdf

P-TAG: large scale automatic generation of personalized annotation tags for the web

Existing desktop search applications, trying to keep up with the rapidly increasing storage capacities of our hard disks, offer an incomplete solution for information retrieval. In this paper we describe our Beagle++ desktop search prototype, which enhances conventional full-text search with semantics and ranking modules. This prototype extracts and stores activity-based metadata explicitly as RDF annotations. Our main contributions are extensions we integrate into the Beagle desktop search infrastructure to exploit this additional contextual information for searching and ranking the resources on the desktop. Contextual information plus ranking brings desktop search much closer to the performance of web search engines. Initially disconnected sets of resources on the desktop are connected by our contextual metadata, PageRank derived algorithms allow us to rank these resources appropriately. First experiments investigating precision and recall quality of our search prototype show encouraging improvements over standard search.

/pdf/beagle-semantically-enhanced-searching-and-ranking-on-the-3dbvrw1kzb.pdf

Beagle ++ : semantically enhanced searching and ranking on the desktop

The rapidly increasing quantity and diversity of data stored on our PCs made locating information in this environment very difficult. Consequently, recent research has focussed on building semantically enhanced systems for either organizing or searching data on the desktop. Building on previous work, in this paper we present the Beagle++ toolbox, a set of extendable building blocks for implementing such a system. The corresponding modular desktop search architecture integrates our previously developed metadata generators and ranking components, uses an RDF database to share data between components, and can easily integrate other external components to improve desktop search quality. Additionally, we provide implementation details about all our current components, how they interact with each other, and how to install the complete system on top of a Linux distribution.

/pdf/the-beagle-toolbox-towards-an-extendable-desktop-search-46o0nq5b55.pdf

The Beagle++ toolbox: towards an extendable desktop search architecture

Leveraging personal metadata for Desktop search: The Beagle++ system

The personal information stored on the desktop usually reaches huge dimensions nowadays. Its handling is even more difficult, taking into account complex environments and tasks we work with. An efficient method of identifying the present working context would mean an easier management of the needed resources. In this paper we propose a new way of identifying desktop usage contexts, based upon a distance between documents, which also takes into account their access timestamps. We investigate and compare our technique with traditional term vector clustering, our initial experiments showing promising results with our proposed approach.

/pdf/desktop-context-detection-using-implicit-feedback-l0leb2b5ht.pdf

Stefania Costache

Papers

P-TAG: large scale automatic generation of personalized annotation tags for the web

Beagle ++ : semantically enhanced searching and ranking on the desktop

The Beagle++ toolbox: towards an extendable desktop search architecture

Leveraging personal metadata for Desktop search: The Beagle++ system

Desktop Context Detection Using Implicit Feedback