Using web structure for classifying and describing web pages

Glover, Eric; Tsioutsiouliklis, Kostas; Lawrence, Steve; Pennock, David M.; Flake, Gary William

doi:10.1145/511446.511520

Cited by 187 publications

(119 citation statements)

References 12 publications

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“…With regard to effectiveness, we have mentioned in the beginning that [10,12] used the Web structure itself to determine a focus for harvesting data. In this line, by determining such a focus we could have a more domain-specific disambiguation than in our current approach.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Towards the self-annotating web

Cimiano

Handschuh

Staab³

2004

Proceedings of the 13th International Conference on World Wide Web

242

170

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The success of the Semantic Web depends on the availability of ontologies as well as on the proliferation of web pages annotated with metadata conforming to these ontologies. Thus, a crucial question is where to acquire these metadata. In this paper we propose PANKOW (Pattern-based Annotation through Knowledge on the Web), a method which employs an unsupervised, pattern-based approach to categorize instances with regard to an ontology. The approach is evaluated against the manual annotations of two human subjects. The approach is implemented in OntoMat, an annotation tool for the Semantic Web and shows very promising results.

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Section: Resultsmentioning

confidence: 99%

“…• In [10,12], the Web structure itself is used to determine a focus for harvesting data. Thus, specialized semantic relationships, such as recommendations coming from a particular Web community, can be derived.…”

Section: Introductionmentioning

confidence: 99%

Towards the self-annotating web

Cimiano

Handschuh

Staab³

2004

Proceedings of the 13th International Conference on World Wide Web

242

170

View full text Add to dashboard Cite

show abstract

“…Slattery and Craven (2000) explored the hyperlink topology using an extended HITS algorithm. Similarly, Cohn and Hofmann (2001) and Glover et al (2002) showed that classification performance can be improved by combining link-based and contentbased techniques. Chakrabarti et al (1998) proposed a probabilistic model to utilize both text and linkage information to classify a database of patents and a small Web collection.…”

Section: Web-page Categorizationmentioning

confidence: 95%

Reinforcing Web-object Categorization Through Interrelationships

Xue

Shen

et al. 2006

Data Min Knowl Disc

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Abstract. Existing categorization algorithms deal with homogeneous Web objects, and consider interrelated objects as additional features when taking the interrelationships with other types of objects into account. However, focusing on any single aspect of the inter-object relationship is not sufficient to fully reveal the true categories of Web objects. In this paper, we propose a novel categorization algorithm, called the Iterative Reinforcement Categorization Algorithm (IRC), to exploit the full interrelationship between different types of Web objects on the Web, including Web pages and queries. IRC classifies the interrelated Web objects by iteratively reinforcing the individual classification results of different types of objects via their interrelationship. Experiments on a clickthrough-log dataset from the MSN search engine show that, in terms of the F 1 measure, IRC achieves a 26.4% improvement over a pure content-based classification method. It also achieves a 21% improvement over a query-metadata-based method, as well as a 16.4% improvement on F 1 measure over the well-known virtual document-based method. Our experiments show that IRC converges fast enough to be applicable to real world applications.

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“…The previous works have tried to exploit the textual contents in each page, and also various web-related information sources [5], such as html tags [6]- [9], URLs [7] [9]- [11], subgraphs of web pages [12] [13], directory structures [13] [14], anchor texts [6]- [8], contents of globally link-related pages [8] [15]- [17], and contents of local surrounding pages [7] [12]- [14].…”

Section: Related Workmentioning

confidence: 99%

Buildingweb page collections efficiently exploiting local surrounding pages

Wang¹,

Oyama

2009

Prog. Inform.

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This paper describes a method for building a high-quality web page collection with a reduced manual assessment cost that exploits local surrounding pages. Effectiveness of the method is shown through experiments using a researcher's homepage as an example of the target categories. The method consists of two processes: rough filtering and accurate classification. In both processes, we introduce a logical page group structure concept that is represented by the relation between an entry page and its surrounding pages based on their connection type and relative URL directory level, and use the contents of local surrounding pages according to that concept. For the first process, we propose a very efficient method for comprehensively gathering all potential researchers' homepages from the web using property-based keyword lists. Four kinds of page group models (PGMs) based on the page group structure were used for merging the keywords from the surrounding pages. Although a lot of noise pages are included if we use keywords in the surrounding pages without considering the page group structure, the experimental results show that our method can reduce the increase of noise pages to an allowable level and can gather a significant number of the positive pages that could not be gathered using a single-page-based method. For the second process, we propose composing a three-grade classifier using two base classifiers: precision-assured and recall-assured. It classifies the input to assured positive, assured negative, and uncertain pages, where the uncertain pages need a manual assessment, so that the collection quality required by an application can be assured. Each of the base classifiers is further composed of a surrounding page classifier (SC) and an entry page classifier (EC). The SC selects likely component pages and the EC classifies the entry pages using information from both the entry page and the likely component pages. An evident performance improvement of the base classifiers by the introduction of the SC is shown through experiments. Then, the reduction of the number of uncertain pages is evaluated and the effectiveness of the proposed method is shown.

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Using web structure for classifying and describing web pages

Cited by 187 publications

References 12 publications

Towards the self-annotating web

Towards the self-annotating web

Reinforcing Web-object Categorization Through Interrelationships

Buildingweb page collections efficiently exploiting local surrounding pages

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