P. Pirolli, J. Pitkow, R. Rao, "Silk from a sow's ear: Extracting usable structures from the Web," Proc. ACM SIGCHI Conference on Human Factors in Computing, 1996.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....system. Finally, Section 7 provides a conclusion. 2 Related Work Monitoring and understanding how the Web is used is an active area of research in both the academic and commercial worlds. A recent survey of Web Usage Mining projects has been published in [SCDT00] Several projects [BM98, CTS00, PPR96, SPF99] have shown the usefulness of content or structure when performing Web Usage Mining, however none have focused on the fact that the process can not be completed without it. While applying data mining to the structure and content of Web sites is an interesting area of research in its own ....

Peter Pirolli, James Pitkow, and Ramana Rao. Silk from a sow's ear: Extracting usable structures from the web. In CHI-96, Vancouver, 1996.

....G = V; E) which is directed, we define matrix A to be: A ij = ae 1 if (i; j) 2 E or (j; i) 2 E A is an adjacency matrix of the graph. Link structure alone provides us with rich information on the topic. By exploring the link structure, we are able to extract useful information from the web [5, 17, 6, 16, 21, 24, 22]. One of the most popular algorithm to retrieve information from the link structure is credited to Jon Kleinberg. We will briefly discuss his HITS algorithm in Appendix A. 5.2. Textual information. It is known that clustering documents based entirely on text information is not effective in ....

P. Pirolli, J. Pitkow, and R. Rao. Silk from a sow's ear: Extracting usable structures from the web. Proc. SIGCHI'96, pages 118--125, 1996.

....and decompose them. This is an open problem. Several link based page clustering approaches have been taken in the past to extract aggregates from hypertext, including strong connectivity [6] clustering based on routes likely to be taken by users [18] and spreading activation computations [16]. A combination of these techniques could be used in the future to decompose large host such as geocities.com into actual web sites. This, and the collapsing of multiple hosts that share the same domain, will allow for the creation of site graph that more accurately reflects the linkage between ....

P. Pirolli, J. Pitkow, and R. Rao. Silk from a sow's ear: Extracting usable structure from the web. In Proc. ACM SIGCHI, 1996.

....user access patterns to suggest improvements that help to design better Web pages. For example, Catledge et al. find that users rarely traverse a path of more than two hyperlinks before returning to the starting point. This observation would suggest to create dense Web sites. Pirolli et al. [33] propose to create aggregation of Web pages according to their importance or their content. Perkowitz et al. 30] propose the design of adaptive Web sites by promoting and demoting pages, highlighting hyperlinks, adding hyperlinks and clustering related pages. Perkowitz et al. 31] present an ....

Peter Pirolli, James Pitkow, and Ramana Rao. Silk from a sow's ear: Extracting usable structure from the Web. In Proceedings of ACM CHI 96 Conference on Human Factors in Computing Systems, volume i of PAPERS: World Wide Web, pages 118-125, 1996.

....the server log would not contain information about returning to page B. A B C D E Spreading activation In order to perform analysis of link topology, inter page similarity and usage paths graphs for a World Wide Web subset Pirolli, Pitkov and Rao have proposed in their highly influential paper [12] a way of of applying the Huberman and Hogg s spreading activation algorithm. The graph analysis is rather rarely used in the applications based on textual similarity. Note however, that classical document similarity matrices, like those discussed in Section 3, can be treated as graph incidence ....

....to be the network usage matrix as defined above. We can then compute activations for limited number of iterations (for example 10, as proposed by Pitkov group) and then return most active pages as a web aggregate related to starting page. Other applications of this algorithm have been presented in [12]. 3. Text mining related methods Traditional methods for information retrieval in full text databases and World Wide Web is a good example of such database need a huge amount of external knowledge. Such knowledge repositories supporting document retrieval process can have many forms like ....

P. Pirolli, J.Pitkow, R.Rao, "Silk from a Sow's Ear: Extracting Usable Structures from Web", Proc. of the Conference on Human Factors in Computing Systems: Common Ground, pages 118-125, New York, 1996

.... strength of several page properties (such as its size, or number of hyperlinks) and using following table to perform classification: This is text describing the hyperlink in the source page Modification takes into account aforementioned anchor text analysis Classes description is available in [6]. hub q a p h authority q h p a q p p q Node type Size Inlinks Outlinks Children depth Similarity to children Request frequency Entry point Precision Index 0.67 Source index 0.53 Reference 0.64 Destination 0.53 Head 0.70 Org. Home 0.30 ....

.... of documents, which at first seems necessary to distinguish for example personal home page from content page (i.e. page that actually delivers information, and not facilitates navigation) Complete description of this method, together with node type and properties explanation is available in [6]. 5.2.4.2 Coocurence analysis Other very promising method for computing relationship strength between lexical objects (not only documents, but also smaller entities such as paragraphs or even words) is Latent Semantic Analysis. The primary assumption of this method is that there exists some ....

Peter Pirolli, James Pitkow, Ramana Rao: "Silk from a Sow's Ear: Extracting Usable Structures from the Web", Xerox Palo Alto Research Centre, 1996

....3.3. 2.3 Related Work 2.3.1 Metadata analysis techniques There been a have number of different research efforts in metadata analysis which are relevant to the algorithms developed in our investigation. Many of these are compared to our work at relevant points in this report. Pirolli et al. [25], then Catarci et al. 7] discussed techniques for categorising HTML pages to identify their roles in web information structures; this is discussed in Section 3.1.3. The Lighthouse system [20] synthesised page clustering with a metasearch system; this is compared to our work in Section 5.2. ....

....learn which sources were of interest to its users, and then present them with the up to date content from those sources on a regular basis. The system included a module to identify the role of different pages in the structure of the web, which was inspired by the earlier work of Pirolli et al. [25]. The classification system proposed in [25] used arbitrary weightings for different components of feature vectors to place each page into one of several archetypical categories. Catarci et al..presented more sophisticated methods, described below, for allocating documents to these categories. A ....

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P. Pirolli, J. Pitkow, and R. Rao. Silk from a sow's ear: Extracting usable structures from the web. In Proceedings of the Conference on Human Factors in Computing Systems, CHI '96 Vancouver, Canada, 1996.

....as clustering, categorization, or data mining, although a discussion of these issues is beyond the scope of this paper. Various heuristics for link based ranking, such as counting in degrees of nodes or computing simple graph measures, have been known since the early days of web search engines [32, 36]. The Pagerank approach was introduced in 1998 as the basis of the Google ranking scheme [11, 35] Another related approach to link based ranking, proposed at around the same time, is the HITS algorithm of Kleinberg [28] Over the last few years, numerous extensions and variations of these basic ....

P. Pirolli, J. Pitkow, and R. Rao. Silk from a sow's ear: Extracting usable structures from the web. In Proc. of ACM Conf. on Human Factors in Computing Systems, April 1996.

....of explicit Web structure to improve them encourages us to work on Web structure extraction. Several works have focused on statistics studies [5] 4] 31] dealing with the use of HTML tags or with the links distribution which leads for example to the notion of hub and authority pages. Pirolli [29] has categorized Web pages following 5 predefined categories which are related to site structure, based on usage, site connectivity and content data. Broder [7] has studied the Web connectivity and has extracted a macroscopic Web structure. But none of these works deals with Web structure ....

P. Pirolli, J. Pitkow, and R. Rao. Silk from a sow's ear : extracting usable structures from the Web. In Conference on Human Factors in Computing Systems (CHI'96), Vancouver, Canada, 1996.

.... pages contain an internal structure, and hypertext links describe an external structure composed by the structure of Web sites and the Web macroscopic structure (external to Web sites) Several works have showed that it is possible to extract a hierarchical structure describing Web sites [6] 7] [8], while other deal with macroscopic structure [9] 10] An index has to represent the semantic content of documents, including the structure. Thus, an IR model has to integrate links and their impact directly into the document model. II. Structured IR on the Web We distinguish 3 approaches: ....

Peter Pirolli, James Pitkow, and Ramana Rao, \Silk from a sow's ear : extracting usable structures from the Web," in Conference on Human Factors in Computing Systems, Vancouver, Canada, April 1996, pp. 118-125.

.... not embedded in paragraphs but in exposed locations and are used to express and navigate logical structures [1] Hereby, they usually form patterns, like hierarchies or sequences or lead to landmark pages like the homepage of a site, a search page or an index page, routing the users to other pages [27]. Our observation of current link usage on the Web suggests that many more links are rather of structural than of associative character: most Web pages include navigation areas, often explicitly located in a navigation bar . E commerce sites use links mainly to structure groups of articles or ....

Pirolli, P., J. Pitkow, et al.: Silk from a Sow's Ear: Extracting Usable Structures from the Web. Proc. ofACM SIGCHI Conf. on Human Factors in Computing Systems, Vancouver, Canada, 1996.

....brief survey of web modeling. Then we summarize the history of web improvement with caching and site design. 1.3.1 Modeling the web It is important to know how the web looks like and how it is expanding. These are two of the most challenging and intriguing questions about the web. Some authors [3, 25, 26] extracted important characteristics of the web. As a consequence of fast expansion, their results are only an outdated snapshot of the web and do not tell us much about how it looks now, or how it is expanding. Due to this uncertainty, many researches based their work on random graphs. This ....

Peter Pirolli, James Pitkow, and Ramana Rao. Silk from a Sow's Ear: Extracting Usable Structure from the Web. In Proceedings of ACM CHI 96 Conference on Human Factors in Computing Systems, volume 1 of PAPERS: World Wide Web, pages 118125. http://www.acm.org/sigchi/chi96/proceedings/papers/Pirolli_2/pp2.html, 1996.

.... path [5] Some researchers have proposed studying the average length of all shortest paths instead, the average connected distance [3] A web site usually consists of different clusters, richly interconnected parts of the site graph dedicated to a topic with but a few links to other topics [13]. This is similar to the graphtheoretic notion block, a maximal non separable sub graph, connected to the remaining part via one node, the cutnode. 5] By releasing the constraints so that cross references do not hinder the recognition of a cutnode, this feature can be put into use. Related to ....

....based on the function they fulfill. Although most pages that merely facilitate navigation are usually marked with proper names (e.g. menu, glossary) it might be useful to identify them by their local structural properties for structure reflects semantic relationships between web pages [2] In [13] and [2] the following page types are discriminated by such characteristics: home pages are the first pages of a set of pages one would visit. The distance from the home page to other pages within the set is small. According to Botafogo [2] the number of links on the home page (its out degree) ....

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Pirolli, P., Pitkow, J., Rap, R.: Silk from a Sow's Ear: Extracting Usable Structures from the Web. CH1

....This leads to the emergence of communities of web pages. Due to the immense size of the web, mining the link structure of the web to determine the communities is a chal lenging problem. Several authors have considered graph theoretic techniques to address this problem [1] 2] 3] 4] [5], 6] None of these techniques enable a query to determine the community, or communities, that a particular web page belongs to dynamically. The ability to do so is interesting for several applications. For example, this ability would be quite interesting in gathering competitive intelligence ....

....that are used to determine those communities. We then propose a new method that, given a web page, will determine the communities to which it belongs dynamically. II. FINDING COMMUNITIES ON THE WEB Several different notions of community on the web have been proposed [1] 2] 3] 4] [5], 6] The common thread amongst the notions of community proposed is that they model the web as a graph based on link structure, and search for properties of the graph deemed to indicate a community of web pages. In this section, we survey the graph properties used to indicate communities, and ....

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Peter Pirolli, James Pitkow, and Ramana Rao, "Silk from sow's ear: Extracting usable structures from the web," in Proc. ACM Conf. Human Factors in Computing Systems, CH[. 1996, ACM Press.

....from the parent are close together; titles, descriptions, and anchor text represent at least part of the target page; and that anchor text may be a useful discriminator among unseen child pages. These results present the foundations necessary for the success of many 84 proposed techniques (e.g. PPR96, Spe97] and implemented Web systems, including search and meta search engines (e.g. Inf02b, SE95, SE97, DH97c, HD97, Inf02a, ZE98, ZE99, LG98b, LG98a, Goo02, Alt02, Ink02, Fas02, McB94, Lyc02b] focused crawlers (e.g. CGMP98, CvdBD99, BSHJ 99, Men97, MB00, Lie97, RM99] linkage analyzers ....

Peter Pirolli, James E. Pitkow, and Ramana Rao. Silk from a sow's ear: Extracting usable structures from the Web. In Proceedings of CHI '96: Human factors in computing systems, Vancouver, B.C., Canada, April 1996. ACM Press.

....by Hearst and Paderson [10] based on textual information only, with emphasis on summarization. More recently this approach is taken in Grouper web interface[19] Exploring web link structures in the information retrieval context to identify topical themes is examined by Larson[13] Pirolli, et al. [15]. Most recently, Text information is used together with HITS algorithm by Chakrabarti et al. 4] in topic distillation by Bharat and Henzinger [1] and as attribute vectors for clustering by Modha and Spengler [14] The new thrust in our approach is (1) to comprehensively incorporate information ....

P. Pirolli, J. Pitkow, and R. Rao. Silk from a sow's ear: Extracting usable structures from the web. Proc. SIGCHI'96, 1996.

....[17] have been proposed to identify cluster or communities. Clustering of web pages helps to identify central topic for a query and therefore is very useful for web information retrieval and analysis. Besides clustering, there are also a large number of web link connectivity information analyses [19, 24, 20, 22, 5]. For these reasons, separation of disconnected and nearlydisconnected components is useful to discover clusters of web communities and connectivity analysis, as an important application of a general method for graph segmentation problem. Standard method to nd disconnected components are ....

P. Pirolli, J. Pitkow, and R. Rao. Silk from a sow's ear: Extracting usable structures from the web. Proc. SIGCHI'96, 1996.

....site visitors to clusters. The links that are presented to a given user are dynamically selected based on what pages other users assigned to the same cluster have visited. The di#culty of identifying users and user sessions from Web server logs has been addressed in research performed by Pitkow [5, 21, 23]. Two of the biggest impediments to collecting reliable usage data are local caching and proxy servers. In order to improve performance and minimize network tra#c, most Web browsers cache the pages that have been requested. As a result, when a user hits the back button, the cached page is ....

....can be easily obtained by means of a site crawler , that parses the HTML files to create a list of all of the hypertext links on a given page, and then follows each link until all of the site pages are mapped. The pages are classified using an adaptation of the classification scheme presented in [23]. The WEBMINER system recognizes five main types of pages: Head Page a page whose purpose is to be the first page that users visit, i.e. home pages. Content Page a page that contains a portion of the information content that the Web site is providing. Navigation Page a page ....

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P. Pirolli, J. Pitkow, and R. Rao. Silk from a sow's ear: Extracting usable structures from the Web. In Proc. of 1996 Conference on Human Factors in Computing Systems (CHI-96), Vancouver, British Columbia, Canada, 1996.

....for most authoritative pages to a certain topic. The approach proposed by Rafiei et al. 19] identifies the topics of a designated page. Dean et al. 6] discusses how to find related pages to a certain page. There is also a group of works (e.g. Kumar et al. 14] Gibson et al. 8] Pirolli et al. [18]) that aim at inferring and analyzing web communities or other web structures from the hyperlinks. Henzinger et al. 10] suggested measuring link quality of a web page using the random walking model. Very recently, Lempel et al. 15] proposes PicASHOW system, which employs link analysis to ....

Pirolli, P., Pitkow, J., and Rao, R., "Silk from a Sow's Ear: Extracting Usable Structure from the Web." In Proc. ACM SIGCHI Conf. on Human Factors in Computing Systems, pp. 383-390, 1997.

....et al. 53] implemented Kleinberg s algorithms in the HITS system at IBM Almaden. Later this became the CLEVER project. While hub and authority have become rather commonly used, other workers [13, 97] used index node to refer to hubs, and reference nodes as authorities. Pirolli et al. [94] analyzed pages served by the Xerox Research Park server to 6 annotate and aggregate them. They used both link structure and text similarity to group pages together into conceptually unified entities [34] Of interest was their method of page characterization based on the features exhibited by ....

....information at various levels of abstraction. integrated querying and browsing navigating a collection . sharing gathered information this is what the NSDL provides This work was done at NEC s C C Research Laboratories in San Jose. It refers to the work being done at Xerox Research Parc [94, 95]. Other work at NEC C C is described by Katz and Li [66, 78] Topic distillation is the process by which defined topics are teased out of a larger collection of items, very much in the spirit of analysis. The purpose 8 of distillation is to identify hubs, i.e. pages with large lists of links to ....

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P. Pirolli, J. Pitkow, and R. Rao. Silk from a sow's ear: Extracting usable structures from the Web. Apr. 1996. Available: p118-pirolli/p118-pirolli.html>.

....text based methods. Applications of our method include the creation of improved search engines, content filtering, and objective analysis of the content of the web and relationships between communities represented on the web. Such analysis, taking into account issues such as the digital divide [9], may help improve our understanding of the world. Acknowledgments We thank Inktomi for the random URL data. References [1] Ravindra K. Ahuja, Thomas L. Magnanti, and James B. Orlin. Network Flows : Theory, Algorithms, and Applications. Prentice Hall, Englewood Cliffs, NJ, 1993. 2] R. ....

....of the Eighteenth Annual ACM Symposium on Theory of Computing, pages 136 146, Berkeley, California, 28 30 May 1986. 8] Bernardo A. Huberman, Peter L. T. Pirolli, James E. Pitkow, and Rajan M. Lukose. Strong regularities in World Wide Web surfing. Science, 280(5360) 95 97, 1998. [9] T.P. Novak and D.L.Hoffman. Bridging the digital divide: The impact of race on computer access and Internet use. Science, 281:919, 1998. 10] D. Watts and S. Strogatz. Collective dynamics of small world networks. Nature, 393:440 442, 1998. SIDEBAR: Finding related pages on the web Previous ....

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Peter Pirolli, James Pitkow, and Ramana Rao. Silk from a sow's ear: Extracting usable structures from the web. In Proc. ACM Conf. Human Factors in Computing Systems, CHI. ACM Press, 1996. 7

....the digital computer. The World Wide Web is quickly acquiring a large user community, and becoming the primary source of information for many information tasks [55] Cognitive psychologists have described this rich relationship between users and an information environment as an information ecology [76, 77]. The word ecology is used to describe how the task and or the information changes and evolves through time. People seek information and consume it in these information ecologies, and they form strategies for acquiring the most important pieces of information. Psychologists have observed that ....

....is spreading activation [4] which is an algorithm for computing the relevance between items. Spreading activation can be computed using various pieces of information, such as structure of the links and frequency of usage, co citation strength between documents, and word vector based similarity [77]. Spreading activation provides information at the intermediate level by condensing the relationship between each node and its relevance to all other documents in the set. 1.3.4 Advantages of Visual Sensemaking in the Visualization Spreadsheet From a cognitive point of view, the success of ....

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Peter Pirolli, James Pitkow, and Ramana Rao. Silk from a sow's ear: Extracting usable structure from the web. In Proceedings of ACM CHI 96 Conference on Human Factors in Computing Systems, volume 1, pages 118--125, 1996.

....growth and diversi cation of the Web. Probably the most widely recognized example of this kind is the PageRank index employed by the Google search engine [6] PageRank is but one of many models and algorithms to rank Web resources according to their position in a link structure (see, e.g. [25, 20, 9, 1, 5, 8]) Our goal is to supplement rankings with a meaningful visualization of the graph they are computed on. While graph visualization is an active area of research as well [10, 19] its integration with quantitative analysis is only beginning to receive attention. It is, however, rather dicult to ....

P. Pirolli, J. Pitkow, and R. Rao. Silk from a sow's ear: Extracting usable structures from the Web. In Proc. ACM Conf. Human Factors in Computing Systems, pp. 118-125, 1996.

....systems [44] Pirolli [43] explains the idea of information foraging and analyzes projects such as Scatter Gather in this context. Empirical research involving usage modeling and information capture is also relevant here. Drawing ideas from the ACT R theory of cognition, Pirolli et al. [45] describe how a quantitative model of information foraging can be defined. Tools for capturing history of interaction in information foraging are also well studied [25, 63] Mining web user logs has become a popular technique for obtaining models of site navigation [40, 41, 59] While this strand ....

P. Pirolli, J. Pitkow, and R. Rao. Silk from a Sow's Ear: Extracting Usable Structures from the Web. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI'96), pages 118--125. Vancouver, Canada, 1996.

.... grouping by classification or clustering ex. path distance to a reference page head, navigation, content, look up, personal Integrating content and structure III structure as weighted vector of page(view)s ex. page types in Pirolli et al. B24] and Cooley et al. B20] DP 36] [54] [15] Info on schools indiv. school . list of schools 1 parameter . 2 par.s 3 parameters Location Name . Location Name . PKDD 2001 Tutorial: KDD for Personalization 1. service based concept hierarchy: which query options Relating content and structure to mined usage I : ....

Pirolli, P., Pitkow, J., and Rao, R. Silk from a sow's ear: Extracting usable structures from the web. In CHI-96, Vancouver. PKDD 2001 Tutorial: KDD for Personalization

....(Nobel Prize Winner) The Web has exploded into an information ecology with several hundred million users and over a billion Web pages. Understanding the complexity of the interactions of information seekers with the Web ecology is a difficult scientific endeavor that has great practical value [ 12,18]. For Kim was suppoled by a summer undergraduate internship program. As quoted by Hal Varian in Scientific American Sept. 1995. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or ....

Pirolli, P., Pitkow, J., and Rao, R. (1996) Silk from a sow's ear: Extracting usable structures from the web. Proceedings of the Conference on Human Factors in Computing Systems, CHI '96 Vancouver, Canada.

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P. Pirolli, J. Pitkow, R. Rao, "Silk from a sow's ear: Extracting usable structures from the Web," Proc. ACM SIGCHI Conference on Human Factors in Computing, 1996.

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Pirolli, P., Pitkow, J., and Rao, R. Silk from a sow's ear: Extracting usable structures from the web. In CHI-96, Vancouver.

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P. Pirolli, J. Pitkow, and R. Rao. Silk from a sow's ear: Extracting usable structures from the web. In Proc. ofCHI'96 , 1996.

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P. Pirolli, J. Pitkow, and R. Rao. Silk from a sow's ear: Extracting usable structures from the web. In Proc. ACM Conference on Human Factors in Computing Systems, pages 118--125, 1996. 732

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P. Pirolli, J. Pitkow, and R. Rao. Silk from a sow's ear: Extracting usable structures from the web. In Proc. of the ACM SIGCHI Conference on Human Factors in Computing, pages 118--125. ACM Press, 1996.

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P. Pirolli, J. Pitkow, and R. Rao. Silk from a sow's ear: Extracting usable structures from the web. In Proc. ACM Conf. Human Factors in Computing Systems, CHI. ACM Press, 1996.

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Pirolli P, Pitkow J, Rao R. Silk from a sow's ear: extracting usable structures from the web. In Proceedings of Conference on Human Factors in Computing Systems (CHI(96), Vancouver, British Columbia, Canada 1996; 1996:118 -- 25.

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Peter Pirolli, James Pitkow, and Ramana Rao. Silk from a sow's ear: Extracting usable structures from the web. In CHI'96, Vancouver, Canada, Apr. 1996.

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P. Pirolli, J. Pitkow, and R. Rao. Silk from a sow's ear: Extracting usable structures from the web. In CHI-96, Vancouver, 1996.

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Pirolli P., Pitkow J. and Rao R. (1996) "Silk from a Sow's Ear: Extracting Usable Structures from the Web." Systems (April 13-18, Vancouver BC) ACM/SIGCHI, N.Y., pp. 118-125.

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P. Pirolli, J. Pitkow, and R. Rao. "Silk from a Sow's Ear: Extracting Usable Structures from the Web". In CHI'96, Proceedings of the ACM Conference on Human Factors and Computing Systems. ACM Press, 1996.

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P. Pirolli, J. Pitkow, and R. Rao. Silk from a sow's ear: Extracting usable structures from the web. In Proc. ACM Conf. Human Factors in Computing Systems, CHI. ACM Press, 1996. 363

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P. Pirolli, J. Pitkow, and R. Rao. Silk from a sow's ear: Extracting usable structures from the web. In Proc. of the ACM SIGCHI Conference on Human Factors in Computing, pages 118--125. ACM Press, 1996.

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P. Pirolli, J. Pitkow, R. Rao, "Silk from a sow's ear: Extracting usable structures from the Web." Proc. ACM SIGCHI Conference on Human Factors in Computing, 1996.

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Pirolli, P., Pitkow, J., and Rao, R. Silk from a Sow's Ear: Extracting Usable Structures from the Web, in Proceedings of CHI'96 (Vancouver BC, April 1996), ACM Press, 118-125.

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Peter Pirolli, James Pitkow, and Ramana Rao. Silk from a sow's ear: Extracting usable structures from the Web. In Proc. ACM Conf. Human Factors in Computing Systems (CHI '96), pages 118--125, 1996.

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P. Pirolli, J.E. Pitkow, and R. Rao. Silk from a sow's ear: Extracting usable structures from the Web. In Proceedings of ACM Conference on Human Factors in Computing Systems, pages 118--125, Vancouver, 1996.

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Pirolli, P., Pitkow, J., Rao, R.. Silk from a Sow's Ear: Extracting Usable Structures from the Web", Proc CHI 1996.

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P. Pirolli, J. Pitkow, and R. Rao. "Silk from a Sow's Ear: Extracting Usable Structures from the Web". In CHI'96, Proceedings of the ACM Conference on Human Factors and Computing Systems. ACM Press, 1996.

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P. Pirolli, J. Pitkow, and R. Rao. Silk from a Sow's Ear: Extracting Usable Structure from the Web. In Conference on Human Factors in Computing Systems, Apr. 1996.

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P. Pirolli, J. Pitkow, and R. Rao, "Silk from sow's ear: Extracting usable structures from the Web," in Proceedings of the

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Pirolli, P., Pitkow, J., and Rao, R.: Silk from a Sow's Ear: Extracting Usable Structures from the Web. In Proceedings of the ACM Conference on Human Factors in Computing Systems, Vancouver, Canada, Apr 1996.

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