1598607
The infocious web search engine: improving web searching through linguistic analysis
In this paper we present the Infocious Web search engine [23]. Our goal in creating Infocious is to improve the way people find information on the Web by resolving ambiguities present in natural language text. This is achieved by performing linguistic analysis on the content of the Web pages we index, which is a departure from existing Web search engines that return results mainly based on keyword matching. This additional step of linguistic processing gives Infocious two main advantages. First, Infocious gains a deeper understanding of the content of Web pages so it can better match users' queries with indexed documents and therefore can improve relevancy of the returned results. Second, based on its linguistic processing, Infocious can organize and present the results to the user in more intuitive ways. In this paper we present the linguistic processing technologies that we incorporated in Infocious and how they are applied in helping users find information on the Web more efficiently. We discuss the various components in the architecture of Infocious and how each of them benefits from the added linguistic processing. Finally, we experimentally evaluate the performance of a component which leverages linguistic information in order to categorize Web pages.
171503
Parallel crawlers
In this paper we study how we can design an effective parallel crawler. As the size of the Web grows, it becomes imperative to parallelize a crawling process, in order to finish downloading pages in a reasonable amount of time. We first propose multiple architectures for a parallel crawler and identify fundamental issues related to parallel crawling. Based on this understanding, we then propose metrics to evaluate a parallel crawler, and compare the proposed architectures using 40 million pages collected from the Web. Our results clarify the relative merits of each architecture and provide a good guideline on when to adopt which architecture.
1739520
Exploiting Geographical Location Information of Web Pages
Many information resources on the web are relevant primarily to limited geographical communities. For instance, web sites containing information on restaurants, theaters, and apartment rentals are relevant primarily to web users in geographical proximity to these locations. In contrast, other information resources are relevant to a broader geographical community. For instance, an on-line newspaper may be relevant to users across the United States. Unfortunately, the geographical scope of web resources is largely ignored by web search engines. We make the case for identifying and exploiting the geographical location information of web sites so that web search engines can rank resources in a geographically sensitive fashion, in addition to using more traditional information-retrieval strategies. In this paper, we first consider how to compute the geographical location of web pages. Subsequently, we consider how to exploit such information in one specific "proof-of-concept" application we implemented in JAVA, and discuss other examples as well.
1713822
A fast regular expression indexing engine
In this paper; we describe the design, architecture, and lessons learned from the implementation of a fast regular-expression indexing engine FREE. FREE uses a prebuilt index to identify the text data units which may contain a matching string and only examines these further. In this way, FREE shows orders of magnitude performance improvement in certain cases over standard regular expression matching systems, such as lex, awk and grep.
1720833
Automatic identification of user interest for personalized search
One hundred users, one hundred needs. As more and more topics are being discussed on the web and our vocabulary remains relatively stable, it is increasingly difficult to let the search engine know what we want. Coping with ambiguous queries has long been an important part of the research on Information Retrieval, but still remains a challenging task. Personalized search has recently got significant attention in addressing this challenge in the web search community, based on the premise that a user's general preference may help the search engine disambiguate the true intention of a query. However, studies have shown that users are reluctant to provide any explicit input on their personal preference. In this paper, we study how a search engine can learn a user's preference automatically based on her past click history and how it can use the user preference to personalize search results. Our experiments show that users' preferences can be learned accurately even from little click-history data and personalized search based on user preference yields significant improvements over the best existing ranking mechanism in the literature.
2425278
Shuffling a Stacked Deck: The Case for Partially Randomized Ranking of Search Engine Results
In-degree, PageRank, number of visits and other measures of Web page popularity significantly influence the ranking of search results by modern search engines. The assumption is that popularity is closely correlated with quality, a more elusive concept that is difficult to measure directly. Unfortunately, the correlation between popularity and quality is very weak for newly-created pages that have yet to receive many visits and/or in-links. Worse, since discovery of new content is largely done by querying search engines, and because users usually focus their attention on the top few results, newly-created but high-quality pages are effectively "shut out," and it can take a very long time before they become popular.We propose a simple and elegant solution to this problem: the introduction of a controlled amount of randomness into search result ranking methods. Doing so offers new pages a chance to prove their worth, although clearly using too much randomness will degrade result quality and annul any benefits achieved. Hence there is a tradeoff between exploration to estimate the quality of new pages and exploitation of pages already known to be of high quality. We study this tradeoff both analytically and via simulation, in the context of an economic objective function based on aggregate result quality amortized over time. We show that a modest amount of randomness leads to improved search results.
39501622
A logic programming approach to hybrid knowledge representation
A new hybrid system called Sphinx is presented which combines classification-based reasoning with logic programming technique. It consists of two major components: a terminological component and an assertional one. The former uses a frame-based knowledge representation scheme based on classification and the latter is a theorem prover based on Horn logic. The assertional component can also handle a first-order query language and wh-questions. A new knowledge base maintenance mechanism based on the negation as failure inference rule enables Sphinx to support incremental assertions and retractions. In addition, Sphinx provides explanation capability to help the user in developing and debugging knowledge bases.
43684392
Wavelet Analysis Based Blind Watermarking for 3-D Surface Meshes
As most previous wavelet analysis based 3-D mesh watermarking methods embed the watermark information into wavelet coefficients arranged in a certain order, they have not been used as blind schemes since the connectivity information must be exactly known in the watermark extraction process. In this paper, we propose a blind watermarking method based on wavelet analysis for 3-D mesh model. Two new techniques are introduced. One is to exploit the statistical features of scale coefficients on an approximation (low resolution) level for watermark embedding. Another is to extract the hidden watermark, not from the same resolution level as used in embedding process, but directly from the spatial domain. As the proposed watermark detection does not require the wavelet analysis, any pre-processing such as registration and re-sampling, is not needed. These techniques allow to detect the watermark without referring to the original meshes. In addition, the proposed are applicable directly to irregular meshes by using irregular wavelet analysis. Through simulations, we prove that our method is fairly robust against various attacks including topological ones.
9493395
3-D Dynamic Mesh Compression using Wavelet-Based Multiresolution Analysis
In this paper, we present a wavelet-based progressive compression method for 3-D dynamic meshes. Our method exploits the spatial and temporal redundancy. We encode the geometry of base mesh, the wavelet coefficients and the connectivity of each resolution level in order to reduce the spatial redundancy of intra meshes. For inter mesh coding, we encode the differences of geometry of base meshes and of their wavelet coefficients between adjacent frames to reduce the temporal redundancy. Our proposal is based on the wavelet-based multiresolution analysis which uses a perfect reconstruction filter bank and therefore it enables not only progressive representation but also lossless compression. The simulation results demonstrate that the proposed method is applicable to lossy and lossless compression of 3-D dynamic meshes.
26782414
The Semantics of a Functional Logic Language with Input Mode
46360239
A New Algorithm for Subsumption Test