Search (5 results, page 1 of 1)

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Abstract

As the Web continues to grow and encompass broader and more diverse sources of information, providing effective search facilities to users becomes an increasingly challenging problem. To help users deal with the deluge of Web-accessible information, we propose a search system which makes use of context to improve search results in a scalable way. By context, we mean any sources of information, in addition to any search query, that provide clues about the user's true information need. For instance, a user's bookmarks and search history can be considered a part of the search context. We consider two types of context-based search. The first type of functionality we consider is "similarity search." In this case, as the user is browsing Web pages, URLs for pages similar to the current page are retrieved and displayed in a side panel. No query is explicitly issued; context alone (i.e., the page currently being viewed) is used to provide the user with useful related information. The second type of functionality involves taking search context into account when ranking results to standard search queries. Web search differs from traditional information retrieval tasks in several major ways, making effective context-sensitive Web search challenging. First, scalability is of critical importance. With billions of publicly accessible documents, the Web is much larger than traditional datasets. Similarly, with millions of search queries issued each day, the query load is much higher than for traditional information retrieval systems. Second, there are no guarantees on the quality ofWeb pages, with Web-authors taking an adversarial, rather than cooperative, approach in attempts to inflate the rankings of their pages. Third, there is a significant amount of metadata embodied in the link structure corresponding to the hyperlinks between Web pages that can be exploitedduring the retrieval process. In this thesis, we design a search system, using the Stanford WebBase platform, that exploits the link structure of the Web to provide scalable, context-sensitive search.

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Abstract

The current web consists of documents that are highly heterogeneous and hard for machines to understand. The Semantic Web is a progressive movement of the Word Wide Web, aiming at converting the current web of unstructured documents to the web of data. In the Semantic Web, web documents are annotated with metadata using standardized ontology language. These annotated documents are directly processable by machines and it highly improves their usability and usefulness. In Ericsson, similar problems occur. There are massive documents being created with well-defined structures. Though these documents are about domain specific knowledge and can have rich relations, they are currently managed by a traditional search engine, which ignores the rich domain specific information and presents few data to users. Motivated by the Semantic Web, we aim to find standard ways to process these documents, extract rich domain specific information and annotate these data to documents with formal markup languages. We propose this project to develop a domain specific search engine for processing different documents and building explicit relations for them. This research project consists of the three main focuses: examining different domain specific documents and finding ways to extract their metadata; integrating a text search engine with an ontology server; exploring novel ways to build relations for documents. We implement this system and demonstrate its functions. As a prototype, the system provides required features and will be extended in the future.

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Abstract

Mit der Entstehung des World Wide Web im Jahre 1989 und dem darauf folgenden rasanten Anstieg der Zahl an Webseiten, kam es sehr schnell zu der Notwendigkeit, eine gewisse Ordnung in die Vielzahl von Inhalten zu bringen. So wurde schon im Jahre 1991 ein erster Vorläufer der heutigen Websuchmaschinen namens Gopher entwickelt. Die Struktur von Gopher, bei der zunächst alle Webseiten katalogisiert wurden, um anschließend komplett durchsucht werden zu können, war damals richtungweisend und wird auch heute noch in den meisten anderen Websuchmaschinen verwendet. Von damals bis heute hat sich sehr viel am Markt der Suchmaschinen verändert. Seit dem Jahre 2004 gibt es nur mehr drei große Websuchmaschinen, bezogen auf die Anzahl erfasster Dokumente. Neben Yahoo! Search und Microsofts MSN Search ist Google die bisher erfolgreichste Suchmaschine der Welt. Dargestellt werden die Suchergebnisse, indem sie der Relevanz nach sortiert werden. Jede Suchmaschine hat ihre eigenen geheimen Kriterien, welche für die Bewertung der Relevanz herangezogen werden. Googles Suchergebnisse werden aus einer Kombination zweier Verfahren angeordnet. Neben der Hypertext-Matching-Analyse ist dies die PageRank-Technologie. Der so genannte PageRank-Algorithmus, benannt nach seinem Erfinder Lawrence Page, ist die wesentliche Komponente, die Google auf seinen Erfolgsweg gebracht hat. Über die genaue Funktionsweise dieses Algorithmus hat Google, insbesondere nach einigen Verbesserungen in den letzten Jahren, nicht alle Details preisgegeben. Fest steht jedoch, dass der PageRank-Algorithmus die Relevanz einer Webseite auf Basis der Hyperlinkstruktur des Webs berechnet, wobei die Relevanz einer Webseite danach gewichtet wird, wie viele Links auf sie zeigen und Verweise von ihrerseits stark verlinkten Seiten stärker ins Gewicht fallen.

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Pages

22 S

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Pages

22 S