40,894 research outputs found
Initial Observations on Query Based Sampling in Distributed CLIR
Cross Language Information Retrieval (CLIR) enables people to search information written in different languages from their query languages. Information can be retrieved either from a single cross lingual collection or from a variety of dis-tributed cross lingual sources. This paper pre-sents initial results exploring the effectiveness of distributed CLIR using query-based sampling techniques, which to the best of our knowledge has not been investigated before. In distributed retrieval with multiple databases, query-based sampling provides a simple and effective way for acquiring accurate resource descriptions which helps to select which databases to search. Obser-vations from our initial experiments show that the negative impact of query-based sampling on cross language search may not be as great as it is on monolingual retrieval
Query-related data extraction of hidden web documents
The larger amount of information on the Web is stored in document databases and is not indexed by general-purpose search engines (i.e., Google and Yahoo). Such information is
dynamically generated through querying databases — which are
referred to as Hidden Web databases. Documents returned in
response to a user query are typically presented using templategenerated Web pages. This paper proposes a novel approach that identifies Web page templates by analysing the textual contents and the adjacent tag structures of a document in order to extract query-related data. Preliminary results demonstrate that our approach effectively detects templates and retrieves data with high recall and precision
Information extraction from template-generated hidden web documents
The larger amount of information on the Web is stored in document databases and is not indexed by general-purpose
search engines (such as Google and Yahoo). Databases dynamically generate a list of documents in response to a user
query – which are referred to as Hidden Web databases. Such documents are typically presented to users as templategenerated
Web pages. This paper presents a new approach that identifies Web page templates in order to extract queryrelated
information from documents. We propose two forms of representation to analyse the content of a document –
Text with Immediate Adjacent Tag Segments (TIATS) and Text with Neighbouring Adjacent Tag Segments (TNATS).
Our techniques exploit tag structures that surround the textual contents of documents in order to detect Web page
templates thereby extracting query-related information. Experimental results demonstrate that TNATS detects Web page
templates most effectively and extracts information with high recall and precision
Finding Patterns in a Knowledge Base using Keywords to Compose Table Answers
We aim to provide table answers to keyword queries against knowledge bases.
For queries referring to multiple entities, like "Washington cities population"
and "Mel Gibson movies", it is better to represent each relevant answer as a
table which aggregates a set of entities or entity-joins within the same table
scheme or pattern. In this paper, we study how to find highly relevant patterns
in a knowledge base for user-given keyword queries to compose table answers. A
knowledge base can be modeled as a directed graph called knowledge graph, where
nodes represent entities in the knowledge base and edges represent the
relationships among them. Each node/edge is labeled with type and text. A
pattern is an aggregation of subtrees which contain all keywords in the texts
and have the same structure and types on node/edges. We propose efficient
algorithms to find patterns that are relevant to the query for a class of
scoring functions. We show the hardness of the problem in theory, and propose
path-based indexes that are affordable in memory. Two query-processing
algorithms are proposed: one is fast in practice for small queries (with small
patterns as answers) by utilizing the indexes; and the other one is better in
theory, with running time linear in the sizes of indexes and answers, which can
handle large queries better. We also conduct extensive experimental study to
compare our approaches with a naive adaption of known techniques.Comment: VLDB 201
Distributed Information Retrieval using Keyword Auctions
This report motivates the need for large-scale distributed approaches to information retrieval, and proposes solutions based on keyword auctions
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