3,762 research outputs found
The State-of-the-arts in Focused Search
The continuous influx of various text data on the Web requires search engines to improve their retrieval abilities for more specific information. The need for relevant results to a userâs topic of interest has gone beyond search for domain or type specific documents to more focused result (e.g. document fragments or answers to a query). The introduction of XML provides a format standard for data representation, storage, and exchange. It helps focused search to be carried out at different granularities of a structured document with XML markups. This report aims at reviewing the state-of-the-arts in focused search, particularly techniques for topic-specific document retrieval, passage retrieval, XML retrieval, and entity ranking. It is concluded with highlight of open problems
Multilingual search for cultural heritage archives via combining multiple translation resources
The linguistic features of material in Cultural Heritage (CH) archives may be in various languages requiring a facility for effective multilingual search. The specialised
language often associated with CH content introduces problems for automatic translation to support search applications. The MultiMatch project is focused on enabling
users to interact with CH content across different media types and languages. We present results from a MultiMatch study exploring various translation techniques for
the CH domain. Our experiments examine translation techniques for the English language CLEF 2006 Cross-Language
Speech Retrieval (CL-SR) task using Spanish, French and German queries. Results compare effectiveness of our query
translation against a monolingual baseline and show improvement when combining a domain-specific translation lexicon with a standard machine translation system
Reasoning & Querying â State of the Art
Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF
The NASA Astrophysics Data System: Architecture
The powerful discovery capabilities available in the ADS bibliographic
services are possible thanks to the design of a flexible search and retrieval
system based on a relational database model. Bibliographic records are stored
as a corpus of structured documents containing fielded data and metadata, while
discipline-specific knowledge is segregated in a set of files independent of
the bibliographic data itself.
The creation and management of links to both internal and external resources
associated with each bibliography in the database is made possible by
representing them as a set of document properties and their attributes.
To improve global access to the ADS data holdings, a number of mirror sites
have been created by cloning the database contents and software on a variety of
hardware and software platforms.
The procedures used to create and manage the database and its mirrors have
been written as a set of scripts that can be run in either an interactive or
unsupervised fashion.
The ADS can be accessed at http://adswww.harvard.eduComment: 25 pages, 8 figures, 3 table
Recommended from our members
Local search: A guide for the information retrieval practitioner
There are a number of combinatorial optimisation problems in information retrieval in which the use of local search methods are worthwhile. The purpose of this paper is to show how local search can be used to solve some well known tasks in information retrieval (IR), how previous research in the field is piecemeal, bereft of a structure and methodologically flawed, and to suggest more rigorous ways of applying local search methods to solve IR problems. We provide a query based taxonomy for analysing the use of local search in IR tasks and an overview of issues such as fitness functions, statistical significance and test collections when conducting experiments on combinatorial optimisation problems. The paper gives a guide on the pitfalls and problems for IR practitioners who wish to use local search to solve their research issues, and gives practical advice on the use of such methods. The query based taxonomy is a novel structure which can be used by the IR practitioner in order to examine the use of local search in IR
- âŚ