Search results for " retrieval."
showing 10 items of 1102 documents
<title>Combining multiple image descriptions for browsing and retrieval</title>
2000
Retrieving images form large collections using image content is an important problem, in this multimedia age. A quick content-based visual access to the stored image is capital for efficient navigation through image collections. In this paper we introduce several techniques which characterize color homogeneous object and their spatial relationships for efficient content-based image retrieval. We present a region growing technique for efficient color homogeneous objects segmentation and extend the 2D string to an accurate description of spatial information and relationships. In order to improve content-based image retrieval, our method emphasized several objectives, such as: automated extrac…
Content Code Blurring: A New Approach to Content Extraction
2008
Most HTML documents on the world wide web contain far more than the article or text which forms their main content. Navigation menus, functional and design elements or commercial banners are typical examples of additional contents. Content extraction is the process of identifying the main content and/or removing the additional contents. We introduce content code blurring, a novel content extraction algorithm. As the main text content is typically a long, homogeneously formatted region in a web document, the aim is to identify exactly these regions in an iterative process. Comparing its performance with existing content extraction solutions we show thatfor most documents content code blurrin…
Semantic web service discovery system for road traffic information services
2015
Create a multi-agent platform for a traveller information system (FIPA standards).Extend Paulucci algorithm with the use of seven similarity measures.Weight the similarity measure according to semantic relation and parameter nature.Improved running-time with a filtering pre-process for non-functional parameters.Improved the recall by measuring the sibling relationship concepts. We describe a multi-agent platform for a traveller information system, allowing travellers to find the road traffic information web service (WSs) that best fits their requirements. After studying existing proposals for discovery of semantic WS, we implemented a hybrid matching algorithm, which is described in detail …
Semantic Portal for Legislative Information
2006
Semantic portals enabled by Semantic Web technologies have been suggested to provide a point of access to an integrated body of information about some domain. In the area of e-Government there are multiple possible domains for semantic portals, one of them being legislative work. In this paper we propose a semantic portal based on a rich metadata repository to support the retrieval of legislative information. The portal provides process oriented semantic browsing capabilities. A prototype of the portal has been implemented for the retrieval of Finnish legislative information.
Wordnet and semidiscrete decomposition for sub-symbolic representation of words
2009
A methodology for sub-symbolic semantic encoding of words is presented. The methodology uses the standard, semantically highly-structured WordNet lexical database and the SemiDiscrete matrix Decomposition to obtain a vector representation with low memory requirements in a semantic n-space. The application of the proposed algorithm over all the WordNet words would lead to a useful tool for the sub-symbolic processing of texts.
Publish By Example
2008
We propose an approach for producing database publishing programs by example. The main idea is to interactively build an example document, representative of the program output. The system infers from this document, without ambiguity, the publishing program. The end-user does not need to know a programming language, a query language or the database schema.
Towards semantic-based RSS merging
2009
Merging information can be of key importance in several XML-based applications. For instance, merging the RSS news from different sources and providers can be beneficial for end-users (journalists, economists, etc.) in various scenarios. In this work, we address this issue and mainly explore the relatedness relationships between RSS entities/ elements. To validate our approach, we also provide a set of experimental tests showing satisfactory results. © 2009 Springer-Verlag Berlin Heidelberg
Combining content extraction heuristics
2008
The main text content of an HTML document on the WWW is typically surrounded by additional contents, such as navigation menus, advertisements, link lists or design elements. Content Extraction (CE) is the task to identify and extract the main content. Ongoing research has spawned several CE heuristics of different quality. However, so far only the Crunch framework combines several heuristics to improve its overall CE performance. Since Crunch, though, many new algorithms have been formulated. The CombinE system is designed to test, evaluate and optimise combinations of CE heuristics. Its aim is to develop CE systems which yield better and more reliable extracts of the main content of a web …
A Novel Approach to Improve the Accuracy of Web Retrieval
2010
General purpose search engines utilize a very simple view on text documents: They consider them as bags of words. It results that after indexing, the semantics of documents is lost. In this paper, we introduce a novel approach to improve the accuracy of Web retrieval. We utilize the WordNet and WordNet SenseRelate All Words Software as main tools to preserve the semantics of the sentences of documents and user queries. Nouns and verbs in the WordNet are organized in the tree hierarchies. The word meanings are presented by numbers that reference to the nodes on the semantic tree. The meaning of each word in the sentence is calculated when the sentence is analyzed. The goal is to put each nou…
Extracting Semantic Knowledge from Unstructured Text Using Embedded Controlled Language
2016
Nowadays, most of the data on the Web is still in the form of unstructured text. Knowledge extraction from unstructured text is highly desirable but extremely challenging due to the inherent ambiguity of natural language. In this article, we present an architecture of an information extraction system based on the concept of Embedded Controlled Language that allows for extracting formal semantic knowledge from an unstructured text corpus. Moreover, the presented approach has a potential to support multilingual input and output.