53,309 research outputs found
Intelligent information processing in a digital library using semantic web
With the explosive growth of information, it is
becoming increasingly difficult to retrieve the relevant
documents with current search engine only. The
information is treated as an ordinary database that
manages the contents and positions. To the individual
user, there is a great deal of useless information in
addition to the substantial amount of useful information.
This begets new challenges to docent community
and motivates researchers to look for intelligent
information retrieval approach and ontologies that
search and/or filter information automatically based on
some higher level of understanding are required. We
study improving the efficiency of search methods and
classify the search patrons into several models based on
the profiles of agent based on ontology.
We have proposed a method to efficiently search for
the target information on a Digital Library network with
multiple independent information sources. This paper
outlines the development of an expert prototype system
based in an ontology for retrieval information of the
Digital Library University of Seville. The results of this
study demonstrate that by improving representation by
incorporating more metadata from within the
information and the ontology into the retrieval process,
the effectiveness of the information retrieval is enhanced.
We used Jcolibri and Prótége for developing the
ontology and creation the expert system respectively
A framework for investigating the interaction in information retrieval
To increase retrieval effectiveness, information retrieval systems must offer better supports to users in their information seeking activities. To achieve this, one major concern is to obtain a better understanding of the nature of the interaction between a user and an information retrieval system. For this, we need a means to analyse the interaction in information retrieval, so as to compare the interaction processes within and across information retrieval systems. We present a framework for investigating the interaction between users and information retrieval systems. The framework is based on channel theory, a theory of information and its flow, which provides an explicit ontology that can be used to represent any aspect of the interaction process. The developed framework allows for the investigation of the interaction in information retrieval at the desired level of abstraction. We use the framework to investigate the interaction in relevance feedback and standard web search
Ontology Based E-Healthcare Information Retrieval System: A Semantic Approach
With the increase of data in the health care system provides a base for the development of an effective information retrieval system. The implementation of such information retrieval system integrates the heterogeneous information from the healthcare environment. Most of the existing information retrieval systems are syntactic based systems, which will provide inefficient results for the search queries. The objective of this approach is to design a semantic based E-Healthcare information retrieval system. The proposed approach uses an ontology to define the disease-treatment information and will be used for the effective information retrieval. The designated approach is evaluated with a web based tool and the results shows that there is an improvement in the approach
A Generalized Framework for Ontology-Based Information Retrieval Application to a public-transportation system
In this paper we present a generic framework for ontology-based information
retrieval. We focus on the recognition of semantic information extracted from
data sources and the mapping of this knowledge into ontology. In order to
achieve more scalability, we propose an approach for semantic indexing based on
entity retrieval model. In addition, we have used ontology of public
transportation domain in order to validate these proposals. Finally, we
evaluated our system using ontology mapping and real world data sources.
Experiments show that our framework can provide meaningful search results
Enhanced SPARQL-based design rationale retrieval
Design rationale (DR) is an important category within design knowledge, and effective reuse of it depends on its successful retrieval. In this paper, an ontology-based DR retrieval approach is presented, which allows users to search by entering normal queries such as questions in natural language. First, an ontology-based semantic model of DR is developed based on the extended issue-based information system-based DR representation in order to effectively utilize the semantics embedded in DR, and a database of ontology-based DR is constructed, which supports SPARQL queries. Second, two SPARQL query generation methods are proposed. The first method generates initial SPARQL queries from natural language queries automatically using template matching, and the other generates initial SPARQL queries automatically from DR record-based queries. In addition, keyword extension and optimization is conducted to enhance the SPARQL-based retrieval. Third, a design rationale retrieval prototype system is implemented. The experimental results show the advantages of the proposed approach
Ontology extraction for index generation
The administration of electronic publication in the Information Era congregates old and new problems,
especially those related with Information Retrieval and Automatic Knowledge Extraction. This article
presents an Information Retrieval System that uses Natural Language Processing and Ontology to
index collection s texts. We describe a system that constructs a domain specific ontology, starting
from the syntactic and semantic analyses of the texts that compose the collection. First the texts are
tokenized, then a robust syntactic analysis is made, subsequently the semantic analysis is accomplished
in conformity with a metalanguage of knowledge representation, based on a basic ontology composed
of 47 classes. The ontology, automatically extracted, generates richer domain specific knowledge.
It propitiates, through its semantic net, the right conditions for the user to find with larger efficiency
and agility the terms adapted for the consultation to the texts. A prototype of this system was built
and used for the indexation of a collection of 221 electronic texts of Information Science written in
Portuguese from Brazil. Instead of being based in statistical theories, we propose a robust Information Retrieval System that uses cognitive theories, allowing a larger efficiency in the answer to the users' queries
Geographical information retrieval with ontologies of place
Geographical context is required of many information retrieval tasks in which the target of the search may be documents, images or records which are referenced to geographical space only by means of place names. Often there may be an imprecise match between the query name and the names associated with candidate sources of information. There is a need therefore for geographical information retrieval facilities that can rank the relevance of candidate information with respect to geographical closeness of place as well as semantic closeness with respect to the information of interest. Here we present an ontology of place that combines limited coordinate data with semantic and qualitative spatial relationships between places. This parsimonious model of geographical place supports maintenance of knowledge of place names that relate to extensive regions of the Earth at multiple levels of granularity. The ontology has been implemented with a semantic modelling system linking non-spatial conceptual hierarchies with the place ontology. An hierarchical spatial distance measure is combined with Euclidean distance between place centroids to create a hybrid spatial distance measure. This is integrated with thematic distance, based on classification semantics, to create an integrated semantic closeness measure that can be used for a relevance ranking of retrieved objects
Digital Repositories and the Semantic Web: Semantic Search and Navigation for DSpace
4th International Conference on Open RepositoriesThis presentation was part of the session : DSpace User Group PresentationsDate: 2009-05-21 08:30 AM – 10:00 AMIn many digital repository implementations, resources are often described against some flavor of metadata schema, popularly the Dublin Core Element Set (DCMES), as is the case with the DSpace system. However, such an approach cannot capture richer semantic relations that exist or may be implied, in the sense of a Semantic Web ontology. Therefore we first suggest a method in order to semantically intensify the underlying data model and develop an automatic translation of the flatly organized metadata information to this new ontology. Then we propose an implementation that provides for inference-based knowledge discovery, retrieval and navigation on top of digital repositories, based on this ontology. We apply this technique to real information stored in the University of Patras Institutional Repository that is based on DSpace, and confirm that more powerful, inference-based queries can indeed be performed
Multi modal multi-semantic image retrieval
PhDThe rapid growth in the volume of visual information, e.g. image, and video can
overwhelm users’ ability to find and access the specific visual information of interest
to them. In recent years, ontology knowledge-based (KB) image information retrieval
techniques have been adopted into in order to attempt to extract knowledge from these
images, enhancing the retrieval performance. A KB framework is presented to
promote semi-automatic annotation and semantic image retrieval using multimodal
cues (visual features and text captions). In addition, a hierarchical structure for the KB
allows metadata to be shared that supports multi-semantics (polysemy) for concepts.
The framework builds up an effective knowledge base pertaining to a domain specific
image collection, e.g. sports, and is able to disambiguate and assign high level
semantics to ‘unannotated’ images.
Local feature analysis of visual content, namely using Scale Invariant Feature
Transform (SIFT) descriptors, have been deployed in the ‘Bag of Visual Words’
model (BVW) as an effective method to represent visual content information and to
enhance its classification and retrieval. Local features are more useful than global
features, e.g. colour, shape or texture, as they are invariant to image scale, orientation
and camera angle. An innovative approach is proposed for the representation,
annotation and retrieval of visual content using a hybrid technique based upon the use
of an unstructured visual word and upon a (structured) hierarchical ontology KB
model. The structural model facilitates the disambiguation of unstructured visual
words and a more effective classification of visual content, compared to a vector
space model, through exploiting local conceptual structures and their relationships.
The key contributions of this framework in using local features for image
representation include: first, a method to generate visual words using the semantic
local adaptive clustering (SLAC) algorithm which takes term weight and spatial
locations of keypoints into account. Consequently, the semantic information is
preserved. Second a technique is used to detect the domain specific ‘non-informative
visual words’ which are ineffective at representing the content of visual data and
degrade its categorisation ability. Third, a method to combine an ontology model with
xi
a visual word model to resolve synonym (visual heterogeneity) and polysemy
problems, is proposed. The experimental results show that this approach can discover
semantically meaningful visual content descriptions and recognise specific events,
e.g., sports events, depicted in images efficiently.
Since discovering the semantics of an image is an extremely challenging problem, one
promising approach to enhance visual content interpretation is to use any associated
textual information that accompanies an image, as a cue to predict the meaning of an
image, by transforming this textual information into a structured annotation for an
image e.g. using XML, RDF, OWL or MPEG-7. Although, text and image are distinct
types of information representation and modality, there are some strong, invariant,
implicit, connections between images and any accompanying text information.
Semantic analysis of image captions can be used by image retrieval systems to
retrieve selected images more precisely. To do this, a Natural Language Processing
(NLP) is exploited firstly in order to extract concepts from image captions. Next, an
ontology-based knowledge model is deployed in order to resolve natural language
ambiguities. To deal with the accompanying text information, two methods to extract
knowledge from textual information have been proposed. First, metadata can be
extracted automatically from text captions and restructured with respect to a semantic
model. Second, the use of LSI in relation to a domain-specific ontology-based
knowledge model enables the combined framework to tolerate ambiguities and
variations (incompleteness) of metadata. The use of the ontology-based knowledge
model allows the system to find indirectly relevant concepts in image captions and
thus leverage these to represent the semantics of images at a higher level.
Experimental results show that the proposed framework significantly enhances image
retrieval and leads to narrowing of the semantic gap between lower level machinederived
and higher level human-understandable conceptualisation
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