2006
DOI: 10.1016/j.knosys.2006.04.015
|View full text |Cite
|
Sign up to set email alerts
|

Ontology based text indexing and querying for the semantic web

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
26
0
3

Year Published

2007
2007
2024
2024

Publication Types

Select...
5
4
1

Relationship

0
10

Authors

Journals

citations
Cited by 73 publications
(29 citation statements)
references
References 21 publications
0
26
0
3
Order By: Relevance
“…Pancerz (2012b) proposed to consider attribute values describing objects in the ontological spaces, where ontologies are constructed on the basis of controlled vocabularies and the relationships of the concepts in the controlled vocabularies (cf. definitions given by Neches et al (1991) and Köhler et al (2006)). In that approach, we use formal representations of ontologies by means of graph structures.…”
Section: Simple Decision Systems Over Ontological Graphsmentioning
confidence: 99%
“…Pancerz (2012b) proposed to consider attribute values describing objects in the ontological spaces, where ontologies are constructed on the basis of controlled vocabularies and the relationships of the concepts in the controlled vocabularies (cf. definitions given by Neches et al (1991) and Köhler et al (2006)). In that approach, we use formal representations of ontologies by means of graph structures.…”
Section: Simple Decision Systems Over Ontological Graphsmentioning
confidence: 99%
“…Because this mapping not only formalize thetext message [7]- [10], but also provides other algorithms with a formal basis to process the text and its message according to the knowledge formalized in the ontology [1], [11], [12]. Regardless of secondary processing, the main purpose of the matching document to lexical resources is to solve the problem of natural language ambiguity [9], [10], [13]. In this regard, wide varieties of supervised and unsupervised word sense disambiguation methods have been applied to determine to which entry of a lexical resource a word refers.…”
Section: Related Workmentioning
confidence: 99%
“…In our experience, in the field of the multimedia information retrieval, ontologies are the best option for the implementation of the SMD KB core. This is due to the following reasons: On the Query processing [1] Query expansion [8] Query mapping and federation [50] Index creation Video indexing [44] Text indexing [21] Image indexing [51] Multimodal indexing [43] Support for manual annotation [34] Semantic modelling of multimedia standards MPEG-7, DMS-1 [49] Ranking Semantic based ranking [41] Document organization [19] Relevant documents…”
Section: Semantic Middleware Knowledge Basementioning
confidence: 99%