Shiva: A Framework for Graph Based Ontology Matching

@article{Mathur2014ShivaAF,
  title={Shiva: A Framework for Graph Based Ontology Matching},
  author={Iti Mathur and Nisheeth Joshi and Hemant Darbari and Ajai Kumar},
  journal={ArXiv},
  year={2014},
  volume={abs/1403.7465}
}
Since long, corporations are looking for knowledge sources which can provide structured description of data and can focus on meaning and shared understanding. Structures which can facilitate open world assumptions and can be flexible enough to incorporate and recognize more than one name for an entity. A source whose major purpose is to facilitate human communication and interoperability. Clearly, databases fail to provide these features and ontologies have emerged as an alternative choice, but… 

Figures and Tables from this paper

Shiva++: An Enhanced Graph based Ontology Matcher
TLDR
The development of one such matcher which merges the concepts available in two ontologies at two levels; 1) at string level and 2) at semantic level; thus producing better merged ontologies.
Shiva++_CL: An Enhanced Graph based Cross Lingual Ontology Matcher
TLDR
This paper shows the development of cross lingual ontology matcher at two levels:1) at String level and 2) Semantic level.
A framework to aggregate multiple ontology matchers
TLDR
A computational solution for ontology meta-matching (OMM) and a framework designed for developers to make use of alignment techniques in their applications that is able to adapt to different test cases.
A weighted graph-oriented ontology matching algorithm for enhancing ontology mapping and alignment in Semantic Web
TLDR
The pragmatic nature of this matching solution is an advantage which approaches the task in a simplistic and efficient manner and provides some room for the manual decision making which can be efficient in deciding matching levels in certain hesitant matches.
CROSS LINGUAL ONTOLOGY MATCHING BASED ON FUZZY SYNTACTIC MATCHING
  • Shubhi Shrivastava
  • Computer Science
    International Journal of Advanced Research in Computer Science
  • 2020
TLDR
A way to deal with take care of the issue of multilingualism on the semantic web, in view of Syntactic matching is presented.
Enriching user model ontology for handicraft domain by FOAF
TLDR
This paper proposes an approach for user model ontology construction, and proposes two algorithms for matching and merging ontologies so as to achieve the enrichment with FOAF.
Ontology Matching Using BabelNet Dictionary and Word Sense Disambiguation Algorithms
TLDR
Overall, the most effective methods are Wu & Palmer and Adapted Lesk, which is widely used for Word Sense Disambiguation (WSD) in the field of Automatic Natural Language Processing (NLP).
Comparison of Ontology Alignment Systems Across Single Matching Task Via the McNemar’s Test
TLDR
The statistical procedures that enable us to theoretically favor one system over one another are proposed and applied to the systems participated in the OAEI 2016 anatomy track, and also compares several well-known similarity metrics for the same matching problem.
Comparison of ontology alignment algorithms across single matching task via the McNemar test
TLDR
Investigation on the methods participated in the anatomy track of OAEI 2016 demonstrates that AML and CroMatcher are the top two methods and DKP-AOM and Alin are the boŠom two ones.
...
...

References

SHOWING 1-10 OF 23 REFERENCES
Ontology Matching with CIDER: Evaluation Report for the OAEI 2008
TLDR
A schema-based alignment algorithm compares each pair of ontology terms by extracting their ontological contexts up to a certain depth and combining different elementary ontology matching techniques, which shows a very good behaviour in terms of precision, while preserving an acceptable recall.
LogMap: Logic-Based and Scalable Ontology Matching
TLDR
This paper presents LogMap--a highly scalable ontology matching system with 'built-in' reasoning and diagnosis capabilities, and is the only matching system that can deal with semantically rich ontologies containing tens (and even hundreds of thousands of classes).
RiMOM: A Dynamic Multistrategy Ontology Alignment Framework
TLDR
This paper presents a dynamic multistrategy ontology alignment framework, named RiMOM, and proposes a systematic approach to quantitatively estimate the similarity characteristics for each alignment task and a strategy selection method to automatically combine the matching strategies based on two estimated factors.
Ontology matching with CIDER: evaluation report for OAEI 2011
TLDR
In this new approach, the burden of manual selection of weights has been definitely eliminated, while preserving the performance with respect to CIDER’s previous participation in the benchmark track (at OAEI’08), and areas of potential improvement are discovered.
Using AgreementMaker to align ontologies for OAEI 2010
TLDR
The participation of AgreementMaker in the 2011 OAEI competition in four tracks is described, with the goal to explore previously unused features of the ontologies in order to improve the matching results.
DOMAIN ONTOLOGY DEVELOPMENT FOR COMMUNICABLE DISEASES
TLDR
The development, verification and validation of an ontology in a health domain is discussed and a mechanism which is strongly backed by a sound inference system is proposed.
YAM++ : A Multi-strategy Based Approach for Ontology Matching Task
TLDR
The capability of the ontology matching tool YAM++ is presented, which shows that it is able to discover mappings between entities of given two ontologies by using machine learning approach and it is shown that it can deal with multi-lingual ontologies matching problem.
Using AgreementMaker to align Ontologies for OAEI 2009: Overview, Results, and Outlook
This paper describes our participation in the Ontology Alignment Evaluation Initiative (OAEI) 2009 with the AgreementMaker system for ontology matching, in which we obtained excellent results. In
TaxoMap alignment and refinement modules: results for OAEI 2010
TLDR
This new implementation of taxoMap uses a pattern-based approach implemented in the TaxoMap Framework helping an engineer to refine mappings to take into account specific conventions used in ontologies.
OntoAna: Domain Ontology for Human Anatomy
TLDR
An ontology on human anatomy is developed, which captures information regarding cardiovascular system, digestive system, skeleton and nervous system, which can be used by people working in medical and health care domain.
...
...