WordNet: a lexical database for English
Communications of the ACM
Communications of the ACM
Outline for a Logical Theory of Adaptive Systems
Journal of the ACM (JACM)
Natural language processing and knowledge representation
Artficial Immune Systems and Their Applications
Artficial Immune Systems and Their Applications
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Measuring Similarity between Ontologies
EKAW '02 Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web
The Evolution of Emergent Organization in Immune System Gene Libraries
Proceedings of the 6th International Conference on Genetic Algorithms
DODDLE: A Domain Ontology Rapid Development Environment
PRICAI '98 Proceedings of the 5th Pacific Rim International Conference on Artificial Intelligence: Topics in Artificial Intelligence
An Evolutionary Immune Network for Data Clustering
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
Ontological Engineering
Noun classification from predicate-argument structures
ACL '90 Proceedings of the 28th annual meeting on Association for Computational Linguistics
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
An Immune Network Approach for Web Document Clustering
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Measures of distributional similarity
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Automatic construction of a hypernym-labeled noun hierarchy from text
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Developing Semantic Web Services
Developing Semantic Web Services
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
A performance comparison of PSO and GA in scheduling hybrid flow-shops with multiprocessor tasks
Proceedings of the 2008 ACM symposium on Applied computing
A Model to Optimize DNA Sequences Based on Particle Swarm Optimization
AMS '08 Proceedings of the 2008 Second Asia International Conference on Modelling & Simulation (AMS)
A Hybrid Approach to Ontology Relationship Learning
NLDB '08 Proceedings of the 13th international conference on Natural Language and Information Systems: Applications of Natural Language to Information Systems
An Exploratory Study on Malay Processing Tool for Acquisition of Taxonomy Using FCA
ISDA '08 Proceedings of the 2008 Eighth International Conference on Intelligent Systems Design and Applications - Volume 01
Automatic Extraction of Structurally Coherent Mini-Taxonomies
ER '08 Proceedings of the 27th International Conference on Conceptual Modeling
A collaborative filtering method based on artificial immune network
Expert Systems with Applications: An International Journal
Research of Coal-Gas Outburst Forecasting Based on Artificial Immune Network Clustering Model
WKDD '09 Proceedings of the 2009 Second International Workshop on Knowledge Discovery and Data Mining
A Hybrid Approach for Learning Concept Hierarchy from Malay Text Using GAHC and Immune Network
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
Learning concept hierarchies from text corpora using formal concept analysis
Journal of Artificial Intelligence Research
Immune inspired somatic contiguous hypermutation for function optimisation
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Is shallow parsing useful for unsupervised learning of semantic clusters?
CICLing'03 Proceedings of the 4th international conference on Computational linguistics and intelligent text processing
Concept hierarchy construction by combining spectral clustering and subsumption estimation
WISE'06 Proceedings of the 7th international conference on Web Information Systems
What have gene libraries done for AIS?
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
An artificial immune system architecture for computer securityapplications
IEEE Transactions on Evolutionary Computation
Learning of semantic sibling group hierarchies - K-means vs. bi-secting-K-means
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
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A concept hierarchy is an integral part of an ontology but it is expensive and time consuming to build. Motivated by this, many unsupervised learning methods have been proposed to (semi-) automatically develop a concept hierarchy. A significant work is the Guided Agglomerative Hierarchical Clustering (GAHC) which relies on linguistic patterns (i.e., hypernyms) to guide the clustering process. However, GAHC still relies on contextual features to build the concept hierarchy, thus data sparsity still remains an issue in GAHC. Artificial Immune Systems are known for robustness, noise tolerance and adaptability. Thus, an extension to the GAHC is proposed by hybridizing it with Artificial Immune Network (aiNet) which we call Guided Clustering and aiNet for Learning Concept Hierarchy (GCAINY). In this paper, we have tested GCAINY using two parameter settings. The first parameter setting is obtained from the literature as a baseline parameter setting and second is by automatic parameter tuning using Particle Swarm Optimization (PSO). The effectiveness of the GCAINY is evaluated on three data sets. For further validations, a comparison between GCAINY and GAHC has been conducted and with statistical tests showing that GCAINY increases the quality of the induced concept hierarchy. The results reveal that the parameters value found by using PSO significantly produce better concept hierarchy than the vanilla parameter. Thus it can be concluded that the proposed approach has greater ability to be used in the field of ontology learning.