WordNet: a lexical database for English
Communications of the ACM
Knowledge engineering: principles and methods
Data & Knowledge Engineering - Special jubilee issue: DKE 25
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Ontology Learning and Its Application to Automated Terminology Translation
IEEE Intelligent Systems
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
A cluster algorithm for graphs
A cluster algorithm for graphs
Learning Domain Ontologies from Document Warehouses and Dedicated Web Sites
Computational Linguistics
Enriching the knowledge sources used in a maximum entropy part-of-speech tagger
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Automated ontology construction for unstructured text documents
Data & Knowledge Engineering
Expert Systems with Applications: An International Journal
TextOntoEx: Automatic ontology construction from natural English text
Expert Systems with Applications: An International Journal
Learning non-taxonomic relationships from web documents for domain ontology construction
Data & Knowledge Engineering
Ontologies and the semantic web
Communications of the ACM - Surviving the data deluge
EKAW '08 Proceedings of the 16th international conference on Knowledge Engineering: Practice and Patterns
Integrating semantically heterogeneous aggregate views of distributed databases
Distributed and Parallel Databases
Ontology based personalized route planning system using a multi-criteria decision making approach
Expert Systems with Applications: An International Journal
IITAW '08 Proceedings of the 2008 International Symposium on Intelligent Information Technology Application Workshops
Expert Systems with Applications: An International Journal
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
SIGNUM: a graph algorithm for terminology extraction
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
RelExt: a tool for relation extraction from text in ontology extension
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
A quantitative comparison of the subgraph miners mofa, gspan, FFSM, and gaston
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Text2Onto: a framework for ontology learning and data-driven change discovery
NLDB'05 Proceedings of the 10th international conference on Natural Language Processing and Information Systems
A semantic retrieval framework for engineering domain knowledge
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
A semantic role labelling-based framework for learning ontologies from Spanish documents
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Extracting domain knowledge and taking its full advantage has been an important way to reducing costs and accelerating processes in domain-related applications. Domain ontology, providing a common and unambiguous understanding of a domain for both the users and the system to communicate with each other via a set of representational primitives, has been proposed as an important and natural approach to represent domain knowledge. Most domain knowledge about domain entities with their properties and relationships is embodied in document collections. Thus, extracting ontologies from these documents is an important means of ontology construction. In this paper, a graph-based approach for automatic construction of domain ontology from domain corpus, named GRAONTO, has been proposed. First, each document in the collection is represented by a graph. After the generation of document graphs, random walk term weighting is employed to estimate the relevance of the information of a term to the corpus from both local and global perspectives. Next, the MCL (Markov Clustering) algorithm is used to disambiguate terms with different meanings and group similar terms to produce concepts. Next, an improved gSpan algorithm constrained by both vertices and informativeness is exploited to find arbitrary latent relations among these concepts. Finally, the domain ontology is output in the OWL format. For ontology evaluation purposes, a method for adaptive adjustment of concepts and relations with respect to its practical effectiveness is conceived. Evaluation experiments show that GRAONTO is a promising approach for domain ontology construction.