Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Deriving concept hierarchies from text
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Ontology-supported and ontology-driven conceptual navigation on the World Wide Web
HYPERTEXT '00 Proceedings of the eleventh ACM on Hypertext and hypermedia
Machine Learning
Modern Information Retrieval
An Information Theoretic Approach to Rule Induction from Databases
IEEE Transactions on Knowledge and Data Engineering
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
GETESS - Searching the Web Exploiting German Texts
CIA '99 Proceedings of the Third International Workshop on Cooperative Information Agents III
Learning probabilistic models of link structure
The Journal of Machine Learning Research
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Introduction to the special issue on link mining
ACM SIGKDD Explorations Newsletter
Speech and Language Processing (2nd Edition)
Speech and Language Processing (2nd Edition)
A Novel Combination of Answer Set Programming with Description Logics for the Semantic Web
ESWC '07 Proceedings of the 4th European conference on The Semantic Web: Research and Applications
Learning concept hierarchies from text corpora using formal concept analysis
Journal of Artificial Intelligence Research
Using verbs to characterize noun-noun relations
AIMSA'06 Proceedings of the 12th international conference on Artificial Intelligence: methodology, Systems, and Applications
Capturing knowledge about philosophy
Proceedings of the 4th international conference on Knowledge capture
InPhO: a system for collaboratively populating and extending a dynamic ontology
Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries
Crowdsourcing the assembly of concept hierarchies
Proceedings of the 10th annual joint conference on Digital libraries
Thesaurus extension using web search engines
ICADL'10 Proceedings of the role of digital libraries in a time of global change, and 12th international conference on Asia-Pacific digital libraries
Ontology based information extraction from text
Knowledge-driven multimedia information extraction and ontology evolution
Deep Web Information Retrieval Process: A Technical Survey
International Journal of Information Technology and Web Engineering
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The successful deployment of digital technologies by humanities scholars presents computer scientists with a number of unique scientific and technological challenges. The task seems particularly daunting because issues in the humanities are presented in abstract language demanding the kind of subtle interpretation often thought to be beyond the scope of artificial intelligence, and humanities scholars themselves often disagree about the structure of their disciplines. The future of humanities computing depends on having tools for automatically discovering complex semantic relationships among different parts of a corpus. Digital library tools for the humanities will need to be capable of dynamically tracking the introduction of new ideas and interpretations and applying them to older texts in ways that support the needs of scholars and students. This paper describes the design of new algorithms and the adjustment of existing algorithms to support the automated and semi-automated management of domain-rich metadata for an established digital humanities project, the Stanford Encyclopedia of Philosophy. Our approach starts with a "hand-built" formal ontology that is modified and extended by a combination of automated and semi-automated methods, thus becoming a "dynamic ontology". We assess the suitability of current information retrieval and information extraction methods for the task of automatically maintaining the ontology. We describe a novel measure of term-relatedness that appears to be particularly helpful for predicting hierarchical relationships in the ontology. We believe that our project makes a further contribution to information science by being the first to harness the collaboration inherent in a expert-maintained dynamic reference work to the task of maintaining and verifying a formal ontology. We place special emphasis on the task of bringing domain expertise to bear on all phases of the development and deployment of the system, from the initial design of the software and ontology to its dynamic use in a fully operational digital reference work.