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160
The nested chinese restaurant process and bayesian inference of topic hierarchies
, 2007
"... We present the nested Chinese restaurant process (nCRP), a stochastic process which assigns probability distributions to infinitely-deep, infinitely-branching trees. We show how this stochastic process can be used as a prior distribution in a Bayesian nonparametric model of document collections. Spe ..."
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Cited by 128 (15 self)
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We present the nested Chinese restaurant process (nCRP), a stochastic process which assigns probability distributions to infinitely-deep, infinitely-branching trees. We show how this stochastic process can be used as a prior distribution in a Bayesian nonparametric model of document collections. Specifically, we present an application to information retrieval in which documents are modeled as paths down a random tree, and the preferential attachment dynamics of the nCRP leads to clustering of documents according to sharing of topics at multiple levels of abstraction. Given a corpus of documents, a posterior inference algorithm finds an approximation to a posterior distribution over trees, topics and allocations of words to levels of the tree. We demonstrate this algorithm on collections of scientific abstracts from several journals. This model exemplifies a recent trend in statistical machine learning—the use of Bayesian nonparametric methods to infer distributions on flexible data structures.
On How to Perform a Gold Standard Based Evaluation of Ontology Learning
- In Proceedings of the 5th International Semantic Web Conference (ISWC’06
, 2006
"... Abstract. In recent years several measures for the gold standard based evaluation of ontology learning were proposed. They can be distinguished by the layers of an ontology (e.g. lexical term layer and concept hierarchy) they evaluate. Judging those measures with a list of criteria we show that ther ..."
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Cited by 59 (2 self)
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Abstract. In recent years several measures for the gold standard based evaluation of ontology learning were proposed. They can be distinguished by the layers of an ontology (e.g. lexical term layer and concept hierarchy) they evaluate. Judging those measures with a list of criteria we show that there exist some measures sufficient for evaluating the lexical term layer. However, existing measures for the evaluation of concept hierarchies fail to meet basic criteria. This paper presents a new taxonomic measure which overcomes the problems of current approaches. 1
Ontology Learning from Text: An Overview
- In Paul Buitelaar, P., Cimiano, P., Magnini B. (Eds.), Ontology Learning from Text: Methods, Applications and Evaluation
, 2005
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Efficient unsupervised discovery of word categories using symmetric patterns and high frequency words
- COLING-ACL ’06
, 2006
"... We present a novel approach for discovering word categories, sets of words sharing a significant aspect of their meaning. We utilize meta-patterns of highfrequency words and content words in order to discover pattern candidates. Symmetric patterns are then identified using graph-based measures, and ..."
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Cited by 38 (14 self)
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We present a novel approach for discovering word categories, sets of words sharing a significant aspect of their meaning. We utilize meta-patterns of highfrequency words and content words in order to discover pattern candidates. Symmetric patterns are then identified using graph-based measures, and word categories are created based on graph clique sets. Our method is the first pattern-based method that requires no corpus annotation or manually provided seed patterns or words. We evaluate our algorithm on very large corpora in two languages, using both human judgments and WordNet-based evaluation. Our fully unsupervised results are superior to previous work that used a POS tagged corpus, and computation time for huge corpora are orders of magnitude faster than previously reported.
Ontology-driven information extraction with OntoSyphon
- In: Proceedings of the 5th International Semantic Web Conference (ISWC 2006). Volume 4273 of LNCS., Athens, GA, Springer (2006) 428 – 444
, 2006
"... The Semantic Web’s need for machine understandable content has led researchers to attempt to automatically acquire such content from a number of sources, including the web. To date, such research has focused on “document-driven” systems that individually process a small set of documents, annotating ..."
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Cited by 25 (1 self)
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The Semantic Web’s need for machine understandable content has led researchers to attempt to automatically acquire such content from a number of sources, including the web. To date, such research has focused on “document-driven” systems that individually process a small set of documents, annotating each with respect to a given ontology. This paper introduces OntoSyphon, an alternative that strives to more fully leverage existing ontological content while scaling to extract comparatively shallow content from millions of documents. OntoSyphon operates in an “ontology-driven” manner: taking any ontology as input, OntoSyphon uses the ontology to specify web searches that identify possible semantic instances, relations, and taxonomic information. Redundancy in the web, together with information from the ontology, is then used to automatically verify these candidate instances and relations, enabling OntoSyphon to operate in a fully automated, unsupervised manner. A prototype of OntoSyphon is fully implemented and we present experimental results that demonstrate substantial instance learning in a variety of domains based on independently constructed ontologies. We also introduce new methods for improving instance verification, and demonstrate that they improve upon previously known techniques.
Formal Concept Analysis: A unified framework for building and refining ontologies
, 2008
"... Building a domain ontology usually requires several resources of different types, e.g. thesaurus, object taxonomies, terminologies, databases, sets of documents, etc, where objects are described in terms of attributes and relations with other objects. One important and hard problem is to be able to ..."
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Cited by 21 (4 self)
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Building a domain ontology usually requires several resources of different types, e.g. thesaurus, object taxonomies, terminologies, databases, sets of documents, etc, where objects are described in terms of attributes and relations with other objects. One important and hard problem is to be able to combine and merge knowledge units extracted from these different resources within an homogeneous formal representation (such as a description logic or OWL). The purpose of this article is to show which kinds of resources should be available for designing a real-world ontology in a given application domain, and then how Formal Concept Analysis and its extension- Relational Concept Analysis- can be used for materializing an associated ontology. This resulting target ontology can then be encoded within OWL or a description logic formalism, allowing classification-based reasoning. A real-world example in microbiology is detailed. Finally, an evaluation including tests on recall and precision shows how source resources can be completed with other existing domain resources using a semi-automatic analysis process.
Constructing folksonomies from user-specified relations on flickr
- In Proc. of 18th International World Wide Web Conference, WWW ’09
, 2009
"... Automatic folksonomy construction from tags has attracted much attention recently. However, inferring hierarchical relations between concepts from tags has a drawback in that it is difficult to distinguish between more popular and more general concepts. Instead of tags we propose to use userspecifie ..."
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Cited by 19 (6 self)
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Automatic folksonomy construction from tags has attracted much attention recently. However, inferring hierarchical relations between concepts from tags has a drawback in that it is difficult to distinguish between more popular and more general concepts. Instead of tags we propose to use userspecified relations for learning folksonomy. We explore two statistical frameworks for aggregating many shallow individual hierarchies, expressed through the collection/set relations on the social photosharing site Flickr, into a common deeper folksonomy that reflects how a community organizes knowledge. Our approach addresses a number of challenges that arise while aggregating information from diverse users, namely noisy vocabulary, and variations in the granularity level of the concepts expressed. Our second contribution is a method for automatically evaluating learned folksonomy by comparing it to a reference taxonomy, e.g., the Web directory created by the Open Directory Project. Our empirical results suggest that user-specified relations are a good source of evidence for learning folksonomies.
Ontologies on Demand? A Description of the State-of-the-Art, Applications, Challenges and Trends for Ontology Learning from Text
, 2006
"... Ontologies are nowadays used for many applications requiring data, services and resources in general to be interoperable and machine understandable. Such applications are for example web service discovery and composition, information integration across databases, intelligent search, etc. The general ..."
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Cited by 18 (1 self)
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Ontologies are nowadays used for many applications requiring data, services and resources in general to be interoperable and machine understandable. Such applications are for example web service discovery and composition, information integration across databases, intelligent search, etc. The general idea is that data and services are semantically described with respect to ontologies, which are formal specifications of a domain of interest, and can thus be shared and reused in a way such that the shared meaning specified by the ontology remains formally the same across different parties and applications. As the cost of creating ontologies is relatively high, different proposals have emerged for learning ontologies from structured and unstructured resources. In this article we examine the maturity of techniques for ontology learning from textual resources, addressing the question whether the state-of-the-art is mature enough to produce ontologies ‘on demand’.
Toward a fuzzy domain ontology extraction method for adaptive e-learning
- Knowledge and Data Engineering, IEEE Transactions on
, 2009
"... and other research outputs Towards a fuzzy domain ontology extraction method for adaptive e-learning ..."
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Cited by 18 (3 self)
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and other research outputs Towards a fuzzy domain ontology extraction method for adaptive e-learning
Ontolearn Reloaded: A graph-based algorithm for taxonomy induction
, 2012
"... In 2004 we published in this journal an article describing OntoLearn, one of the first systems to automatically induce a taxonomy from documents and Web sites. Since then, OntoLearn has continued to be an active area of research in our group and has become a reference work within the community. 1 In ..."
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Cited by 17 (6 self)
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In 2004 we published in this journal an article describing OntoLearn, one of the first systems to automatically induce a taxonomy from documents and Web sites. Since then, OntoLearn has continued to be an active area of research in our group and has become a reference work within the community. 1 In this paper we describe our next-generation taxonomy learning methodology, which we name OntoLearn Reloaded. Unlike many taxonomy learning approaches in the literature, our novel algorithm learns both concepts and relations entirely from scratch via the automated extraction of terms, definitions and hypernyms. This results in a very dense, cyclic and potentially disconnected hypernym graph. The algorithm then induces a taxonomy from this graph via optimal branching and a novel weighting policy. Our experiments show that we obtain high-quality results, both when building brand-new taxonomies and when reconstructing subhierarchies of existing taxonomies. 1.
