3,085 research outputs found
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Visual support for ontology learning: an experience report
Ontology learning methods aim to automate ontology
construction. They are complex methods involving several
elements such as documents, terms and concepts. During the development of an ontology learning method, as well as during its deployment, several situations occur where
understanding the relations between these elements is crucial. Our hypothesis is that visual techniques can be used to aid this understanding. To support this claim, we present a set of such complex situations and describe the visual solutions that we developed to support them
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Ontology learning for semantic web services
Semantic Web Services promise automatic service discovery and composition, relying heavily on domain ontology as a core component. With large Web Service repository, manual ontology development is proving a bottleneck (with associated expense and likely errors) to the realisation of a semantic Web of services. Providing the appropriate tools that assist in and automate ontology development is essential for a dynamic service vision to be realised. As a statement of research-in-progress, this paper proposes combining different ontology learning paradigms in Web Services domain, highlighting the need for further research that accommodates the variation in Web Service descriptive and operational sources. A research agenda is proposed that recognises this variation in artefacts as they are selected, pre-processed and analyzed by ontology learning techniques
Towards unsupervised ontology learning from data
Data-driven elicitation of ontologies from structured data is a well-recognized knowledge acquisition bottleneck. The development of efficient techniques for (semi-)automating this task is therefore practically vital - yet, hindered by the lack of robust theoretical foundations. In this paper, we study the problem of learning Description Logic TBoxes from interpretations, which naturally translates to the task of ontology learning from data.In the presented framework, the learner is provided with a set of positive interpretations (i.e., logical models) of the TBox adopted by the teacher. The goal is to correctly identify the TBox given this input. We characterize the key constraints on the models that warrant finite learnability of TBoxes expressed in selected fragments of the Description Logic ε λ and define corresponding learning algorithms.This work was funded in part by the National Research Foundation under Grant no. 85482
Unsupervised Terminological Ontology Learning based on Hierarchical Topic Modeling
In this paper, we present hierarchical relationbased latent Dirichlet
allocation (hrLDA), a data-driven hierarchical topic model for extracting
terminological ontologies from a large number of heterogeneous documents. In
contrast to traditional topic models, hrLDA relies on noun phrases instead of
unigrams, considers syntax and document structures, and enriches topic
hierarchies with topic relations. Through a series of experiments, we
demonstrate the superiority of hrLDA over existing topic models, especially for
building hierarchies. Furthermore, we illustrate the robustness of hrLDA in the
settings of noisy data sets, which are likely to occur in many practical
scenarios. Our ontology evaluation results show that ontologies extracted from
hrLDA are very competitive with the ontologies created by domain experts
Semantic HMC for Big Data Analysis
Analyzing Big Data can help corporations to im-prove their efficiency. In
this work we present a new vision to derive Value from Big Data using a
Semantic Hierarchical Multi-label Classification called Semantic HMC based in a
non-supervised Ontology learning process. We also proposea Semantic HMC
process, using scalable Machine-Learning techniques and Rule-based reasoning
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