An Algorithm Based on Counterfactuals for Concept Learning in the Semantic Web

Iannone, Luigi; Palmisano, Ignazio

doi:10.1007/11504894_53

Cited by 33 publications

(54 citation statements)

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“…Then we can write: 21 This is exactly the same that we have done twice in the outer induction above. 22 Or, better, ∀E, E C : ∃E ∈ ρ * (C) ∧ E ≡ E .…”

Section: Appendix: Proofsmentioning

confidence: 97%

“…In such a case, the current concept definition ParGen has to be specialized. Differently from previous works [14,15,22] in which YinYang had only one strategy to specialize based on counterfactuals, we endowed it with another one implementing basically the theoretical Add conjunct ρ E (see Def. 4.12).…”

Section: Yinyangmentioning

confidence: 99%

“…Proof: We should show that, given C a concept in ALC, ∀E, E C : E ∈ ρ * (C). 22 Suppose, without loss of generality, that C is in ALCnormal form. From Def.…”

Section: Appendix: Proofsmentioning

confidence: 99%

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An algorithm based on counterfactuals for concept learning in the Semantic Web

2007

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In the line of realizing the Semantic-Web by means of mechanized practices, we tackle the problem of ontology building ontologies, assisting the\ud knowledge engineers’ job by means of Machine Learning techniques. In particular, we investigate on solutions for the induction of concept descriptions in a semi-automatic fashion. In particular we present an algorithm that is able to infer definitions in the ALC Description Logic (a sub-language of OWL-DL) from instances made available by domain experts. The effectiveness of the method with respect to past algorithms is also empirically evaluated with an experimentation in the document image understanding domain

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“…Then we can write: 21 This is exactly the same that we have done twice in the outer induction above. 22 Or, better, ∀E, E C : ∃E ∈ ρ * (C) ∧ E ≡ E .…”

Section: Appendix: Proofsmentioning

confidence: 97%

Section: Yinyangmentioning

confidence: 99%

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An algorithm based on counterfactuals for concept learning in the Semantic Web

2007

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“…Indeed, the most burdensome related maintenance tasks such as ontology construction, refinement and evolution, enabling the SW applications demand for such automation. These tasks can be assisted by specific supervised [5,30,22,3] or unsupervised learning methods [26,16,13].…”

Section: Conceptual Clustering For the Semantic Webmentioning

confidence: 99%

Metric-based stochastic conceptual clustering for ontologies

Fanizzi

d’Amato

Esposito

2009

Information Systems

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“…More precisely, ML may serve the SW by supporting ontology construction and management, ontology evaluation, ontology refinement, ontology evolution, as well as the mapping, merging and alignment of ontologies (Bloehdorn et al 2006;Euzenat and Shvaiko 2007;Grobelnik and Mladenic 2006;Maedche and Staab 2004). Another task is learning logical constraints formulated in the language of the employed ontology (Cohen and Hirsh 1994;Fanizzi et al 2008a;Iannone et al 2007;Lehmann and Hitzler 2008;Lehmann 2009;Lisi and Esposito 2005). In all these tasks, machine learning needs to produce deterministic logical statements by using, e.g., methods from inductive logic programming.…”

Section: Introductionmentioning

confidence: 98%

Mining the Semantic Web

Rettinger

Lösch

Tresp

et al. 2012

Data Min Knowl Disc

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In the Semantic Web vision of the World Wide Web, content will not only be accessible to humans but will also be available in machine interpretable form as ontological knowledge bases. Ontological knowledge bases enable formal querying and reasoning and, consequently, a main research focus has been the investigation of how deductive reasoning can be utilized in ontological representations to enable more advanced applications. However, purely logic methods have not yet proven to be very effective for several reasons: First, there still is the unsolved problem of scalability of reasoning to Web scale. Second, logical reasoning has problems with uncertain information, which is abundant on Semantic Web data due to its distributed and heterogeneous nature. Third, the construction of ontological knowledge bases suitable for advanced reasoning techniques is complex, which ultimately results in Responsible editor: 123 614 A. Rettinger et al.a lack of such expressive real-world data sets with large amounts of instance data. From another perspective, the more expressive structured representations open up new opportunities for data mining, knowledge extraction and machine learning techniques. If moving towards the idea that part of the knowledge already lies in the data, inductive methods appear promising, in particular since inductive methods can inherently handle noisy, inconsistent, uncertain and missing data. While there has been broad coverage of inducing concept structures from less structured sources (text, Web pages), like in ontology learning, given the problems mentioned above, we focus on new methods for dealing with Semantic Web knowledge bases, relying on statistical inference on their standard representations. We argue that machine learning research has to offer a wide variety of methods applicable to different expressivity levels of Semantic Web knowledge bases: ranging from weakly expressive but widely available knowledge bases in RDF to highly expressive first-order knowledge bases, this paper surveys statistical approaches to mining the Semantic Web. We specifically cover similarity and distance-based methods, kernel machines, multivariate prediction models, relational graphical models and first-order probabilistic learning approaches and discuss their applicability to Semantic Web representations. Finally, we present selected experiments which were conducted on Semantic Web mining tasks for some of the algorithms presented before. This is intended to show the breadth and general potential of this exiting new research and application area for data mining.

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An Algorithm Based on Counterfactuals for Concept Learning in the Semantic Web

Cited by 33 publications

References 9 publications

An algorithm based on counterfactuals for concept learning in the Semantic Web

An algorithm based on counterfactuals for concept learning in the Semantic Web

Metric-based stochastic conceptual clustering for ontologies

Mining the Semantic Web

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