Download Graph-Based Representation and Reasoning: 21st International by Nathalie Hernandez, Robert Jäschke, Madalina Croitoru PDF

By Nathalie Hernandez, Robert Jäschke, Madalina Croitoru

This booklet constitutes the complaints of the twenty first overseas convention on Conceptual buildings, ICCS 2014, held in Iaşi, Romania, in July 2014. The 17 common papers and six brief papers offered during this quantity have been conscientiously reviewed and chosen from forty and 10 submissions, respectively. the subjects coated are: conceptual constructions, wisdom illustration, reasoning, conceptual graphs, formal thought research, semantic net, info integration, laptop studying, info mining and data retrieval.

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Extra resources for Graph-Based Representation and Reasoning: 21st International Conference on Conceptual Structures, ICCS 2014, Iaşi, Romania, July 27-30, 2014, Proceedings

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In the early nineties, one became aware that sets of vectors (the so-called propositional or attribute-value representation) was not anymore sufficient to represent datasets. The field of inductive logic programming (ILP) [11] was the first to be successful in machine learning describing data with the more powerful language of first order logic. In fact, first order logic is so powerful that many problems including deduction are undecidable, and to mitigate the computational intractability, several settings between the propositional one and the first order logic one were explored [4].

Let T = (Arg(Σ), R) be an argumentation system over a knowledge base Σ. The output of T is the pair Output(T ) = C, such that: C = {Conc(a) | a ∈ Arg(Σ)} x y iff ∃a ∈ Arg(Σ) such that Conc(a) = x and ∀b ∈ Arg(Σ) such that Conc(b) = y, a, b ∈ Bbs(T ). Unlike certain well-known inconsistency-tolerating logics (like the the 3 and 4 valued ones [4,6]), the above logics satisfy the following crucial property: if the premises are consistent, the conclusions coincide with those of CN. 5 Conclusion Argumentation is a natural approach for handling inconsistency.

Molecule databases, optimized pattern mining solutions have been developed [7] exploiting the structure of the database graphs. Unfortunately, large data networks don’t have an easy to exploit structure and are at the same time orders of magnitude 28 J. Ramon larger. The NP-hardness of the problem suggests that the increase in computational complexity caused by the increasing database sizes is not expected to be compensated by the increasing computing power predicted by the law of Moore. We therefore face the fundamental problem of making data analysis algorithms scale well with the growing databases.

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