By Enrique Machuca, Lawrence Mandow, Lucie Galand (auth.), Concha Bielza, Antonio Salmerón, Amparo Alonso-Betanzos, J. Ignacio Hidalgo, Luis Martínez, Alicia Troncoso, Emilio Corchado, Juan M. Corchado (eds.)
This e-book constitutes the refereed lawsuits of the fifteenth convention of the Spanish organization for synthetic Intelligence, CAEPIA 20013, held in Madrid, Spain, in September 2013. The 27 revised complete papers awarded have been rigorously chosen from sixty six submissions. The papers are equipped in topical sections on Constraints, seek and making plans, clever internet and knowledge retrieval, fuzzy platforms, wisdom illustration, reasoning and good judgment, desktop studying, multiagent platforms, multidisciplinary issues and purposes, metaheuristics, uncertainty in man made intelligence.
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Extra info for Advances in Artificial Intelligence: 15th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2013, Madrid, Spain, September 17-20, 2013. Proceedings
Moreover, in case that the data is time stamped –the case for RS exploiting time information– differences in evaluation can be meaningful, and may affect relative ranking of the algorithms’ performance . Several offline evaluation methodologies had been employed in measuring recommender system performance. In  several time-aware (and time-unaware) methodologies are described, by means of a methodological description framework that is based on a number of key methodological conditions that drive a RS evaluation process.
They are mapped from |SO|+1. Although this decision produces an overlapping between the subject and object ID, the interpretation of the correct partition is trivial once we know if the ID in the graph plays a subject or an object role. The current example contains one subject: m3:t5-21-10 (ID 2) and six objects (range 2-7). Finally, the P partition represents all the predicate terms: |P|, and are independently mapped to the range [1, |P|] (no ambiguity is possible as they are labels in the graph).
2. Implementation of the Triples component Triples. This comprises the pure structure of the underlying RDF graph. Figure 1 (right) shows that this structure respects the original graph, avoiding the noise produced by long labels and repetitions. The implementation of this component deserves a deeper analysis. The left side of Figure 2 draws an alternative representation of the ID graph as a forest of trees, one per subject. Each subject is the root of its associated tree, representing the ordered list of predicates reachable from the subject in the middle level.