Evolving Knowledge Ecosystems were proposed recently to approach the Big Data challenge, following the hypothesis that knowledge evolves in a way similar to biological systems. Therefore, the inner working of the knowledge ecosystem can be spotted from natural evolution. An evolving knowledge ecosystem consists of Knowledge Organisms, which form a representation of the knowledge, and the enviro...

Ontology matching is one of the biggest challenges of Semantic Web research. In the last years the number of matching techniques and systems has significantly increased, and this, in turn, has raised the issue of their evaluation and comparison. In this paper we present a mapping dataset extracted from the Google, Yahoo and Looksmart web directories. This dataset allows for the evaluation of bo...

We consider phase matching in the generation of very short-wavelength coherent radiation by nonlinear frequency upconversion in plasma and suggest some ways to improve phase matching in high-order harmonic generation. We obtain what are to our knowledge the first simple analytical expressions for a phase-matching factor in multiphoton mixing of an arbitrary order and demonstrate theoretically t...

BACKGROUND We are currently facing a proliferation of heterogeneous biomedical data sources accessible through various knowledge-based applications. These data are annotated by increasingly extensive and widely disseminated knowledge organisation systems ranging from simple terminologies and structured vocabularies to formal ontologies. In order to solve the interoperability issue, which arises...

In this paper we study very large-scale neighborhoods for the minimum total weighted completion time problem on parallel machines, which is known to be strongly NP-hard. We develop two different ideas leading to very large-scale neighborhoods in which the best improving neighbor can be determined by calculating a weighted matching. The first neighborhood is introduced in a general fashion using...

Large Scale Graph Matching (LSGM) is one of the fundamental problems in Graph theory and it has applications in many areas such as Computer Vision, Machine Learning, Pattern Recognition and Big Data Analytics (Data Science). Matching belongs to the combinatorial class of problems which refers to finding correspondence between the nodes of a graph or among set of graphs (subgraphs) either precis...

Generalized matching problems arise in a number of applications, including computational advertising, recommender systems, and trade markets. Consider, for example, the problem of recommending multimedia items (e.g., DVDs) to users such that (1) users are recommended items that they are likely to be interested in, (2) every user gets neither too few nor too many recommendations, and (3) only it...

Schema and ontology matching have attracted a great deal of interest among researchers. Despite the advances achieved, the large matching problem still presents a real challenge, such as it is a timeconsuming and memory-intensive process. We therefore propose a scalable, clustering-based matching approach that breaks up the large matching problem into smaller matching problems. In particular, w...

Feature-to-feature matching is the key issue in the Bag-of-Features model. The baseline approach employs a coarse feature-to-feature matching, namely, two descriptors are assumed to match if they are assigned the same quantization index. However, this Hard Assignment strategy usually incurs undesirable low precision. To fix it, Multiple Assignment and Soft Assignment are proposed. These two met...

Though dynamic programming matching can carry out approximate string matching when there may be deletions or insertions in a document, its effectiveness and efficiency are usually too poor to use it for large-scale information retrieval. In this paper, we propose a method of dynamic programming matching for information retrieval. This method is as effective as a conventional information retriev...