CoCoE stands for Complexity, Coherence and Entropy, and presents an extensible methodology for empirical analysis of Linked Open Data (i.e., RDF graphs). CoCoE can offer answers to questions like: Is dataset A better than B for knowledge discovery since it is more complex and informative?, Is dataset X better than Y for simple value lookups due its flatter structure?, etc. In order to address such questions, we introduce a set of well-founded measures based on complementary notions from distributional semantics, network analysis and information theory. These measures are part of a specific implementation of the CoCoE methodology that is available for download. Last but not least, we illustrate CoCoE by its application to selected biomedical RDF datasets.
@cite_0: In this paper we describe several new clustering algorithms for nodes in a mobile ad hoc network. We propose to combine two known approaches into a single clustering algorithm which considers connectivity as a primary criterion and lower ID as secondary criterion for selecting cluster heads. The goal is to minimize the number of clusters, which results in dominating sets of smaller sizes (this is important for applications in broadcasting and Bluetooth formation). We also describe algorithms for modifying cluster structure in the presence of topological changes. Next, we generalize the cluster definition so that a cluster contains all nodes that are at a distance of at most k hops from the cluster head. The efficiency of four clustering algorithms (k-lowestID and k-CONID, k=1 and k=2) is tested by measuring the average number of created clusters, the number of border nodes, and the cluster size in random unit graphs. The most interesting experimental result is stability of the ratio of the sum of CHs and border nodes in the set. It was constantly 60-70 for 1-lowestID and 46-56 for 1-ConID, for any value of n (number of nodes) and d (average node degree). Similar conclusions and similar number were obtained for k=2. We also proposed a unified framework for most existing and new clustering algorithms where a properly defined weight at each node is the only difference in the algorithm. Finally, we propose a framework for generating random unit graphs with obstacles.
@cite_12: In recent years, ontologies have become a mainstream topic in biomedical research. When biological entities are described using a common schema, such as an ontology, they can be compared by means of their annotations. This type of comparison is called semantic similarity, since it assesses the degree of relatedness between two entities by the similarity in meaning of their annotations. The application of semantic similarity to biomedical ontologies is recent; nevertheless, several studies have been published in the last few years describing and evaluating diverse approaches. Semantic similarity has become a valuable tool for validating the results drawn from biomedical studies such as gene clustering, gene expression data analysis, prediction and validation of molecular interactions, and disease gene prioritization. We review semantic similarity measures applied to biomedical ontologies and propose their classification according to the strategies they employ: node-based versus edge-based and pairwise versus groupwise. We also present comparative assessment studies and discuss the implications of their results. We survey the existing implementations of semantic similarity measures, and we describe examples of applications to biomedical research. This will clarify how biomedical researchers can benefit from semantic similarity measures and help them choose the approach most suitable for their studies. Biomedical ontologies are evolving toward increased coverage, formality, and integration, and their use for annotation is increasingly becoming a focus of both effort by biomedical experts and application of automated annotation procedures to create corpora of higher quality and completeness than are currently available. Given that semantic similarity measures are directly dependent on these evolutions, we can expect to see them gaining more relevance and even becoming as essential as sequence similarity is today in biomedical research.
@cite_8: Network theory has become pervasive in all sectors of biology, from biochemical signalling to human societies, but identification of relevant functional communities has been impaired by many nodes belonging to several overlapping groups at once, and by hierarchical structures. These authors offer a radically different viewpoint, focusing on links rather than nodes, which allows them to demonstrate that overlapping communities and network hierarchies are two faces of the same issue.
The clustering method introduced here builds on principles similar to k-hop clustering @cite_0 . Another related approach is nonparametric hierarchical link clustering @cite_8 , which is more general and sophisticated than our simple method, yet its Python implementation we have experimented with proved to be intractable when used in our experiments. A comprehensive overview of semantic similarity measures applicable in is provided in @cite_12 . The similarities used in our experiments were the cosine and Wu-Palmer ones, chosen as representatives of the vector space-based and taxonomy-based similarity types.