Memetic algorithms for ontology alignment

2011 - 2012Semantic interoperability represents the capability of two or more systems to meaningfully and accurately interpret the exchanged data so as to produce useful results. It is an essential feature of all distributed and open knowledge based systems designed for both e-government and private businesses, since it enables machine interpretation, inferencing and computable logic. Unfortunately, the task of achieving semantic interoperability is very difficult because it requires that the meanings of any data must be specified in an appropriate detail in order to resolve any potential ambiguity. Currently, the best technology recognized for achieving such level of precision in specification of meaning is represented by ontologies. According to the most frequently referenced definition [1], an ontology is an explicit specification of a conceptualization, i.e., the formal specification of the objects, concepts, and other entities that are presumed to exist in some area of interest and the relationships that hold them [2]. However, different tasks or different points of view lead ontology designers to produce different conceptualizations of the same domain of interest. This means that the subjectivity of the ontology modeling results in the creation of heterogeneous ontologies characterized by terminological and conceptual discrepancies. Examples of these discrepancies are the use of different words to name the same concept, the use of the same word to name different concepts, the creation of hierarchies for a specific domain region with different levels of detail and so on. The arising so-called semantic heterogeneity problem represents, in turn, an obstacle for achieving semantic interoperability... [edited by author]XI n.s

An Introductory Guide to Aligning Networks Using SANA, the Simulated Annealing Network Aligner.

Sequence alignment has had an enormous impact on our understanding of biology, evolution, and disease. The alignment of biological networks holds similar promise. Biological networks generally model interactions between biomolecules such as proteins, genes, metabolites, or mRNAs. There is strong evidence that the network topology-the "structure" of the network-is correlated with the functions performed, so that network topology can be used to help predict or understand function. However, unlike sequence comparison and alignment-which is an essentially solved problem-network comparison and alignment is an NP-complete problem for which heuristic algorithms must be used.Here we introduce SANA, the Simulated Annealing Network Aligner. SANA is one of many algorithms proposed for the arena of biological network alignment. In the context of global network alignment, SANA stands out for its speed, memory efficiency, ease-of-use, and flexibility in the arena of producing alignments between two or more networks. SANA produces better alignments in minutes on a laptop than most other algorithms can produce in hours or days of CPU time on large server-class machines. We walk the user through how to use SANA for several types of biomolecular networks

Unified Alignment of Protein-Protein Interaction Networks

Paralleling the increasing availability of protein-protein interaction (PPI) network data, several network alignment methods have been proposed. Network alignments have been used to uncover functionally conserved network parts and to transfer annotations. However, due to the computational intractability of the network alignment problem, aligners are heuristics providing divergent solutions and no consensus exists on a gold standard, or which scoring scheme should be used to evaluate them. We comprehensively evaluate the alignment scoring schemes and global network aligners on large scale PPI data and observe that three methods, HUBALIGN, L-GRAAL and NATALIE, regularly produce the most topologically and biologically coherent alignments. We study the collective behaviour of network aligners and observe that PPI networks are almost entirely aligned with a handful of aligners that we unify into a new tool, Ulign. Ulign enables complete alignment of two networks, which traditional global and local aligners fail to do. Also, multiple mappings of Ulign define biologically relevant soft clusterings of proteins in PPI networks, which may be used for refining the transfer of annotations across networks. Hence, PPI networks are already well investigated by current aligners, so to gain additional biological insights, a paradigm shift is needed. We propose such a shift come from aligning all available data types collectively rather than any particular data type in isolation from others

An ontology matching approach for semantic modeling: A case study in smart cities

This paper investigates the semantic modeling of smart cities and proposes two ontology matching frameworks, called Clustering for Ontology Matching-based Instances (COMI) and Pattern mining for Ontology Matching-based Instances (POMI). The goal is to discover the relevant knowledge by investigating the correlations among smart city data based on clustering and pattern mining approaches. The COMI method first groups the highly correlated ontologies of smart-city data into similar clusters using the generic k-means algorithm. The key idea of this method is that it clusters the instances of each ontology and then matches two ontologies by matching their clusters and the corresponding instances within the clusters. The POMI method studies the correlations among the data properties and selects the most relevant properties for the ontology matching process. To demonstrate the usefulness and accuracy of the COMI and POMI frameworks, several experiments on the DBpedia, Ontology Alignment Evaluation Initiative, and NOAA ontology databases were conducted. The results show that COMI and POMI outperform the state-of-the-art ontology matching models regarding computational cost without losing the quality during the matching process. Furthermore, these results confirm the ability of COMI and POMI to deal with heterogeneous large-scale data in smart-city environments.publishedVersio

Natalie 2.0: Sparse Global Network Alignment as a Special Case of Quadratic Assignment

International audienceData on molecular interactions is increasing at a tremendous pace, while the development of solid methods for analyzing this network data is still lagging behind. This holds in particular for the field of comparative network analysis, where one wants to identify commonalities between biological networks. Since biological functionality primarily operates at the network level, there is a clear need for topology-aware comparison methods. We present a method for global network alignment that is fast and robust and can flexibly deal with various scoring schemes taking both node-to-node correspondences as well as network topologies into account. We exploit that network alignment is a special case of the well-studied quadratic assignment problem (QAP). We focus on sparse network alignment, where each node can be mapped only to a typically small subset of nodes in the other network. This corresponds to a QAP instance with a symmetric and sparse weight matrix. We obtain strong upper and lower bounds for the problem by improving a Lagrangian relaxation approach and introduce the open source software tool Natalie 2.0, a publicly available implementation of our method. In an extensive computational study on protein interaction networks for six different species, we find that our new method outperforms alternative established and recent state-of-the-art methods

Negotiating ludic normativity in Facebook meme pages

Title: Negotiating ludic normativity in Facebook meme pages Author: Ondřej Procházka Affiliation: Department of Culture Studies, Tilburg School of Humanities and Digital Sciences This thesis explores the capacity of Internet memes to inflect social realities in the communities organized around them on social media, particularly Facebook. Memes are not mere playful ‘jokes’ or ‘parodies’ spreading virally on the Internet in countless variations, they are also powerful tools for political investment aimed to sway public attention and opinions. Memes have been increasingly documented as a vital component in the unprecedented spread and ‘normalization’ of hateful sentiments and ideologies characterized by ‘fake news’ and ‘post-truth’ politics appealing to emotions rather than ‘facts’ in the digital mainstream. Based on author’s more than five-year observation of communities around Countryball memes, this work argues that much of the socio-cultural and communicative dynamics involving memes can be understood in terms of ludic play. The object of the study – Countryballs memes – are simple meme-comics featuring ball-shaped creatures in colors denoting nation-states while satirically reinventing international ‘drama’ through the prism of socio-cultural and linguistic stereotypes. Having become a household name among memes, Countryballs offer communicative resources to playfully engage not only with wider socio-political issues, but also to with the linguistic, semiotic and ideological boundaries of our communicative norms shaped by the affordances of social media. The present work demonstrates how play can be used as a useful concept for understanding not only how matters of public attention are packed, framed and transmitted in the digital culture via (Countryball) memes, but more importantly how such matters are in fact interpreted by those who engage with them. More specifically, it shows how play enables alternative modes of expression and meaning making with different normative patterns and preferences which stand outside ‘standard’, ‘rational’ or ‘civil’ expectations. And it is precisely ludic play that fosters different types of communication and sociality which are often done ‘just for fun’, however serious or offensive their effects may be. To identify these effects and their implications in the contemporary digital age, the thesis employs a discourse-analytical methodology informed by current advances in digital ethnography and sociolinguistics. It focuses on negotiations among participants in memetic communities about what counts as ‘appropriate’, ‘acceptable’ or ‘correct’ in their socio-communicative behavior. Together in four case studies, the present work provides a comprehensive account of how participants articulate, police, break and re-construct ludic normativity in connection with recent socio-political issues and digital culture at large. This includes the role of memes in the newly emerging forms of communication, in the rise of populism and nationalism, algorithmic manipulation and exploitation, curating digital content and more. The concept of play is continually revisited throughout the discussion against the developments in the scholarship on Internet memes and their ludic genealogy. In doing so, the thesis also revisits some of the traditional concepts such as the notion of ‘community’ and ‘communicative competence’ to arrive at more precise accounts of the concrete processes of globalization and digitalization in our societies and their effects