@incollection{hoser2006semantic,
  abstract = { A key argument for modeling knowledge in ontologies is the easy reuse and re-engineering of the knowledge. However, current ontology engineering tools provide only basic functionalities for analyzing ontologies. Since ontologies can be considered as graphs, graph analysis techniques are a suitable answer for this need. Graph analysis has been performed by sociologists for over 60 years, and resulted in the vivid research area of Social Network Analysis (SNA).While social network structures currently receive high attention in the Semantic Web community, there are only very few SNA applications, and virtually none for analyzing the structure of ontologies. We illustrate the benefits of applying SNA to ontologies and the Semantic Web, and discuss which research topics arise on the edge between the two areas. In particular, we discuss how different notions of centrality describe the core content and structure of an ontology. From the rather simple notion of degree centrality over betweenness centrality to the more complex eigenvector centrality, we illustrate the insights these measures provide on two ontologies, which are different in purpose, scope, and size. },
  address = {Berlin/Heidelberg},
  author = {Hoser, Bettina and Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd},
  booktitle = {The Semantic Web: Research and Applications},
  doi = {10.1007/11762256_38},
  editor = {Sure, York and Domingue, John},
  interhash = {344ec3b4ee8af1a2c6b86efc14917fa9},
  intrahash = {2b720233e4493d4e0dee95be86dd07e8},
  isbn = {978-3-540-34544-2},
  note = {10.1007/11762256_38},
  pages = {514--529},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Semantic Network Analysis of Ontologies},
  url = {http://dx.doi.org/10.1007/11762256_38},
  volume = 4011,
  year = 2006
}

@inproceedings{hjss06bibsonomy,
  address = {Aalborg, Denmark},
  author = {Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd},
  booktitle = {Proceedings of the Conceptual Structures Tool Interoperability Workshop at the 14th International Conference on Conceptual Structures},
  editor = {de Moor, Aldo and Polovina, Simon and Delugach, Harry},
  interhash = {d28c9f535d0f24eadb9d342168836199},
  intrahash = {2cbd8e3236adea7c54779605a5aa4fd6},
  isbn = {87-7307-769-0},
  month = jul,
  publisher = {Aalborg University Press},
  title = {{BibSonomy}: A Social Bookmark and Publication Sharing System},
  url = {http://www.kde.cs.uni-kassel.de/pub/pdf/hotho06bibsonomy.pdf},
  vgwort = {27},
  year = 2006
}

@article{lhfh05social,
  author = {Lund, Ben and Hammond, Tony and Flack, Martin and Hannay, Timo},
  interhash = {46c0a98ab6ccb96ff4722f35781807de},
  intrahash = {13958ef5da2d2133b9b84e9a3cb40da1},
  journal = {D-Lib Magazine},
  month = {April},
  number = 4,
  organization = {{N}ature {P}ublishing {G}roup},
  title = {{S}ocial {B}ookmarking {T}ools ({II}): {A} {C}ase {S}tudy - {C}onnotea},
  url = {http://www.dlib.org/dlib/april05/lund/04lund.html},
  volume = 11,
  year = 2005
}

@article{hammond2005social,
  abstract = {This paper reviews some current initiatives, as of early 2005, in providing public link management applications on the Web � utilities that are often referred to under the general moniker of 'social bookmarking tools'. There are a couple of things going on here: 1 server-side software aimed specifically at managing links with, crucially, a strong, social networking flavour, and 2 an unabashedly open and unstructured approach to tagging, or user classification, of those links.  A number of such utilities are presented here, together with an emergent new class of tools that caters more to the academic communities and that stores not only user-supplied tags, but also structured citation metadata terms wherever it is possible to glean this information from service providers. This provision of rich, structured metadata means that the user is provided with an accurate third-party identification of a document, which could be used to retrieve that document, but is also free to search on user-supplied terms so that documents of interest or rather, references to documents can be made discoverable and aggregated with other similar descriptions either recorded by the user or by other users.},
  author = {Hammond, Tony and Hannay, Timo and Lund, Ben and Scott, Joanna},
  interhash = {c7457d9dc07545a061de119d96ca4e47},
  intrahash = {89c6c43ad692ccfbe4c09d31926ab8a7},
  issn = {1082-9873},
  journal = {D-Lib Magazine},
  month = apr,
  number = 4,
  organization = {Nature Publishing Group},
  title = {Social Bookmarking Tools (I): A General Review},
  url = {http://www.dlib.org/dlib/april05/hammond/04hammond.html},
  volume = 11,
  year = 2005
}

@misc{Ramos06,
  abstract = {Wasps, bees, ants and termites all make effective use of their environment and resources by displaying collective "swarm" intelligence. Termite colonies - for instance - build nests with a complexity far beyond the comprehension of the individual termite, while ant colonies dynamically allocate labor to various vital tasks such as foraging or defense without any central decision-making ability. Recent research suggests that microbial life can be even richer: highly social, intricately networked, and teeming with interactions, as found in bacteria. What strikes from these observations is that both ant colonies and bacteria have similar natural mechanisms based on Stigmergy and Self-Organization in order to emerge coherent and sophisticated patterns of global foraging behavior. Keeping in mind the above characteristics we propose a Self-Regulated Swarm (SRS) algorithm which hybridizes the advantageous characteristics of Swarm Intelligence as the emergence of a societal environmental memory or cognitive map via collective pheromone laying in the landscape (properly balancing the exploration/exploitation nature of our dynamic search strategy), with a simple Evolutionary mechanism that trough a direct reproduction procedure linked to local environmental features is able to self-regulate the above exploratory swarm population, speeding it up globally. In order to test his adaptive response and robustness, we have recurred to different dynamic multimodal complex functions as well as to Dynamic Optimization Control problems, measuring reaction speeds and performance. Final comparisons were made with standard Genetic Algorithms (GAs), Bacterial Foraging strategies (BFOA), as well as with recent Co-Evolutionary approaches. SRS's were able to demonstrate quick adaptive responses, while outperforming the results obtained by the other approaches. Additionally, some successful behaviors were found: SRS was able to maintain a number of different solutions, while adapting to unforeseen situations even when over the same cooperative foraging period, the community is requested to deal with two different and contradictory purposes; the possibility to spontaneously create and maintain different subpopulations on different peaks, emerging different exploratory corridors with intelligent path planning capabilities; the ability to request for new agents (division of labor) over dramatic changing periods, and economizing those foraging resources over periods of intermediate stabilization. Finally, results illustrate that the present SRS collective swarm of bio-inspired ant-like agents is able to track about 65% of moving peaks traveling up to ten times faster than the velocity of a single individual composing that precise swarm tracking system. This emerged behavior is probably one of the most interesting ones achieved by the present work.},
  author = {Ramos, Vitorino and Fernandes, Carlos and Rosa, Agostinho C.},
  citeulike-article-id = {407750},
  conference = {International Conference on the Simulation and Synthesis of Living Systems},
  interhash = {ad015c697574e1fd38a25c5da9194d46},
  intrahash = {804ad41798bd794f4f85c96bd6217127},
  note = {arXiv:cs/0512002v1},
  priority = {4},
  title = {On Self-Regulated Swarms, Societal Memory, Speed and Dynamics},
  url = {http://arxiv.org/abs/cs/0512002},
  year = 2006
}

@techreport{Ramos05,
  abstract = {Swarm Intelligence (SI) is the property of a system whereby the collective behaviors of (unsophisticated) entities interacting locally with their environment cause coherent functional global patterns to emerge. SI provides a basis with wich it is possible to explore collective (or distributed) problem solving without centralized control or the provision of a global model. To tackle the formation of a coherent social collective intelligence from individual behaviors, we discuss several concepts related to Self-Organization, Stigmergy and Social Foraging in animals. Then, in a more abstract level we suggest and stress the role played not only by the environmental media as a driving force for societal learning, as well as by positive and negative feedbacks produced by the many interactions among agents. Finally, presenting a simple model based on the above features, we will adress the collective adaptation of a social community to a cultural (environmenatl, contextual) or media informational dynamical landscape, represented here - for the purpose of different experiments - by several three-dimensional mathematical functions that suddenly change over time. Results indicate that the collective intelligence is able to cope and quickly adapt to unforseen situations even when over the same cooperative foraging period, the community is requested to deal with two different and contradictory purposes.},
  author = {Ramos, Vitorino and Fernandes, Carlos and Rosa, Agostinho C.},
  citeulike-article-id = {407689},
  institution = {Insituto Superior Técnico, Universidade Técnica de Lisboa},
  interhash = {94852bf2e4e639cbbb0f29ababeb6161},
  intrahash = {7debdcf93027a77b3b928caae4121dff},
  note = {arXiv:nlin/0502057v1},
  number = {CVRM-IST 127E-2005},
  priority = {4},
  title = {Social Cognitive Maps, Swarm Collective Perception and Distributed Search on Dynamic Landscapes},
  url = {http://arxiv.org/abs/nlin/0502057},
  year = 2005
}