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I've been running Simpy for almost 4 years now. Every once in a while I get the "Why should I use Simpy?", "Why is Simpy better than, say, del.icio.us?" This post answers those questions in the form of "Why I use Simpy" answer. This are my personal reasons for using Simpy, but you can also read other people's opinions. I also encourage you to add your reasons in the comments, so I can add your reasons to this list.

The aim of the EU FP 7 Large-Scale Integrating Project LarKC is to develop the Large Knowledge Collider (LarKC, for short, pronounced "lark"), a platform for massive distributed incomplete reasoning that will remove the scalability barriers of currently existing reasoning systems for the Semantic Web.

Proceedings of the Third ISWC Workshop on Uncertainty Reasoning for the Semantic Web Busan, Korea, November 12, 2007

This document defines a concise bounded description of a resource in terms of an RDF graph [5], as a general and broadly optimal unit of specific knowledge about that resource to be utilized by, and/or interchanged between, semantic web agents. Given a particular node in a particular RDF graph, a concise bounded description is a subgraph consisting of those statements which together constitute a focused body of knowledge about the resource denoted by that particular node. The precise nature of that subgraph will hopefully become clear given the definition, discussion and example provided below. Optimality is, of course, application dependent and it is not presumed that a concise bounded description is an optimal form of description for every application; however, it is presented herein as a reasonably general and broadly optimal form of description for many applications, and unless otherwise warranted, constitutes a reasonable default response to the request "tell me about this resource".

The project KiWi is concerned with knowledge management in Semantic Wikis and funded by the European Commission under the Project Number 211932 in the EU Seventh Framework Programme (FP7). KiWi's objective is to investigate how knowledge management in highly dynamic environments can be supported using Semantic Wiki technologies, and how Semantic Wikis can be improved to satisfy the requirements of knowledge management. For this purpose, KiWi will * implement an advanced knowledge management system based on the Semantic Wiki IkeWiki and extend it by improved, rule-based reasoning support, information extraction, personalisation, and advanced visualisations and editors * verify the system on two use cases in the areas of project knowledge management and software knowledge management, with flexible workflow models and specific support for the respective application areas.

Web 3.0: Personalization, Reasoning Or Openness?

The aims of clinical and translational research are to achieve a better understanding of the pathogenesis of human disease in order to develop effective diagnostic, therapeutic and prevention strategies. Biomedical informatics can play an important role in supporting this research by facilitating the management, integration, analysis and exchange of data derived from and related to the research problems being studied. A key aspect of this support is to bring clarity, rigor and formalism to the representation of 1. disease initiation, progression, pathogenesis, signs, symptoms, assessments, clinical and laboratory findings, disease diagnosis, treatment, treatment response and outcome, and 2. the interrelations between these distinct entities both in patient management and in clinical research, thus allowing the data to be more readily retrievable and shareable, and more able to serve in the support of algorithmic reasoning.

By integrating heterogeneous original data with uniform RDF format, creating additional ontology mappings between GO terms, and introducing a set of reasoning rulebases, we proposed a RDF model GORouter.

Sensemaking is the way in which people understand the world at large scale: how they decide what kind of goals are reasonable to try for and what kinds of strategies are worth trying or using. Sensemaking is related to what the military call situational awareness.

Sensemaking systems don't only help people find stuff faster. That's just the information retrieval part. The bigger story is about augmenting and amplifying our abilities to make sense. Sensemaking adds things like skimming, power reading, organizing, spotting patterns, tracing social networks, taking notes, summarizing, drilling for details, and flagging biases. Reading an article is different from reading a book, and that's different from reading from a collection or stream. Radically new forms of human-information interaction are being enabled by these new technologies. Sensemaking systems not only have front ends (visualization), but also back ends (content analytics and reasoning).

Last week, I participated in a WWW2007 panel called "Multimedia Metadata Standards in a Semantic Web 3.0", where I took the opportunity to declare the Semantic Web dead. As you can imagine, such a declaration in front of a crowd of semantic web researchers provoked many responses. While I believe panels should be provocative and entertaining, I also have specific reasons for why I went as far as calling the Semantic Web "dead". Let me explain what I mean.

Complexity grows as technology, environment and economy become more interconnected. Complex Reasoning considers how each element affects and is affected by others. Seed™ models your theories of how a change in one element of a system creates changes in other elements. It then simulates their dynamic behavior and communicates your learning.

The global spread of the H5N1 avian influenza has already extensively damaged economies worldwide and food security in developing countries. The spread of infection to new ecosystems results in viral adaptation to new hosts, including humans, which inevitably amplifies the potential for pandemic flu. H5N1 represents an unprecedented model of how influenza infections may become widespread. It is recognized that avian influenza viruses may be the progenitors of the next human pandemic virus, and for this reason their genetic evolution should be monitored and investigated in a timely manner. The full support of the international scientific community is therefore urgently required to better understand the spread and evolution of the virus, and the determinants of its transmissibility and pathogenicity in humans. This in turn demands that scientists with different fields of expertise have full access to comprehensive genetic sequence, clinical, and epidemiological data from both animal and human virus isolates.

To manage information quality (IQ) effectively, one needs to know how IQ changes over time, what causes it to change, and whether the changes can be predicted. In this paper we analyze the structure of IQ change in Wikipedia, an open, collaborative general encyclopedia. We found several patterns in Wikipedia's IQ process trajectories and linked them to article types. Drawing on the results of our analysis, we develop a general model of IQ change that can be used for reasoning about IQ dynamics in many different settings, including traditional databases and information repositories.

OWL 2 extends the W3C OWL Web Ontology Language with a small but useful set of features that have been requested by users, for which effective reasoning algorithms are now available, and that OWL tool developers are willing to support. The new features include extra syntactic sugar, additional property and qualified cardinality constructors, extended datatype support, simple metamodeling, and extended annotations.

Perhaps the most common reason given for joining the microsharing site Twitter is "participating in the conversation" or some version of that. I myself am guilty of using this explanation. But is Twitter truly a conversational platform? Here I argue that the underlying mechanics of Twitter more closely resemble the knowledge co-creation seen in wikis than the dynamics seen with conversational tools like instant messaging and interactions within online social networks.

I hope that "Smarter" is going to be a key tag for the Web 3.0, and yet I think "More Open, More Ubiquitous, with even More Information (Overload) and a little Smarter" is what it's really going to be. We'll have to wait till "Web 4.0" for a web that really is stepwise more intelligent, one that could really be called semantic and hold the hidden promises of a "Semantic Web". And the reason I believe this is that the community is focused on linking more stuff together in new ways and breaking down data siloes, much more than it is focused on creating new, smarter filters for all the data that's going to be made accessible that way.

COSMO is the proposed Common Semantic Model, viewed as consisting of a lattice of ontologies which will serve as a set of basic logically-specified concepts (classes, relations, functions, instances) with which the meanings of all terms and concepts in domain ontologies can be specified. The most important function of the COSMO is to serve as a Foundation Ontology that has a sufficient inverntory of fundamental concept representations so that it can support utilities to translate assertions of fundamentally different ontologies into the terminology and format of each other. The use of a common set of defining concepts will permit accurate interoperability of knowledge-based systems using the logical relations of their ontologies as the basis for reasoning in the system. The COSMO can also be used as the starting ontology for creation of more specialized domain ontologies.

For the past few months I've been an active member of Twine.com; a beta semantic web app riddled with AI to help us organize, share and discover information. The beta is still under heavy construction, but at this point in time, I've migrated entirely from Del.icio.us, personal wikis and similar online services and over to Twine.

How do chess players make decisions? How do they avoid the combinatorial explosion? How do we go from rooks and knights to abstract thought? What is abstract thought like? These are some of the questions involving the Capyblanca project. The name, of course, is a blend between José Raoul Capablanca, and Hofstadter's original Copycat Project implemented by Melanie Mitchell, which brought us so many ideas. Well, after almost 5 years, we have a proof-of-concept in the form of a running program, and we are GPL'ing the code, so interested readers might take it to new directions which we cannot foresee.