PaaSport is a cloud broker between PaaS providers and cloud application developers. Abstract PaaS is a Cloud computing service that provides a computing platform to develop, run, and manage applications without the complexity of infrastructure maintenance. SMEs are reluctant to enter the growing PaaS market due to the possibility of being locked in to a certain platform, mostly provided by the market’s giants. The PaaSport Marketplace aims to avoid the provider lock-in problem by allowing Platform provider SMEs to roll out semantically interoperable PaaS offerings and Software SMEs to deploy or migrate their applications on the best-matching offering, through a thin, non-intrusive Cloud broker. The ontology is used for semantically representing a PaaS offering capabilities and b requirements of applications to be deployed. The ontology has been designed to optimally support a semantic matchmaking and ranking algorithm that recommends the best-matching PaaS offering to the application developer. The DUL ontology offers seamless extensibility, since both PaaS Characteristics and parameters are defined as classes; therefore, extending the ontology with new characteristics and parameters requires the addition of new specialized subclasses of the already existing classes, which is less complicated than adding ontology properties. The PaaSport ontology is evaluated through verification tools, competency questions, human experts, application tasks and query performance tests. Previous article in issue.
Intelligent Software Agents
To further that effort, today we are introducing similarity search on Flickr. In many ways, photo search is very different from traditional web or text search. First, the goal of web search is usually to satisfy a particular information need, while with photo search the goal is often one of discovery; as such, it should be delightful as well as functional.
We have taken this to heart throughout Flickr.
Semantic Matching Algorithm by M. Paolucci et al. Vincent Wolowski 1 nt Papers Origin and description of semantic matching algorithm: M. Ian Horrocks: “A Software Framework for Matchmaking based on Semantic Web Technology” () Description of .
Background[ edit ] Given that an AI does not inherently have language, it is unable to think about the meanings behind the words of a language. An artificial notion of meaning needs to be created for a strong AI to emerge. Creating an artificial representation of meaning requires the analysis of what meaning is. There are many terms associated with meaning, like semantics, pragmatics, knowledge, understanding or word sense . Each of these terms describes a particular aspect of meaning, and contribute a multitude of theories explaining what meaning is.
These theories need to be analyzed to develop an artificial notion of meaning best fit for our current state of knowledge. Graph representations[ edit ] Abstract approach on how knowledge representation and reasoning allow a problem specific solution answer to a given problem questions Representing meaning as a graph is one of the two ways that both an AI cognition and a linguistic researcher think about meaning connectionist view. Logicians utilize a formal representation of meaning on the other side to build upon the idea of symbolic representation, where as description logics describe languages and the meaning of symbols.
This contention between ‘neat’ and ‘scruffy’ techniques have gone on for the last 40 years .
A Software Framework For Matchmaking Based on Semantic Web Technology
A semantic matchmaking system for online dating Wilson, E. A semantic matchmaking system for online dating. Download PDF Abstract The popularity of the online dating industry has grown immensely over the past decade. There is an abundance of online dating websites with various features to attract users.
A few of weeks ago I had a threesome with a couple I’m friends with. We are all 20 years old and for the guy’s birthday we gave him a threesome.
Share One man fasted for over a year and survived. How far can you go without risking your health? Thinkstock If you were an avid newspaper reader in , you might have seen a strange little story from United Press International. In his case, it was literally a broken fast, one that had begun more than a year before.
Varady says that a starvation study would violate the Declaration of Helsinki, which established guidelines for human experimentation with an emphasis on the welfare of the subject. The only difference is semantic. Throughout history, fasting was mostly a religious practice, and still is for Muslims during Ramadan. For weight loss, though, the story probably begins with Bernarr Macfadden in the early 20th century.
Macfadden was an influential health and fitness guru who was often far ahead of his time.
Di Noia , E. Di Sciascio , F. Donini Submitted on 12 Oct Abstract: Matchmaking arises when supply and demand meet in an electronic marketplace, or when agents search for a web service to perform some task, or even when recruiting agencies match curricula and job profiles. In such open environments, the objective of a matchmaking process is to discover best available offers to a given request. We address the problem of matchmaking from a knowledge representation perspective, with a formalization based on Description Logics.
Dutch research school for Information and Knowledge Systems.
Dynamic Semantic Matchmaking for Stream Data and Knowledge, UK For this scholarship students should have a commitment to interdisciplinary research and should have a strong background in computer science or a closely related discipline Study Subject: University of Aberdeen Level: The goal of the project is to develop novel ontological stream reasoning techniques to support dynamic semantic matchmaking, such as those needed in business networks.
This PhD will explore the issues associated with ontological stream reasoning and dynamic semantic matchmaking, and in particular will investigate the role of novel reasoning techniques, such as faithful approximations, to support incremental reasoning, not only with stream data but potentially also with stream knowledge, and how to integrate and exploit an ontological stream reasoner in a semantic social network.
Evaluations of the project include the performance of the ontological stream reasoning services and its usefulness in the integrated semantic social network. Sponsored Ads Students should have a commitment to interdisciplinary research and should have a strong background in computer science or a closely related discipline. Application Procedure You should apply for MSc with a view to transfer to PhD in Computing Science, to ensure that your application is passed to the correct College for processing.
A semantic matchmaking system for online dating
The World Wide Web is changing. While once conceived of and implemented as a collection of static pages for browsing, it now promises to become a web of services–a dynamic aggregate of interactive, automated, and intelligent services that interoperate via the Internet. Multiple web services will interoperate to perform tasks, provide information, transact business, and generally take action for users, dynamically and on demand.
/ (PDF) Secure Opportunistic Multipath Key Exchange Sergiu Costea and Marios O. Choudary and Doru Gucea and Björn Tackmann and Costin Raiciu.
One challenge of service coordination in the semantic Web is concerned with how to best connect the ultimate service requester with the ultimate service provider? Like intermediaries in the physical economy, a special kind of software agents, so called middle-agents, is supposed to solve this problem based on the declarative characterization of the capabilities of both service requester and provider agents. In fact, the standard Web service interaction life cycle corresponds to the classical service matchmaking process.
More generally, resource retrieval extends the notion of service matchmaking to the process of discovering any kind of resource services, data, information, knowledge for given settings, participating entities, and purposes. It is at the core of several scenarios in the Semantic Web area, spanning from web-services, grid computing, and Peer-to-Peer computing, to applications such as e-commerce, human resource management, or social networks applications such as mating and dating services.
This year, the SMR2 workshop also integrates the first edition of the open international contest on semantic service selection S3.
Semantic Service Matchmaking in the ATM Domain Considering Infrastructure Capabi
The identification, specification, allocation, and coordination of tasks that will not be done. A usual event dog bites man is not news. It’s human interest, and can be reported only if it helps the reporter score a political point.
Semantic Web services follow a life cycle, right from deployment to its invocation. The life cycle of Semantic Web services comprises different stages like service modeling, service discovery, service definition and service delivery.
The Semantic Web Blog recently had the opportunity to converse via email with Kotis on the topic: Tell us a little bit about your background and how you have come to the Internet of Things as a focus of your semantic efforts. My research focus has been always on Knowledge Representation and Semantic Web technologies, with emphasis on the problem of semantic interoperability ontology alignment methods and tools. We myself and the scientific coordinator of the program, Dr. Artem Katasonov have worked towards a framework and a prototype system that demonstrated how it is possible to automatically interoperate heterogeneous IoT solutions and devices in IoT settings using an IoT ontology as a semantic registry — e.
A short video of this demo is available here and a related poster is also available here. Describe for us what an ontology of the Internet of Things is all about. An ontology for the Internet of Things provides all the necessary semantics for the specification of IoT devices as well as the specifications of the IoT solution input, output, control logic that is deployed using these devices. These semantics include terminology related to sensors and observations, reusing the one already provided by the SSN ontology by W3C Semantic Sensor Network Incubator Group , and extended to capture also the semantics of devices beyond sensors — i.
Furthermore, and more importantly for our work, the ontology includes semantics for the description of the registered IoT solutions — i. Explain to us how this can all play out in practical terms. In our work we have shown that an ontology can be used as a semantic registry for the facilitation of the automated deployment of generic and legacy IoT solutions in environments where heterogeneous devices also have been deployed. Practically, this will require the existence of a central point — e.
What is the risk of moving into the Internet of things world without an ontology to ground it?
Semantic Web Gets Closer To The Internet of Things
Its methods find innovative applications on and off the world wide web. Its underlying technologies have significant impact on adjacent fields of research and on industrial applications. This new book series reports on the state-of-the-art in foundations, methods, and applications of semantic web and its underlying technologies. It is a central forum for the communication of recent developments and comprises research monographs, textbooks and edited volumes on all topics related to the semantic web.
In this first volume several non-monotonic extensions to description logics DLs are investigated, namely auto-epistemic DLs, circumscriptive DLs and terminological default rules, all of which extend standard DL inference mechanisms by forms of closed-world and default reasoning associated to common-sense features. A matchmaking framework is established for semantic resource descriptions formulated in the DL formalism that uses various DL inferences to judge resource compatibility.
Onthe other hand a Semantic matchmaking is necessary: context has different representations making them semantically rich. This increases the need for semantic description mechanismsandsemanticreasoning. In the light ofthe above description, the Context-aware, Ontologybased, Semantic Service Discovery(COSS)-.
Nuclear Shit Tests 6. Shit tests are a basic yet vitally important part of understanding and applying the red pill philosophy to your life. Now without further ado, let us begin. Why are they called shit tests? The underlying mechanism of shit tests is to test your mettle. Humans have a propensity to lie and tell people what they think they want to hear. This is especially true of women and the effeminate men who emulate them; both are consensus seeking creatures who crave the approval of the group above all else.
They are anti-confrontational to the most sublime degree, but nevertheless, I digress. Shit tests can be blatant or they can be covert, how they manifest depends upon the intent and personality of the individual employing the test. You will fail to understand that what you are experiencing is a social initiation ritual that all men must go through when they are new to a male-dominated group.
You are kept straight and presentable by the frame you are kept in.
Semantic Inner Product Based Web Service Matchmaking Method
By adding constraints over aspects that the Seeker is interested in, the query can be used to filter out irrelevant advertisements. There are two kinds of queries that can be defined: The persistent query is a query that will remain valid for a length of time defined by the Seeker itself. The Host immediately returns matched advertisements that are currently present in the repository. Within the validity period of the query, whenever a matching advertisement is added to the repository or an advertisement is modified so that it becomes a match , the Host will notify the Seeker with a new set of matched advertisements including those that have been changed or have been added.
matchmaking service executes a matchmaking algorithm for each request sent by the requester. The input of the algorithm is the request and the resource instances stored in the repository of the matchmaking service.
Learning User Profiles from Text for Personalized Information Access Abstract Advances in the Internet and the creation of huge stores of digitized text have opened the gateway to a deluge of information that is difficult to navigate. Although the information is widely available, exploring Web sites and finding information relevant to a user’s interests is a challenging task.
The first obstacle is research, where you must first identify the appropriate information sources and then retrieve the relevant data. Then, you have to sort through this data to filter out the unfocused and unimportant information. Lastly, in order for the information to be truly useful, you must take the time to figure out how to organize and abstract it in a manner that is easy to understand and analyze.
To say the least, all of these steps are extremely time consuming. This “relevant information problem” leads to a clear demand for automated methods able to support users in searching large document repositories in order to retrieve relevant information with respect to their preferences. Catching user interests and representing them in a structured form is a problematic activity.