A platform for semantic web studies

The Semantic Web can be seen as a large, heterogeneous network of ontologies and semantic documents. Characterizing these ontologies, the way they relate and the way they are organized can help in better understanding how knowledge is produced and published online. It also provides new ways to explore and exploit this large collection of ontologies. In this paper, we present the foundation of a research platform for characterizing the Semantic Web, relying on the collection of ontologies and the functionalities provided by the Watson Semantic Web search engine. We more specifically focus on formalizing and monitoring relationships between ontologies online, considering a variety of different relations (similarity, versioning, agreement, modularity) and how they can help us obtaining meaningful overviews of the current state of the Semantic Web

Improving Knowledge Retrieval in Digital Libraries Applying Intelligent Techniques

Nowadays an enormous quantity of heterogeneous and distributed information is stored in the digital University. Exploring online collections to find knowledge relevant to a user’s interests is a challenging work. The artificial intelligence and Semantic Web provide a common framework that allows knowledge to be shared and reused in an efficient way. In this work we propose a comprehensive approach for discovering E-learning objects in large digital collections based on analysis of recorded semantic metadata in those objects and the application of expert system technologies. We have used Case Based-Reasoning methodology to develop a prototype for supporting efficient retrieval knowledge from online repositories. We suggest a conceptual architecture for a semantic search engine. OntoUS is a collaborative effort that proposes a new form of interaction between users and digital libraries, where the latter are adapted to users and their surroundings

Smart Search Engine For Information Retrieval

This project addresses the main research problem in information retrieval and semantic search. It proposes the smart search theory as new theory based on hypothesis that semantic meanings of a document can be described by a set of keywords. With two experiments designed and carried out in this project, the experiment result demonstrates positive evidence that meet the smart search theory. In the theory proposed in this project, the smart search aims to determine a set of keywords for any web documents, by which the semantic meanings of the documents can be uniquely identified. Meanwhile, the size of the set of keywords is supposed to be small enough which can be easily managed. This is the fundamental assumption for creating the smart semantic search engine. In this project, the rationale of the assumption and the theory based on it will be discussed, as well as the processes of how the theory can be applied to the keyword allocation and the data model to be generated. Then the design of the smart search engine will be proposed, in order to create a solution to the efficiency problem while searching among huge amount of increasing information published on the web. To achieve high efficiency in web searching, statistical method is proved to be an effective way and it can be interpreted from the semantic level. Based on the frequency of joint keywords, the keyword list can be generated and linked to each other to form a meaning structure. A data model is built when a proper keyword list is achieved and the model is applied to the design of the smart search engine

A Semantic Web Based Search Engine with X3D Visualisation of Queries and Results

Parts of this PhD have been published: Gkoutzis, Konstantinos, and Vladimir Geroimenko. "Moving from Folksonomies to Taxonomies: Using the Social Web and 3D to Build an Unlimited Semantic Ontology." Proceedings of the 2011 15th International Conference on Information Visualisation. IEEE Computer Society, 2011.The Semantic Web project has introduced new techniques for managing information. Data can now be organised more efficiently and in such a way that computers can take advantage of the relationships that characterise the given input to present more relevant output. Semantic Web based search engines can quickly educe exactly what is needed to be found and retrieve it while avoiding information overload. Up until now, search engines have interacted with their users by asking them to look for words and phrases. We propose the creation of a new generation Semantic Web search engine that will offer a visual interface for queries and results. To create such an engine, information input must be viewed not merely as keywords, but as specific concepts and objects which are all part of the same universal system. To make the manipulation of the interconnected visual objects simpler and more natural, 3D graphics are utilised, based on the X3D Web standard, allowing users to semantically synthesise their queries faster and in a more logical way, both for them and the computer

Foraging in semantic fields : how we search through memory

When searching for concepts in memory-as in the verbal fluency task of naming all the animals one can think of-people appear to explore internal mental representations in much the same way that animals forage in physical space: searching locally within patches of information before transitioning globally between patches. However, the definition of the patches being searched in mental space is not well specified. Do we search by activating explicit predefined categories (e.g., pets) and recall items from within that category (categorical search), or do we activate and recall a connected sequence of individual items without using categorical information, with each item recalled leading to the retrieval of an associated item in a stream (associative search), or both? Using semantic representations in a search of associative memory framework and data from the animal fluency task, we tested competing hypotheses based on associative and categorical search models. Associative, but not categorical, patch transitions took longer to make than position-matched productions, suggesting that categorical transitions were not true transitions. There was also clear evidence of associative search even within categorical patch boundaries. Furthermore, most individuals' behavior was best explained by an associative search model without the addition of categorical information. Thus, our results support a search process that does not use categorical information, but for which patch boundaries shift with each recall and local search is well described by a random walk in semantic space, with switches to new regions of the semantic space when the current region is depleted

Semantic Shopping: A Literature Study

The digitalization of the economy and society overall has a significant impact on customers’ shopping behavior. After being conditioned by experiences in entertainment or simple Internet search, customers increasingly expect that a smart shopping assistant understands his/her shopping intentions and transfers these to shopping recommendations. Thus, the emerging opportunity in this context is to facilitate an intention-based shopping experience similar to the way semantic search engines provide responses to enquiries. In order to progress this new area, we differentiate alternative types of shopping intentions to provide the first set of conversation patterns. Grounded in the Speech Act Theory and a structured literature review, semantic shopping is defined and different types of shopping intentions are deduced

Comparing the Performance of Information Retrieval of Semantic and Keyword Search Engines Based on Phrase Search

Purpose: The aim of the research is to compare the performance of Information Retrieval of semantic and keyword search engines based on phrase search (simple & complex). Methodology: The present applied and semi-experimental research community includes all active search engines on the web. Research samples were selected based on stratified random sampling and purposive sampling. The data collection tool of two researcher-made checklists includes ten simple and complex phrase queries. Findings: Bing and Cluuz (with similar precision of 53%), DuckDuckGo, and Yahoo were the most accurate in searching for simple phrases, respectively. Bing, DuckDuckGo, Yahoo, and Cluuz were the most accurate in their search for complex terms, respectively. In general, Bing, DuckDuckGo, Cluuz and Yahoo have the highest precision, respectively. Also, the average total precision of keyword search engines is more heightened than semantic search engines. Conclusion: The Bing keyword search engine performs better than the other three semantic search engines and other keywords. Semantic search engines claim to have more capabilities in retrieving relevant information than keyword search engines. But in this study, it was found that Cluuz and DuckDuckGo do not excel in search terms over keyword search engines. These tools did not perform as well as semantic web search engines, and it seems that they have a long way to go to become real semantic search engines. And to achieve this, it is necessary to use the facilities, tools, modules, and emerging technologies of the new age, such as machine learning, deep learning, combining these modules with pervasive techniques, data mining, etc. Value: So far, not been compared the phrase search performance in the sample semantic and keyword search engines. And in this regard, the researcher has tried to achieve an actual result with an exact Survey

The Syzygy Surfer: Creative technology for the World Wide Web

Conference paper given at WebSci11.This paper discusses our development of a new Web engine, the Syzygy Surfer, which aims to induce a search/browsing experience that is more creative than traditional search. We do this by purposefully combining the ambiguity of natural language with the precision of Semantic Web technologies. Here we set out the framework for our investigation and discuss the context and background ideas that are informing the research. The paper offers some preliminary examples taken from our work in progress on the device and suggests the way ahead for future developments and applications