Exploiting synergy between ontologies and recommender systems

Recommender systems learn about user preferences over time, automatically finding things of similar interest. This reduces the burden of creating explicit queries. Recommender systems do, however, suffer from cold-start problems where no initial information is available early on upon which to base recommendations.Semantic knowledge structures, such as ontologies, can provide valuable domain knowledge and user information. However, acquiring such knowledge and keeping it up to date is not a trivial task and user interests are particularly difficult to acquire and maintain. This paper investigates the synergy between a web-based research paper recommender system and an ontology containing information automatically extracted from departmental databases available on the web. The ontology is used to address the recommender systems cold-start problem. The recommender system addresses the ontology's interest-acquisition problem. An empirical evaluation of this approach is conducted and the performance of the integrated systems measured

Virtual Machines Embedding for Cloud PON AWGR and Server Based Data Centres

In this study, we investigate the embedding of various cloud applications in PON AWGR and Server Based Data Centres

Exploiting Synergy Between Ontologies and Recommender Systems

Recommender systems learn about user preferences over time, automatically finding things of similar interest. This reduces the burden of creating explicit queries. Recommender systems do, however, suffer from cold-start problems where no initial information is available early on upon which to base recommendations. Semantic knowledge structures, such as ontologies, can provide valuable domain knowledge and user information. However, acquiring such knowledge and keeping it up to date is not a trivial task and user interests are particularly difficult to acquire and maintain. This paper investigates the synergy between a web-based research paper recommender system and an ontology containing information automatically extracted from departmental databases available on the web. The ontology is used to address the recommender systems cold-start problem. The recommender system addresses the ontology's interest-acquisition problem. An empirical evaluation of this approach is conducted and the performance of the integrated systems measured

Web based knowledge extraction and consolidation for automatic ontology instantiation

The Web is probably the largest and richest information repository available today. Search engines are the common access routes to this valuable source. However, the role of these search engines is often limited to the retrieval of lists of potentially relevant documents. The burden of analysing the returned documents and identifying the knowledge of interest is therefore left to the user. The Artequakt system aims to deploy natural language tools to automatically ex-tract and consolidate knowledge from web documents and instantiate a given ontology, which dictates the type and form of knowledge to extract. Artequakt focuses on the domain of artists, and uses the harvested knowledge to gen-erate tailored biographies. This paper describes the latest developments of the system and discusses the problem of knowledge consolidation

Using Protege for automatic ontology instantiation

This paper gives an overview on the use of Protégé in the Artequakt system, which integrated Protégé with a set of natural language tools to automatically extract knowledge about artists from web documents and instantiate a given ontology. Protégé was also linked to structured templates that generate documents from the knowledge fragments it maintains

Generating adaptive hypertext content from the semantic web

Accessing and extracting knowledge from online documents is crucial for therealisation of the Semantic Web and the provision of advanced knowledge services. The Artequakt project is an ongoing investigation tackling these issues to facilitate the creation of tailored biographies from information harvested from the web. In this paper we will present the methods we currently use to model, consolidate and store knowledge extracted from the web so that it can be re-purposed as adaptive content. We look at how Semantic Web technology could be used within this process and also how such techniques might be used to provide content to be published via the Semantic Web

Shear design of HSC beams with combination of links and horizontal web steel

The existing recommendations in Eurocode 2 and the British Code of Practice for the shear design of beams are derived from research conducted essentially on normal-strength concrete (NSC) with cube strengths up to 50 MPa, and it was found that the shear strengths of high-strength concrete (HSC) members made with limestone aggregate are below the characteristic resistances of identical NSC members. Previous experimental tests have also shown that significant differences exist in the angle of crack of shear failure of NSC and HSC. This paper presents data from five beam tests, which demonstrate that HSC with limestone aggregate has a reduced shear strength compared with NSC made with gravel and thus shows a gap in knowledge in the design approach to shear resistance of HSC beams. Previous investigations have suggested that horizontal web steels can contribute to the overall shear resistance of a reinforced concrete member in conjunction with the other constituents, concrete, tension and shear steel. The paper also presents data from tests on 11 beam tests and shows that the shear resistance of HSC beams is highly dependent on dowel action resulting from horizontal web bars positioned at the centre of the depth of the beam. Past attempts to quantify this dowel action are investigated and an improved design rule is proposed

Increased serum IL-6 level time-dependently regulates hyperalgesia and spinal mu opioid receptor expression during CFA-induced arthritis

Interleukin (IL)-6 is known to cause pro- and anti-inflammatory effects during different stages of inflammation. Recent therapeutic investigations have focused on treatment of various inflammatory disorders with anti-cytokine substances. As a result, the aim of this study was to further elucidate the influence of IL-6 in hyperalgesia and edema during different stages of Complete Freund’s Adjuvant (CFA)-induced arthritis (AA) in male Wistar rats. AA was induced by a single subcutaneous injection of CFA into the rats’ hindpaw. Anti-IL-6 was administered either daily or weekly during the 21 days of study. Spinal mu opioid receptor (mOR) expression was detected by Western blotting. Daily and weekly treatment with an anti-IL-6 antibody significantly decreased paw edema in the AA group compared to the AA control group. Additionally, daily and weekly anti-IL-6 administration significantly reduced hyperalgesia on day 7 in the AA group compared to the AA control group; however, there were significant increases in hyperalgesia in the antibody-treated group on days 14 and 21 compared to the AA control group. IL-6 antibody-induced increases in hyperalgesia on the 14th and 21st days after CFA injection correlated with a time-dependent, significant reduction in spinal mOR expression during anti-IL-6 treatment. Our study confirmed the important time-dependent relationship between serum IL-6 levels and hyperalgesia during AA. These results suggest that the stages of inflammation in AA must be considered for anti-hyperalgesic and anti-inflammatory interventions via anti-IL-6 antibody treatment

Automatic extraction of knowledge from web documents

A large amount of digital information available is written as text documents in the form of web pages, reports, papers, emails, etc. Extracting the knowledge of interest from such documents from multiple sources in a timely fashion is therefore crucial. This paper provides an update on the Artequakt system which uses natural language tools to automatically extract knowledge about artists from multiple documents based on a predefined ontology. The ontology represents the type and form of knowledge to extract. This knowledge is then used to generate tailored biographies. The information extraction process of Artequakt is detailed and evaluated in this paper

Evaluating the semantic web: a task-based approach

The increased availability of online knowledge has led to the design of several algorithms that solve a variety of tasks by harvesting the Semantic Web, i.e. by dynamically selecting and exploring a multitude of online ontologies. Our hypothesis is that the performance of such novel algorithms implicity provides an insight into the quality of the used ontologies and thus opens the way to a task-based evaluation of the Semantic Web. We have investigated this hypothesis by studying the lessons learnt about online ontologies when used to solve three tasks: ontology matching, folksonomy enrichment, and word sense disambiguation. Our analysis leads to a suit of conclusions about the status of the Semantic Web, which highlight a number of strengths and weaknesses of the semantic information available online and complement the findings of other analysis of the Semantic Web landscape