It is widely recognized today that the management of imprecision and vagueness will yield more intelligent and realistic knowledge-based applications. Description Logics (DLs) are a family of knowledge representation languages that have gained considerable attention the last decade, mainly due to their decidability and the existence of empirically high performance of reasoning algorithms. In this paper, we extend the well known fuzzy ALC DL to the fuzzy SHIN DL, which extends the fuzzy ALC DL with transitive role axioms (S), inverse roles (I), role hierarchies (H) and number restrictions (N). We illustrate why transitive role axioms are difficult to handle in the presence of fuzzy interpretations and how to handle them properly. Then we extend these results by adding role hierarchies and finally number restrictions. The main contributions of the paper are the decidability proof of the fuzzy DL languages fuzzy-SI and fuzzy-SHIN, as well as decision procedures for the knowledge base satisfiability problem of the fuzzy-SI and fuzzy-SHIN

Abstract. Ontologies are today a key part of every knowledge based system. They provide a source of shared and precisely defined terms, resulting in system interoperability by knowledge sharing and reuse. Unfortunately, the variety of ways that a domain can be conceptualized results in the creation of different ontologies with contradicting or overlapping parts. For this reason ontologies need to be brought into mutual agreement (aligned). One important method for ontology alignment is the comparison of class and property names of ontologies using stringdistance metrics. Today quite a lot of such metrics exist in literature. But all of them have been initially developed for different applications and fields, resulting in poor performance when applied in this new domain. In the current paper we present a new string metric for the comparison of names which performs better on the process of ontology alignment as well as to many other field matching problems.

Abstract. The OWL 2 QL profile has been designed to facilitate query answering via query rewriting. This paper presents an optimized query rewriting algorithm which takes advantage of the special characteristics of the query rewriting problem via first-order resolution in OWL 2 QL and computes efficiently the set of the non redundant rewritings of a user query, by avoiding blind and redundant inferences, as well as by reducing the need for extended query subsumption checks. The evaluation shows that in several cases the algorithm achieves a significant improvement and better scalability if compared to other similar approaches.

T h e S e m a n t i c W e b imprecise knowledge. More precisely, some applications deal with random information and events, others deal with imprecise and fuzzy knowledge, and still others deal with missing or distorted information-resulting in uncertainty. For example, in applications involving sensor readings, such measurements usually come with degrees of evidence; in applications like multimedia processing, object recognition might come with degrees of truth.To deal with uncertainty in the Semantic Web and its applications, many researchers have proposed extending OWL and the Description Logic (DL) formalisms with special mathematical frameworks. Researchers have proposed probabilistic, 1 possibilistic, 2 and fuzzy extensions, 3-5 among others. Researchers have studied fuzzy extensions most extensively, providing impressive results on semantics, reasoning algorithms, and implementations. Building on these results, we've created a fuzzy extension to OWL called Fuzzy OWL. Fuzzy OWL can capture imprecise and vague knowledge-for example, we can say that Athens is hot to a degree 0.8 rather than saying that Athens is either hot or not. Moreover, our reasoning platform, Fuzzy Reasoning Engine (FiRE), lets Fuzzy OWL capture and reason about such knowledge (see www.image.ece.ntua.gr/~nsimou).

The long-term survival of species with temperature-dependant sex determination requires a sufficient range of incubation temperatures to ensure that both males and females are produced. The primary sex ratio of sea turtles is determined by the temperature experienced by eggs during the middle third of incubation. Here, we investigated the variability in the production of male and female offspring by loggerhead sea turtles Caretta caretta at six nesting beaches in the temperate breeding area of Zakynthos, Greece. Hatchling sex ratios were estimated using incubation durations and sand temperatures for 2007-2009, while the empirical relationship between air and sand temperature was used to infer historical (1875-2010) and future (2011-2100) hatchling sex ratios. First, all six beaches produced males; 55% of production was across five beaches, while 45% was focused on one beach (primarily in July). Second, male production varied across the season in different years; there was an initial peak in June, with production (rising, declining or plateauing) later in the season being regulated by August air temperatures. Interestingly, the annual male production rate estimated from the 3-year dataset (23%) was half that estimated from the 135-year reconstruction (50%), with the latter showing broad interannual variation. Finally, modelled predictions of future sex ratios ranged from a conservative 7.6% decline in male production by 2100 versus no production by 2038. This study provides a baseline from which predicted trends in hatchling sex ratio, in parallel to regular field assessments, could be used to provide guidelines for the selection of appropriate nest protection management protocols, to maintain required sex ratios that safeguard the future of this population. bs_bs_banner Animal Conservation. Print ISSN 1367-9430 508 Animal Conservation 15 (2012) 508-518