Semantic similarity estimation from multiple ontologies

The estimation of semantic similarity between words is an important task in many language related applications. In the past, several approaches to assess similarity by evaluating the knowledge modelled in an ontology have been proposed. However, in many domains, knowledge is dispersed through several partial and/or overlapping ontologies. Because most previous works on semantic similarity only support a unique input ontology, we propose a method to enable similarity estimation across multiple ontologies. Our method identifies different cases according to which ontology/ies input terms belong. We propose several heuristics to deal with each case, aiming to solve missing values, when partial knowledge is available, and to capture the strongest semantic evidence that results in the most accurate similarity assessment, when dealing with overlapping knowledge. We evaluate and compare our method using several general purpose and biomedical benchmarks of word pairs whose similarity has been assessed by human experts, and several general purpose (WordNet) and biomedical ontologies (SNOMED CT and MeSH). Results show that our method is able to improve the accuracy of similarity estimation in comparison to single ontology approaches and against state of the art related works in multi-ontology similarity assessment.     

Gold-Containing dendrimers: A new class of macromolecules

The synthesis of dendrimers possessing gold atoms on the surface or inside the internal layers is reported. They have been shown to be precursors to mixed gold-transition metal cluster-containing dendrimers, which represent a new class of hybrid materials. The latter species are rather soluble in common solvents and can be characterized by spectroscopic methods. Moreover, gold-containing dendrimers are useful for making gold nanoparticles, which have very interesting electronic, optical, and catalytic properties.     

Microstructure and physico-chemical bases of textural quality of yam products

The texture of pounded yam is the main attribute of this traditional dish, one of the preferred ways of consuming yam in West Africa. We integrated functional properties and cell microstructure to describe or predict the textural quality of pounded yam. The firmness and adhesiveness of pounded yam prepared from six cultivars were measured. In parallel, the thermo-mechanical properties (DMA, DSC) and starch behaviour were also determined while the structure of raw, cooked and pounded yam was observed using Scanning Electron Microscopy, Light Microscopy and Confocal Laser Scanning Microscopy. No significant correlation was found linking DMA, DSC measurements with the textural quality of pounded yam (adhesiveness, firmness). Conversely, multiple regressions showed that 75% of the variation in firmness could be explained by the dry matter, soluble starch and amylose content of the pounded yam. In addition, CLSM revealed a thicker cell wall in Florido, a cultivar known for its bad pounding ability. We hypothesize that pectin, the major component of cell wall middle lamella, plays a role in the textural quality of pounded yam.     

The Displacement Calculus

If all dependent expressions were adjacent some variety of immediate constituent analysis would suffice for grammar, but syntactic and semantic mismatches are characteristic of natural language; indeed this is a, or the, central problem in grammar. Logical categorial grammar reduces grammar to logic: an expression is well-formed if and only if an associated sequent is a theorem of a categorial logic. The paradigmatic categorial logic is the Lambek calculus, but being a logic of concatenation the Lambek calculus can only capture discontinuous dependencies when they are peripheral. In this paper we present the displacement calculus, which is a logic of intercalation as well as concatenation and which subsumes the Lambek calculus. On the empirical side, we apply the new calculus to discontinuous idioms, quantification, VP ellipsis, medial extraction, pied-piping, appositive relativisation, parentheticals, gapping, comparative subdeletion, cross-serial dependencies, reflexivization, anaphora, dative alternation, and particle shift. On the technical side, we prove that the calculus enjoys Cut-elimination.     

Knowledge-driven delivery of home care services

Home Care (HC) assistance is emerging as an effective and efficient alternative to institutionalized care, especially for the case of senior patients that present multiple co-morbidities and require life long treatments under continuous supervision. The care of such patients requires the definition of specially tailored treatments and their delivery involves the coordination of a team of professionals from different institutions, requiring the management of many kinds of knowledge (medical, organizational, social and procedural). The K4Care project aims to assist the HC of elderly patients by proposing a standard HC model and implementing it in a knowledge-driven e-health platform aimed to support the provision of HC services. This paper focuses on two knowledge-based personalization aspects incorporated in the platform that aim to overcome the difficulties of HC delivery. The first one is the assistance to medical practitioners in the process of defining a customized treatment adjusted to the medical and social conditions of a particular patient in order to consider multiple co-morbidities. The second one is the possibility of tailoring the profiles of the care professionals according to the medical and organizational daily requirements in order to allow a flexible care delivery. Those two aspects, guided by the knowledge explicitly represented in the platform, play a crucial role in the medical and social acceptance of this kind of e-health systems. The paper also includes a real case study designed and tested by healthcare professionals and includes encouraging results from the test of the platform in a real health care environment in the city of Pollenza (Italy).     

Spectrum sharing in cognitive radio networks with imperfect sensing: A discrete-time Markov model

An efficient and utmost utilization of currently scarce and underutilized radio spectrum resources has stimulated the introduction of what has been coined Cognitive Radio (CR) access methodologies and implementations. While the long-established approach has been based on licensed (or primary) spectrum access, this new communication paradigm enables an opportunistic secondary access to shared spectrum resources provided mutual interference is kept below acceptable levels. In this paper we address the problem of primary-secondary spectrum sharing in cognitive radio access networks using a framework based on a Discrete Time Markov Chain (DTMC) model. Its applicability and advantages with respect to other approaches is explained and further justified. Spectrum awareness of primary activity by the secondary users is based on spectrum sensing techniques, which are modeled in order to capture sensing errors in the form of false-alarm and missed-detection. Model validation is successfully achieved by means of a system-level simulator which is able to capture the system behavior with high degree of accuracy. Parameter dependencies and potential tradeoffs are identified enabling an enhanced operation for both primary and secondary users. The suitability of the specified model is justified while allowing a wide range of extended implementations and enhanced capabilities to be considered.