Injecting Background Knowledge into Embedding Models for Predictive Tasks on Knowledge Graphs

Embedding models have been successfully exploited for Knowledge Graph refinement. In these models, the data graph is projected into a low-dimensional space, in which graph structural information are preserved as much as possible, enabling an efficient computation of solutions. We propose a solution for injecting available background knowledge (schema axioms) to further improve the quality of the embeddings. The method has been applied to enhance existing models to produce embeddings that can encode knowledge that is not merely observed but rather derived by reasoning on the available axioms. An experimental evaluation on link prediction and triple classification tasks proves the improvement yielded implementing the proposed method over the original ones

Learning terminological Naïve Bayesian classifiers under different assumptions on missing knowledge

Knowledge available through Semantic Web standards can easily be missing, generally because of the adoption of the Open World Assumption (i.e. the truth value of an assertion is not necessarily known). However, the rich relational structure that characterizes ontologies can be exploited for handling such missing knowledge in an explicit way. We present a Statistical Relational Learning system designed for learning terminological naïve Bayesian classifiers, which estimate the probability that a generic individual belongs to the target concept given its membership to a set of Description Logic concepts. During the learning process, we consistently handle the lack of knowledge that may be introduced by the adoption of the Open World Assumption, depending on the varying nature of the missing knowledge itself

A graph regularization based approach to transductive class-membership prediction

Considering the increasing availability of structured machine processable knowledge in the context of the Semantic Web, only relying on purely deductive inference may be limiting. This work proposes a new method for similarity-based class-membership prediction in Description Logic knowledge bases. The underlying idea is based on the concept of propagating class-membership information among similar individuals; it is non-parametric in nature and characterised by interesting complexity properties, making it a potential candidate for large-scale transductive inference. We also evaluate its effectiveness with respect to other approaches based on inductive inference in SW literature

Message from the ICSC 2012 workshop co-chairs

Welcome to the proceedings containing the papers from two workshops selected for presentation at the Sixth IEEE International Conference on Semantic Computing (ICSC 2012) in Palermo, Italy, September 19–21, 2012

Mechanical and physical characterization of papercrete as new eco-friendly construction material

The manufacturing of Portland cement is responsible for a big amount of energy and greenhouse gas (GHG) emission. Therefore, to date, it is imperative to find alternative materials to replace a major part of cement for sustainable concrete constructions. The present study forms a part of an on-going research project on the application of new cementitious matrices produced using different types of recycled materials. In particular, it focuses on the use of pulp and waste paper to partially replace Portland cement at varying percentages for producing a new lightweight mortar, frequently named papercrete. The development of this economical and eco-friendly material may permit of recycling a big amount of waste paper leading to lower housing costs with also ecological benefits. To this scope, an experimental campaign in the laboratory is carried out to characterize this new innovative material from a physical and mechanical point of view. The preliminary results of this on-going experimental campaign are illustrated and commented on in this paper. The obtained results confirm the possibility of applying this partially-recycled material as a possible alternative for strengthening existing panels of masonry

Fair graph representation learning: Empowering NIFTY via Biased Edge Dropout and Fair Attribute Preprocessing

The increasing complexity and amount of data available in modern applications strongly demand Trustworthy Learning algorithms that can be fed directly with complex and large graphs data. In fact, on one hand, machine learning models must meet high technical standards (e.g., high accuracy with limited computational requirements), but, at the same time, they must be sure not to discriminate against subgroups of the population (e.g., based on gender or ethnicity). Graph Neural Networks (GNNs) are currently the most effective solution to meet the technical requirements, even if it has been demonstrated that they inherit and amplify the biases contained in the data as a reflection of societal inequities. In fact, when dealing with graph data, these biases can be hidden not only in the node attributes but also in the connections between entities. Several Fair GNNs have been proposed in the literature, with uNIfying Fairness and stabiliTY (NIFTY) (Agarwal et al., 2021) being one of the most effective. In this paper, we will empower NIFTY's fairness with two new strategies. The first one is a Biased Edge Dropout, namely, we drop graph edges to balance homophilous and heterophilous sensitive connections, mitigating the bias induced by subgroup node cardinality. The second one is Attributes Preprocessing, which is the process of learning a fair transformation of the original node attributes. The effectiveness of our proposal will be tested on a series of datasets with increasingly challenging scenarios. These scenarios will deal with different levels of knowledge about the entire graph, i.e., how many portions of the graph are known and which sub-portion is labelled at the training and forward phases

Multiple mortality modeling in Poisson Lee-Carter framework

The academic literature in longevity field has recently focused on models for detecting multiple population trends (D'Amato et al., 2012b; Njenga and Sherris, 2011; Russolillo et al., 2011, etc.). In particular, increasing interest has been shown about "related" population dynamics or "parent" populations characterized by similar socioeconomic conditions and eventually also by geographical proximity. These studies suggest dependence across multiple populations and common long-run relationships between countries (for instance, see Lazar et al., 2009). In order to investigate cross-country longevity common trends, we adopt a multiple population approach. The algorithm we propose retains the parametric structure of the Lee-Carter model, extending the basic framework to include some cross-dependence in the error term. As far as time dependence is concerned, we allow for all idiosyncratic components (both in the common stochastic trend and in the error term) to follow a linear process, thus considering a highly flexible specification for the serial dependence structure of our data. We also relax the assumption of normality, which is typical of early studies on mortality (Lee and Carter, 1992) and on factor models (see e.g., the textbook by Anderson, 1984). The empirical results show that the multiple Lee-Carter approach works well in the presence of dependence

Rapid evolution of female-biased genes among four species of Anopheles malaria mosquitoes.

Understanding how phenotypic differences between males and females arise from the sex-biased expression of nearly identical genomes can reveal important insights into the biology and evolution of a species. Among Anopheles mosquito species, these phenotypic differences include vectorial capacity, as it is only females that blood feed and thus transmit human malaria. Here, we use RNA-seq data from multiple tissues of four vector species spanning the Anopheles phylogeny to explore the genomic and evolutionary properties of sex-biased genes. We find that, in these mosquitoes, in contrast to what has been found in many other organisms, female-biased genes are more rapidly evolving in sequence, expression, and genic turnover than male-biased genes. Our results suggest that this atypical pattern may be due to the combination of sex-specific life history challenges encountered by females, such as blood feeding. Furthermore, female propensity to mate only once in nature in male swarms likely diminishes sexual selection of post-reproductive traits related to sperm competition among males. We also develop a comparative framework to systematically explore tissue- and sex-specific splicing to document its conservation throughout the genus and identify a set of candidate genes for future functional analyses of sex-specific isoform usage. Finally, our data reveal that the deficit of male-biased genes on the X Chromosomes in Anopheles is a conserved feature in this genus and can be directly attributed to chromosome-wide transcriptional regulation that de-masculinizes the X in male reproductive tissues

In Situ X-ray Diffraction Investigation of the Crystallisation of Perfluorinated CeIV-Based Metal–Organic Frameworks with UiO-66 and MIL-140 Architectures

We report on the results of an in situ synchrotron powder X-ray diffraction study of the crystallisation in aqueous medium of two recently discovered perfluorinated CeIV-based metal–organic frameworks (MOFs), analogues of the already well investigated ZrIV-based UiO-66 and MIL-140A, namely, F4_UiO-66(Ce) and F4_MIL-140A(Ce). The two MOFs were originally obtained in pure form in similar conditions, using ammonium cerium nitrate and tetrafluoroterephthalic acid as reagents, and small variations of the reaction parameters were found to yield mixed phases. Here, we investigate the crystallisation of these compounds, varying parameters such as temperature, amount of the protonation modulator nitric acid and amount of the coordination modulator acetic acid. When only HNO3 is present in the reaction environment, only F4_MIL-140A(Ce) is obtained. Heating preferentially accelerates nucleation, which becomes rate determining below 57 °C. Upon addition of AcOH to the system, alongside HNO3, mixed-phased products are obtained. F4_UiO-66(Ce) is always formed faster, and no interconversion between the two phases occurs. In the case of F4_UiO-66(Ce), crystal growth is always the rate-determining step. A higher amount of HNO3 favours the formation of F4_MIL-140A(Ce), whereas increasing the amount of AcOH favours the formation of F4_UiO-66(Ce). Based on the in situ results, a new optimised route to achieving a pure, high-quality F4_MIL-140A(Ce) phase in mild conditions (60 °C, 1 h) is also identified