FunMap: Efficient Execution of Functional Mappings for Knowledge Graph Creation

Data has exponentially grown in the last years, and knowledge graphs constitute powerful formalisms to integrate a myriad of existing data sources. Transformation functions -- specified with function-based mapping languages like FunUL and RML+FnO -- can be applied to overcome interoperability issues across heterogeneous data sources. However, the absence of engines to efficiently execute these mapping languages hinders their global adoption. We propose FunMap, an interpreter of function-based mapping languages; it relies on a set of lossless rewriting rules to push down and materialize the execution of functions in initial steps of knowledge graph creation. Although applicable to any function-based mapping language that supports joins between mapping rules, FunMap feasibility is shown on RML+FnO. FunMap reduces data redundancy, e.g., duplicates and unused attributes, and converts RML+FnO mappings into a set of equivalent rules executable on RML-compliant engines. We evaluate FunMap performance over real-world testbeds from the biomedical domain. The results indicate that FunMap reduces the execution time of RML-compliant engines by up to a factor of 18, furnishing, thus, a scalable solution for knowledge graph creation

Sharing knowledge in digital ecosystems using semantic multimedia big data

The use of formal representations has a basic importance in the era of big data. This need is more evident in the context of multimedia big data due to the intrinsic complexity of this type of data. Furthermore, the relationships between objects should be clearly expressed and formalized to give the right meaning to the correlation of data. For this reason the design of formal models to represent and manage information is a necessary task to implement intelligent information systems. Approaches based on the semantic web need to improve the data models that are the basis for implementing big data applications. Using these models, data and information visualization becomes an intrinsic and strategic task for the analysis and exploration of multimedia Big Data. In this article we propose the use of a semantic approach to formalize the structure of a multimedia Big Data model. Moreover, the identification of multimodal features to represent concepts and linguistic-semantic properties to relate them is an effective way to bridge the gap between target semantic classes and low-level multimedia descriptors. The proposed model has been implemented in a NoSQL graph database populated by different knowledge sources. We explore a visualization strategy of this large knowledge base and we present and discuss a case study for sharing information represented by our model according to a peer-to-peer(P2P) architecture. In this digital ecosystem, agents (e.g. machines, intelligent systems, robots,..) act like interconnected peers exchanging and delivering knowledge with each other

Association of serum levels of fibrosis-related biomarkers with disease activity in patients with IgG4-related disease

Background: The aim of this study was to identify fibrosis-related serological surrogate outcome measures in patients with immunoglobulin G4-related disease (IgG4-RD). Methods: This was a clinical observational study of 72 patients with untreated IgG4-RD from four institutions in Japan.The serum concentrations of growth differentiation factor 15 (GDF-15), CCL2, hyaluronic acid (HA), amino-terminal propeptide of type III procollagen (PIIINP), and tissue inhibitor of metalloproteinases 1 (TIMP-1) were measured by enzyme-linked immunosorbent assays. The enhanced liver fibrosis (ELF) score was calculated from the TIMP-1, PIIINP, and HA values. We evaluated associations between the values of these biomarkers and laboratory data, the IgG4-RD responder index (IgG4-RD RI) score, and organ involvements. Results: Compared with the 44 healthy controls, the patients with IgG4-RD showed significantly elevated serum concentrations of GDF-15, MCP-1, HA, PIIINP, and TIMP-1 and ELF scores. The patients\u27 serum concentrations of GDF-15, CCL2, HA, and TIMP-1 (but not PIIINP) were positively correlated with each other. Among them, serum GDF-15 most efficiently distinguished patients with IgG4-RD from healthy controls. Serum GDF-15 was not associated with the IgG4-RD RI score or the number of organ involvements but was independently associated with the presence of retroperitoneal fibrosis and with parotid gland involvement. Conclusions: We observed increased serological surrogate outcome measures of fibrosis in IgG4-RD. GDF-15 may precisely reflect the fibrotic degree in patients with IgG4-RD