7 research outputs found
Challenges in Bridging Social Semantics and Formal Semantics on the Web
This paper describes several results of Wimmics, a research lab which names
stands for: web-instrumented man-machine interactions, communities, and
semantics. The approaches introduced here rely on graph-oriented knowledge
representation, reasoning and operationalization to model and support actors,
actions and interactions in web-based epistemic communities. The re-search
results are applied to support and foster interactions in online communities
and manage their resources
Error-Tolerant RDF Subgraph Matching for Adaptive Presentation of Linked Data on Mobile
International audienceWe present PRISSMA, a context-aware presentation layer for Linked Data. PRISSMA extends the Fresnel vocabulary with the notion of mobile context. Besides, it includes an algorithm that determines whether the sensed context is compatible with some context declarations. The algorithm finds optimal error-tolerant subgraph isomorphisms between RDF graphs using the notion of graph edit distance and is sublinear in the number of context declarations in the system
Access Control for HTTP Operations on Linked Data
International audienceAccess control is a recognized open issue when interacting with RDF using HTTP methods. In literature, authentication and authorization mechanisms either introduce undesired complexity such as SPARQL and ad-hoc policy languages, or rely on basic access control lists, thus resulting in limited policy expressiveness. In this paper we show how the Shi3ld attribute-based authorization framework for SPARQL endpoints has been progressively converted to protect HTTP operations on RDF. We proceed by steps: we start by supporting the SPARQL 1.1 Graph Store Protocol, and we shift towards a SPARQL-less solution for the Linked Data Platform. We demonstrate that the resulting authoriza- tion framework provides the same functionalities of its SPARQL-based counterpart, including the adoption of Semantic Web languages only
Accurate prediction of kinase-substrate networks using knowledge graphs
Phosphorylation of specific substrates by protein kinases is a key control mechanism for
vital cell-fate decisions and other cellular processes. However, discovering specific kinasesubstrate relationships is time-consuming and often rather serendipitous. Computational predictions alleviate these challenges, but the current approaches suffer from limitations like restricted kinome coverage and inaccuracy. They also typically utilise only local features without reflecting broader interaction context. To address these limitations, we have developed an alternative predictive model. It uses statistical relational learning on top of phosphorylation networks interpreted as knowledge graphs, a simple yet robust model for representing networked knowledge. Compared to a representative selection of six existing systems, our model has the highest kinome coverage and produces biologically valid highconfidence predictions not possible with the other tools. Specifically, we have experimentally validated predictions of previously unknown phosphorylations by the LATS1, AKT1,
PKA and MST2 kinases in human. Thus, our tool is useful for focusing phosphoproteomic
experiments, and facilitates the discovery of new phosphorylation reactions. Our model can
be accessed publicly via an easy-to-use web interface (LinkPhinder)
Accurate prediction of kinase-substrate networks using knowledge graphs
Phosphorylation of specific substrates by protein kinases is a key control mechanism for
vital cell-fate decisions and other cellular processes. However, discovering specific kinasesubstrate relationships is time-consuming and often rather serendipitous. Computational predictions alleviate these challenges, but the current approaches suffer from limitations like restricted kinome coverage and inaccuracy. They also typically utilise only local features without reflecting broader interaction context. To address these limitations, we have developed an alternative predictive model. It uses statistical relational learning on top of phosphorylation networks interpreted as knowledge graphs, a simple yet robust model for representing networked knowledge. Compared to a representative selection of six existing systems, our model has the highest kinome coverage and produces biologically valid highconfidence predictions not possible with the other tools. Specifically, we have experimentally validated predictions of previously unknown phosphorylations by the LATS1, AKT1,
PKA and MST2 kinases in human. Thus, our tool is useful for focusing phosphoproteomic
experiments, and facilitates the discovery of new phosphorylation reactions. Our model can
be accessed publicly via an easy-to-use web interface (LinkPhinder)