“Our Knowledge Engineering activities intend to help data consumers and publishers to manipulate the data by providing innovative ways to search for relevant datasets/vocabularies and by providing new insights into their own data/vocabulary use.”
“With knowledege graphs, scientists have now the means to aggregate semantic information from various sources. The challenge is not only to extract meaningful information from this data, but to gain knowledge, to discover previously unknown insight, look for patterns, and to make sense of the data.”
LOD4All public service leverages Linked Open Data search and consumption by providing services for human and computer to search/access/download over more than 40 billion entities (schema and instances) that compose the Linked Open Data network. LOD4All service is reusing the LOD cloud diagram and allows filtering data by domain, license, etc.Read more
SPARQL Endpoint Status (SPARQLES) application is designed to monitor SPARQL Endpoints. It measures the availability of the service, the compliancy with version 1 and 1.1 of the standard, the performance using representative query patterns and the discoverability by machines on the Web.Read more
Hikaku prototype provides new insight in company reports data by combining data from various sources such as US SEC reports, Crunchbase, Wikipedia or New York Times articles. It enables the joint use of financial data from complementary database and provides contextual information for a financial event (e.g. explanation from newspaper articles of a sudden drop in a company stock price).Read more
Linkspire prototype provides new insight in Adverse Drug Reaction by combining data from various sources such as Sider, DrugBank, Diseasome or PubMed bibliographic database. We apply innovative semantic based technologies to make sense of Big Data. Such approach allows us to make explicit Adverse Drug Reactions facts from scattered latent information.Read more
Facilitating Prediction of Adverse Drug Reactions by Using Knowledge Graphs and Multi-Label Learning Models (Journal Article)
Briefings in Bioinformatics, pp. 1-13, 2017.
Identifying Equivalent Relation Paths in Knowledge Graphs (Inproceeding)
Gracia, Jorge; Bond, Francis; McCrae, John; Buitelaar, Paul; Chiarcos, Christian; Hellmann, Sebastian (Ed.): Language, Data, and Knowledge: First International Conference, LDK 2017, Galway, Ireland, June 19-20, 2017, Proceedings, pp. 299–314, Springer International Publishing, 2017.
Mining Cardinalities from Knowledge Bases (Inproceeding)
Benslimane, Djamal; Damiani, Ernesto; Grosky, William; Hameurlain, Abdelkader; Sheth, Amit; Wagner, Roland (Ed.): Database and Expert Systems Applications: 28th International Conference, DEXA 2017, Lyon, France, August 28-31, 2017, Proceedings, Part I, pp. 447–462, Springer International Publishing, 2017.