The Ontop framework for ontology based data access

Ontology Based Data Access Ontology Based Data Access (OBDA) Formally, an OBDA system is a triple O = T , S, M , where: -T is the intensional level of an ontology. We consider ontologies formalized in description logics (DLs), hence T is a DL TBox. -S is a relational database representing the sources. -M is a set of mapping assertions, each one of the form • Φ(x) is a query over S, returning tuples of values for x • Ψ (x) is a query over T whose free variables are from x. The main functionality of OBDA systems is query answering. A schematic description of the query transformation process (usually SPARQL to SQL) performed by a typical OBDA system is provided i

Situated Learning among Open Source Software Developers : The Case of Google Chrome Project

The presence of learning in organizations is important for success and survival. Recent research into open source software developers has primarily suggested a social constructivist view where knowledge is constructed in the social relationships within the team culture. I report results from a case study that investigated the presence of situated learning among open source developers at an earlier time of a project. Thirty-eight developers were systematically selected and examined on their performance, experience and roles during ten months of maintenance work. I followed a model of learning curve effects that associated the improvement in the average resolving time with the accumulated experience. I found a strong relationship between the two variables and confirmed the presence of learning. In addition, I found a less convincing evidence to affirm knowledge depreciated among open source software developers. The depreciation factor was estimated to be 94 percent, compared to other studies which ranged between 65 to 85 percent. An additional investigation was conducted around the organization structure to understand whether core and peripheral members have different average resolving time. The finding was inconclusive to claim both groups have different means towards issue resolution. The consistency in the result of this thesis and several other related research efforts suggests that learning is likely to be an intrinsic characteristic of open source software development rather than just a speculation in the literatures

The scalable precision medicine open knowledge engine (SPOKE): a massive knowledge graph of biomedical information.

MOTIVATION: Knowledge graphs (KGs) are being adopted in industry, commerce and academia. Biomedical KG presents a challenge due to the complexity, size and heterogeneity of the underlying information. RESULTS: In this work, we present the Scalable Precision Medicine Open Knowledge Engine (SPOKE), a biomedical KG connecting millions of concepts via semantically meaningful relationships. SPOKE contains 27 million nodes of 21 different types and 53 million edges of 55 types downloaded from 41 databases. The graph is built on the framework of 11 ontologies that maintain its structure, enable mappings and facilitate navigation. SPOKE is built weekly by python scripts which download each resource, check for integrity and completeness, and then create a \u27parent table\u27 of nodes and edges. Graph queries are translated by a REST API and users can submit searches directly via an API or a graphical user interface. Conclusions/Significance: SPOKE enables the integration of seemingly disparate information to support precision medicine efforts. AVAILABILITY AND IMPLEMENTATION: The SPOKE neighborhood explorer is available at https://spoke.rbvi.ucsf.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

The scalable precision medicine open knowledge engine (SPOKE): a massive knowledge graph of biomedical information

MotivationKnowledge graphs (KGs) are being adopted in industry, commerce and academia. Biomedical KG presents a challenge due to the complexity, size and heterogeneity of the underlying information.ResultsIn this work, we present the Scalable Precision Medicine Open Knowledge Engine (SPOKE), a biomedical KG connecting millions of concepts via semantically meaningful relationships. SPOKE contains 27 million nodes of 21 different types and 53 million edges of 55 types downloaded from 41 databases. The graph is built on the framework of 11 ontologies that maintain its structure, enable mappings and facilitate navigation. SPOKE is built weekly by python scripts which download each resource, check for integrity and completeness, and then create a 'parent table' of nodes and edges. Graph queries are translated by a REST API and users can submit searches directly via an API or a graphical user interface. Conclusions/Significance: SPOKE enables the integration of seemingly disparate information to support precision medicine efforts.Availability and implementationThe SPOKE neighborhood explorer is available at https://spoke.rbvi.ucsf.edu.Supplementary informationSupplementary data are available at Bioinformatics online