Graph-based queries of Semantic Web integrated biological data

42011 MORE 1openIn the post-genomic era, life science researchers are faced with the need to manage and inspect a growing abundance of data and information. Data from different sources, both public and proprietary, have the most value when considered in the context of each other as they give more information. In order to answer questions that spans multiple fields in the biology domain without an integrated approach, a biologist needs to visit all data sources related to the problem and find relevant data. In the last years we have become witnesses of a growing interest for the Semantic Web technologies to integrate and query biological data. Semantic Web technologies were designed to meet the challenges of reduce the complexity of combining data from multiple sources, resolve the lack of widely accepted standards and manage highly distributed and mutable resources. However, Semantic Web standard technologies do not provide any tools to query integrated knowledge bases from a graph perspective, that is defining graph traversal patterns. For example, it is not possible to ask a query like "are enzyme A and compound B related?" without knowing the complete structure of the knowledge base. After exploring different alternatives we come up with the use of a graph traversal programming language on top of a triplestore in order to perform several path traversal queries on an integrated graph. We tested the feasibility of the approach integrating Uniprot, Gene Ontology, Chebi and Kegg resources posing queries of different complexity.openMoretto M.; Cestaro A.; Blanzieri E.; Velasco R.Moretto, M.; Cestaro, A.; Blanzieri, E.; Velasco, R

A Brief Study of Open Source Graph Databases

With the proliferation of large irregular sparse relational datasets, new storage and analysis platforms have arisen to fill gaps in performance and capability left by conventional approaches built on traditional database technologies and query languages. Many of these platforms apply graph structures and analysis techniques to enable users to ingest, update, query and compute on the topological structure of these relationships represented as set(s) of edges between set(s) of vertices. To store and process Facebook-scale datasets, they must be able to support data sources with billions of edges, update rates of millions of updates per second, and complex analysis kernels. These platforms must provide intuitive interfaces that enable graph experts and novice programmers to write implementations of common graph algorithms. In this paper, we explore a variety of graph analysis and storage platforms. We compare their capabil- ities, interfaces, and performance by implementing and computing a set of real-world graph algorithms on synthetic graphs with up to 256 million edges. In the spirit of full disclosure, several authors are affiliated with the development of STINGER.Comment: WSSSPE13, 4 Pages, 18 Pages with Appendix, 25 figure

Metrics for ranking ontologies

Representing knowledge using domain ontologies has shown to be a useful mechanism and format for managing and exchanging information. Due to the difficulty and cost of building ontologies, a number of ontology libraries and search engines are coming to existence to facilitate reusing such knowledge structures. The need for ontology ranking techniques is becoming crucial as the number of ontologies available for reuse is continuing to grow. In this paper we present AKTiveRank, a prototype system for ranking ontologies based on the analysis of their structures. We describe the metrics used in the ranking system and present an experiment on ranking ontologies returned by a popular search engine for an example query

Ontology ranking based on the analysis of concept structures

In view of the need to provide tools to facilitate the reuse of existing knowledge structures such as ontologies, we present in this paper a system, AKTiveRank, for the ranking of ontologies. AKTiveRank uses as input the search terms provided by a knowledge engineer and, using the output of an ontology search engine, ranks the ontologies. We apply a number of classical metrics in an attempt to investigate their appropriateness for ranking ontologies, and compare the results with a questionnaire-based human study. Our results show that AKTiveRank will have great utility although there is potential for improvement

The Graph Database: Jack of All Trades or Just Not SQL?

This special issue of IT Professional focuses on the graph database. The graph database, a relatively new phenomenon, is well suited to the burgeoning information era in which we are increasingly becoming immersed. Here, the guest editors briefly explain how a graph database works, its relation to the relational database management system (RDBMS), and its quantitative and qualitative pros and cons, including how graph databases can be harnessed in a hybrid environment. They also survey the excellent articles submitted for this special issue

Online Index Extraction from Linked Open Data Sources

The production of machine-readable data in the form of RDF datasets belonging to the Linked Open Data (LOD) Cloud is growing very fast. However, selecting relevant knowledge sources from the Cloud, assessing the quality and extracting synthetical information from a LOD source are all tasks that require a strong human effort. This paper proposes an approach for the automatic extraction of the more representative information from a LOD source and the creation of a set of indexes that enhance the description of the dataset. These indexes collect statistical information regarding the size and the complexity of the dataset (e.g. the number of instances), but also depict all the instantiated classes and the properties among them, supplying user with a synthetical view of the LOD source. The technique is fully implemented in LODeX, a tool able to deal with the performance issues of systems that expose SPARQL endpoints and to cope with the heterogeneity on the knowledge representation of RDF data. An evaluation on LODeX on a large number of endpoints (244) belonging to the LOD Cloud has been performed and the effectiveness of the index extraction process has been presented

Analysis of potential errors in technical products by combining knowledge graphs with MBSE approach

Technical products are developed to meet the demands of stakeholders. Therefore, the product's functions and associated properties are important. Various influencing factors e.g., external disturbances can have an impact on the input flows of the products or its characteristics and thus on the functions. If this leads to deviations between the required and as-is functions, these deviations are called errors. It is therefore important to analyze errors in product development and implement measures to increase the robustness of the product. Model-Based Systems Engineering (MBSE) supports the development of complex systems. However, MBSE alone has limited ability to identify in-depth errors. This requires knowledge of possible errors from previous products in specific contexts. For this purpose, the method proposed in this paper facilitates identifying errors in the concept phase by combining MBSE approaches with reusable knowledge (i.e., knowledge graph). The approach is presented using an application example for a mobile robot

Temporal Representation in Semantic Graphs

A wide range of knowledge discovery and analysis applications, ranging from business to biological, make use of semantic graphs when modeling relationships and concepts. Most of the semantic graphs used in these applications are assumed to be static pieces of information, meaning temporal evolution of concepts and relationships are not taken into account. Guided by the need for more advanced semantic graph queries involving temporal concepts, this paper surveys the existing work involving temporal representations in semantic graphs