Semantic Web

About: Semantic Web is a research topic. Over the lifetime, 26987 publications have been published within this topic receiving 534275 citations. The topic is also known as: Sem Web & SemWeb.

Working with multiple ontologies on the semantic web

Abstract: The standardization of the second generation Web Ontology Language, OWL, leaves a crucial issue for Web-based ontologies unsatisfactorily resolved: how to represent and reason with multiple distinct, but linked, ontologies. OWL provides the owl:imports construct which, roughly, allows Web ontologies to include other Web ontologies, but only by merging all the linked ontologies into a single logical "space". Recent work on multidimensional logics, fusions and other combinations of modal logics, distributed and contextual logics, and the like have tried to find formalisms wherein knowledge bases (and their logic) are kept more distinct but yet affect each other. These formalisms have various degrees of robustness in their computational complexity, their modularity, their expressivity, and their intuitiveness to modelers. In this paper, we explore a family of such formalisms, grounded in E-connections as extensions to OWL, with emphasis on a novel sub-formalism that seems very straightforward to implement on existing tableau OWL reasoners, as witnessed by our implementation of this formalism in the OWL reasoner Pellet. We discuss how to integrate those formalisms into OWL, as well as some of the issues that modelers have to face when using such formalisms in the context of a large number of heterogeneous, independently developed, richly interconnected ontologies that we expect to be the norm on the Semantic Web.

Neuro-symbolic representation learning on biological knowledge graphs.

Abstract: Motivation Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. Results: We develop a novel method for feature learning on biological knowledge graphs. Our method combines symbolic methods, in particular knowledge representation using symbolic logic and automated reasoning, with neural networks to generate embeddings of nodes that encode for related information within knowledge graphs. Through the use of symbolic logic, these embeddings contain both explicit and implicit information. We apply these embeddings to the prediction of edges in the knowledge graph representing problems of function prediction, finding candidate genes of diseases, protein-protein interactions, or drug target relations, and demonstrate performance that matches and sometimes outperforms traditional approaches based on manually crafted features. Our method can be applied to any biological knowledge graph, and will thereby open up the increasing amount of Semantic Web based knowledge bases in biology to use in machine learning and data analytics. Availability and implementation https://github.com/bio-ontology-research-group/walking-rdf-and-owl. Contact robert.hoehndorf@kaust.edu.sa. Supplementary information Supplementary data are available at Bioinformatics online.

The semantic Web and its languages

Abstract: Currently computers are changing from single isolated devices to entry points in a worldwide network of information exchange and business transactions called the World WideWeb (WWW). Therefore support in data, information, and knowledge exchange becomesthe key issue in current computer technology. The WWW has drastically changed theavailability of electronically available information. However, this success andexponential grow makes it increasingly difficult to find, to access, to present, and tomaintain the information of use to a wide variety of users. In reaction to this bottleneckmany new research initiatives and commercial enterprises have been set up to enrichavailable information with machineprocessable semantics. Such support is essential for “bringing the web to its full potential” in areas such as knowledge management andelectronic commerce. This

Searching for services on the semantic web using process ontologies

Abstract: The ability to rapidly locate useful on-line services (e.g. software applications, software components, process models, or service organizations), as opposed to simply useful documents, is becoming increasingly critical in many domains. As the sheer number of such services increases it will become increasingly more important to provide tools that allow people (and software) to quickly find the services they need, while minimizing the burden for those who wish to list their services with these search engines. This can be viewed as a critical enabler of the 'friction-free' markets of the 'new economy'. Current service retrieval technology is, however, seriously deficient in this regard. The information retrieval community has focused on the retrieval of documents, not services per se, and has as a result emphasized keyword-based approaches. Those approaches achieve fairly high recall but low precision. The software agents and distributed computing communities have developed simple 'frame-based' approaches for 'matchmaking' between tasks and on-line services increasing precision at the substantial cost of requiring all services to be modeled as frames and only supporting perfect matches. This paper proposes a novel, ontology-based approach that employs the characteristics of a process-taxonomy to increase recall without sacrificing precision and computational complexity of the service retrieval process.