Showing papers on "Semantic Web Stack published in 2019"

Knowledge Graphs: New Directions for Knowledge Representation on the Semantic Web (Dagstuhl Seminar 18371)

Abstract: The increasingly pervasive nature of the Web, expanding to devices and things in everyday life, along with new trends in Artificial Intelligence call for new paradigms and a new look on Knowledge Representation and Processing at scale for the Semantic Web. The emerging, but still to be concretely shaped concept of "Knowledge Graphs" provides an excellent unifying metaphor for this current status of Semantic Web research. More than two decades of Semantic Web research provides a solid basis and a promising technology and standards stack to interlink data, ontologies and knowledge on the Web. However, neither are applications for Knowledge Graphs as such limited to Linked Open Data, nor are instantiations of Knowledge Graphs in enterprises – while often inspired by – limited to the core Semantic Web stack. This report documents the program and the outcomes of Dagstuhl Seminar 18371 "Knowledge Graphs: New Directions for Knowledge Representation on the Semantic Web", where a group of experts from academia and industry discussed fundamental questions around these topics for a week in early September 2018, including the following: what are knowledge graphs? Which applications do we see to emerge? Which open research questions still need be addressed and which technology gaps still need to be closed?

Building the Semantic Web of Things Through a Dynamic Ontology

Abstract: The Web of Things (WoT) has recently appeared as the latest evolution of the Internet of Things and, as the name suggests, requires that devices interoperate through the Internet using Web protocols and standards. Currently, only a few theoretical approaches have been presented by researchers and industry, to fight the fragmentation of the IoT world through the adoption of semantics. This further evolution is known as Semantic WoT and relies on a WoT implementation crafted on the technologies proposed by the Semantic Web stack. This article presents a working implementation of the WoT declined in its Semantic flavor through the adoption of a shared ontology for describing devices. In addition to that, the ontology includes patterns for dynamic interactions between devices, and therefore we define it as dynamic ontology. A practical example will give a proof of concept and overall evaluation, showing how the dynamic setup proposed can foster interoperability at information level allowing on the one hand smart discovery, enabling on the other hand orchestration and automatic interaction through the semantic information available.

Optimizing Semantic Annotations for Web Service Invocation

Abstract: Semantic annotations play an important role in semantics-aware service discovery, recommendation and composition. While existing approaches and tools focus on facilitating the development of semantic annotations on web services, the validation of the quality of annotations is largely overlooked. Meanwhile, the refinement of semantic annotations mostly goes through manual processes, which not only is time-consuming but also requires significant domain knowledge. To enhance the Quality of Semantic Annotation (QoSA), we have developed a technique to incrementally assess and correct semantic annotations of web services. Aiming at supporting web service interoperation, we have formalized the QoSA of input and output parameters. Based on such formalism, test cases are automatically generated to validate service annotations. Learned semantic instances are then accumulated to iteratively validate semantic annotations of other services. Furthermore, a three-phase optimization methodology including local-feedback, global-feedback, and global-propagate is developed to improve the QoSA by incrementally correcting inaccurate annotations. Experiments over a real-world web services repository have demonstrated that our technique can effectively improve QoSA of services, gaining a 78.68 percent improvement in input parameters annotations and identifying 36.47 percent inaccurate output parameters annotations. The proposed technique can be equipped at various service repositories to enhance service discovery and recommendation.

Knowledge Graphs for an Automated Information Provision in the Factory Planning

Abstract: Today's factory planning is characterized by a multitude of different IT systems. This results in data silos that lead to problems with the provision of information within factory planning. This paper addresses this problem by integrating data from different data silos into a knowledge graph based on the Semantic Web Stack. To improve the information provision a function is developed, which automatically identifies data changes and makes the data available in line with the demands of the planners on the basis of a role-based concept. The developed concept is implemented and validated in the factory planning of a german automobile manufacturer.

A Web-App for Analysis of Honey Bee Hive Data

Abstract: The significant drop in honey bee population in recent years has been attributed to Colony Collapse Disorder (CCD). There have been many efforts to monitor honey bee hives and learn about their health and behavior. As part of early efforts for monitoring hives, our research group developed the Beemon system which obtains audio and video recordings as well as humidity and temperature data at beehives for further analysis. In order to make the analysis more efficient and widely accessible, we recently developed a web application to allow easier access to all the Beemon data and provide several tools for analysis. This paper provides details on the design and development of the web application used to make Beemon data available via web-based visualization tools for more effective analysis. Our web application was built using the MEAN (MongoDB, Express, Angular, Node.js) web stack which allowed JavaScript to be used on both the server- and client-sides. This system was built modularly, so the data visualization system was added on as a new server- and client-side module. The system queries data quickly from MongoDB, aggregate views and sends the large blocks of serializable data to the client using Socket.io. The client can interact with our server’s data-providing endpoints and flexibly use any client-side data visualization framework. Our system employs C3.js, a D3-based data visualization platform, which includes base charts, to render visualizations of transmitted data.

Clustering Pipelines of Large RDF POI Data

Abstract: Among the various domains using large RDF graphs, applications often rely on geographical information which is often represented via Points Of Interests. In particular, one challenge is to extract patterns from POI sets to discover Areas Of Interest (AOIs). To tackle this challenge, a typical method is to aggregate various points according to specific distances (e.g. geographical) via clustering algorithms. In this study, we present a flexible architecture to design pipelines able to aggregate POIs from contextual to geographical dimensions in a single run. This solution allows any kind of clustering algorithm combinations to compute AOIs and is built on top of a Semantic Web stack which allows multiple-source querying and filtering through SPARQL.

Node-indri : Moving the indri toolkit to the modern web stack

Abstract: We introduce node-indri, a Node.js module that acts as a wrapper around the Indri toolkit, and thus makes an established IR toolkit accessible to the modern web stack. node-indri exposes many of Indri’s functionalities and provides direct access to document content and retrieval scores for web development (in contrast to, for instance, the Pyndri wrapper). This setup reduces the amount of glue code that has to be developed and maintained when researching search interfaces, which today tend to be developed with specific JavaScript libraries such as React.js, Angular.js or Vue.js. The node-indri repository is open-sourced at https://github.com/felipemoraes/node-indri.