Considering that the largest part of end-use energy consumption worldwide is associated with the buildings sector, there is an inherent need for the conceptualization, specification, implementation, ...and instantiation of novel solutions in smart buildings, able to achieve significant reductions in energy consumption through the adoption of energy efficient techniques and the active engagement of the occupants. Towards the design of such solutions, the identification of the main energy consuming factors, trends, and patterns, along with the appropriate modeling and understanding of the occupants' behavior and the potential for the adoption of environmentally-friendly lifestyle changes have to be realized. In the current article, an innovative energy-aware information technology (IT) ecosystem is presented, aiming to support the design and development of novel personalized energy management and awareness services that can lead to occupants' behavioral change towards actions that can have a positive impact on energy efficiency. Novel information and communication technologies (ICT) are exploited towards this direction, related mainly to the evolution of the Internet of Things (IoT), data modeling, management and fusion, big data analytics, and personalized recommendation mechanisms. The combination of such technologies has resulted in an open and extensible architectural approach able to exploit in a homogeneous, efficient and scalable way the vast amount of energy, environmental, and behavioral data collected in energy efficiency campaigns and lead to the design of energy management and awareness services targeted to the occupants' lifestyles. The overall layered architectural approach is detailed, including design and instantiation aspects based on the selection of set of available technologies and tools. Initial results from the usage of the proposed energy aware IT ecosystem in a pilot site at the University of Murcia are presented along with a set of identified open issues for future research.
The schema.org initiative led by the four major search engines curates a vocabulary for describing web content. The number of semantic annotations on the web are increasing, mostly due to the ...industrial incentives provided by those search engines. The annotations are not only consumed by search engines, but also by other automated agents like intelligent personal assistants (IPAs). However, only annotating data is not enough for automated agents to reach their full potential. Web APIs should also be annotated for automating service consumption, so the IPAs can complete tasks like booking a hotel room or buying a ticket for an event on the fly. Although there has been a vast amount of effort in the semantic web services field, the approaches did not gain too much adoption outside of academia, mainly due to lack of concrete incentives and steep learning curves. In this paper, we suggest a lightweight, bottom-up approach based on schema.org actions to annotate Web APIs. We analyse schema.org vocabulary in the scope of lightweight semantic web services literature and propose extensions where necessary. We demonstrate our work by annotating existing Web APIs of accommodation service providers. Additionally, we briefly demonstrate how these APIs can be used dynamically, for example, by a dialogue system.
For over 50 years researchers and practitioners have searched for ways to elicit and formalize expert knowledge to support AI applications. Expert systems and knowledge bases were all results of ...these efforts. The initial efforts on knowledge bases were focused on defining a domain and task intensionally with rather complex ontologies. The increasing complexity of knowledge and knowledge-based systems eventually led to the development of knowledge engineering methodologies. Knowledge graphs, in contrast to the traditional knowledge bases, represent knowledge more extensionally with a very large set of explicit statements and rather simpler and smaller ontologies. This paradigm change calls for a new take on knowledge engineering that focuses on the curation of ABox statements. In this paper, we introduce various aspects of the knowledge graphs lifecycle namely creation, hosting, curation and deployment. We define each task, give example approaches from the literature and explain our approach with a running example. Additionally, we present the German Tourism Knowledge Graph that is being implemented with our methodology.
Duplicate Detection as a Service Opdenplatz, Juliette; Umutcan \c{S}imşek; Fensel, Dieter
arXiv (Cornell University),
07/2022
Paper, Journal Article
Odprti dostop
Completeness of a knowledge graph is an important quality dimension and factor on how well an application that makes use of it performs. Completeness can be improved by performing knowledge ...enrichment. Duplicate detection aims to find identity links between the instances of knowledge graphs and is a fundamental subtask of knowledge enrichment. Current solutions to the problem require expert knowledge of the tool and the knowledge graph they are applied to. Users might not have this expert knowledge. We present our service-based approach to the duplicate detection task that provides an easy-to-use no-code solution that is still competitive with the state-of-the-art and has recently been adopted in an industrial context. The evaluation will be based on several frequently used test scenarios.
The creation of Linked Data from raw data sources is, in theory, no rocket science (pun intended). Depending on the nature of the input and the mapping technology in use, it can become a quite ...tedious task. For our work on mapping real-life touristic data to the schema.org vocabulary we used RML but soon encountered, that the existing Java mapper implementations reached their limits and were not sufficient for our use cases. In this paper we describe a new implementation of an RML mapper. Written with the JavaScript based NodeJS framework it performs quite well for our uses cases where we work with large XML and JSON files. The performance testing and the execution of the RML test cases have shown, that the implementation has great potential to perform heavy mapping tasks in reasonable time, but comes with some limitations regarding JOINs, Named Graphs and inputs other than XML and JSON - which is fine at the moment, due to the nature of the given use cases.
Goal-oriented dialogue systems typically communicate with a backend (e.g. database, Web API) to complete certain tasks to reach a goal. The intents that a dialogue system can recognize are mostly ...included to the system by the developer statically. For an open dialogue system that can work on more than a small set of well curated data and APIs, this manual intent creation will not scalable. In this paper, we introduce a straightforward methodology for intent creation based on semantic annotation of data and services on the web. With this method, the Natural Language Understanding (NLU) module of a goal-oriented dialogue system can adapt to newly introduced APIs without requiring heavy developer involvement. We were able to extract intents and necessary slots to be filled from schema.org annotations. We were also able to create a set of initial training sentences for classifying user utterances into the generated intents. We demonstrate our approach on the NLU module of a state-of-the art dialogue system development framework.
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