City Data Fusion: Sensor Data Fusion in the Internet of Things

Internet of Things (IoT) has gained substantial attention recently and play a significant role in smart city application deployments. A number of such smart city applications depend on sensor fusion capabilities in the cloud from diverse data sources. We introduce the concept of IoT and present in detail ten different parameters that govern our sensor data fusion evaluation framework. We then evaluate the current state-of-the art in sensor data fusion against our sensor data fusion framework. Our main goal is to examine and survey different sensor data fusion research efforts based on our evaluation framework. The major open research issues related to sensor data fusion are also presented.Comment: Accepted to be published in International Journal of Distributed Systems and Technologies (IJDST), 201

Extended enterprise architecture with the FADEE.

Business-to-Business integration (B2Bi) is considered to be not merely an IT-issue, but also a business problem. This paper draws attention to the challenges companies within an Extended Enterprise are confronted with when integrating their systems. We primarily pay attention to coordination problems that may arise. To overcome these problems we propose the use of Enterprise Architecture descriptions. We discuss the powers of using Enterprise Architecture descriptions in integration exercises. It will become clear that doing Enterprise Architecture is no longer an option; it is mandatory. Furthermore, we present the FADEE, the Framework for the Architectural Description of the Extended Enterprise. This framework gives an overview of how companies can apply the Zachman framework to do Enterprise Architecture in the realm of the Extended Enterprise.Architecture; Business-to-Business integration; Companies; Coordination; Enterprise architecture; Extended enterprise; Extended enterprise architecture framework; FADEE; Framework; Integration; Problems; Research; Systems;

A Semantics-Based Measure of Emoji Similarity

Emoji have grown to become one of the most important forms of communication on the web. With its widespread use, measuring the similarity of emoji has become an important problem for contemporary text processing since it lies at the heart of sentiment analysis, search, and interface design tasks. This paper presents a comprehensive analysis of the semantic similarity of emoji through embedding models that are learned over machine-readable emoji meanings in the EmojiNet knowledge base. Using emoji descriptions, emoji sense labels and emoji sense definitions, and with different training corpora obtained from Twitter and Google News, we develop and test multiple embedding models to measure emoji similarity. To evaluate our work, we create a new dataset called EmoSim508, which assigns human-annotated semantic similarity scores to a set of 508 carefully selected emoji pairs. After validation with EmoSim508, we present a real-world use-case of our emoji embedding models using a sentiment analysis task and show that our models outperform the previous best-performing emoji embedding model on this task. The EmoSim508 dataset and our emoji embedding models are publicly released with this paper and can be downloaded from http://emojinet.knoesis.org/.Comment: This paper is accepted at Web Intelligence 2017 as a full paper, In 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). Leipzig, Germany: ACM, 201

Reasoning & Querying – State of the Art

Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF

Ontology selection: ontology evaluation on the real Semantic Web

The increasing number of ontologies on the Web and the appearance of large scale ontology repositories has brought the topic of ontology selection in the focus of the semantic web research agenda. Our view is that ontology evaluation is core to ontology selection and that, because ontology selection is performed in an open Web environment, it brings new challenges to ontology evaluation. Unfortunately, current research regards ontology selection and evaluation as two separate topics. Our goal in this paper is to explore how these two tasks relate. In particular, we are interested to get a better understanding of the ontology selection task and filter out the challenges that it brings to ontology evaluation. We discuss requirements posed by the open Web environment on ontology selection, we overview existing work on selection and point out future directions. Our major conclusion is that, even if selection methods still need further development, they have already brought novel approaches to ontology evaluatio

From software APIs to web service ontologies: a semi-automatic extraction method

Successful employment of semantic web services depends on the availability of high quality ontologies to describe the domains of these services. As always, building such ontologies is difficult and costly, thus hampering web service deployment. Our hypothesis is that since the functionality offered by a web service is reflected by the underlying software, domain ontologies could be built by analyzing the documentation of that software. We verify this hypothesis in the domain of RDF ontology storage tools.We implemented and fine-tuned a semi-automatic method to extract domain ontologies from software documentation. The quality of the extracted ontologies was verified against a high quality hand-built ontology of the same domain. Despite the low linguistic quality of the corpus, our method allows extracting a considerable amount of information for a domain ontology

Massive ontology interface

This paper describes the Massive Ontology Interface (MOI), a web portal which facilitates interaction with a large ontology (over 200,000 concepts and 1.6M assertions) that is built automatically using OpenCyc as a backbone. The aim of the interface is to simplify interaction with the massive amounts of information and guide the user towards understanding the ontology’s data. Using either a text or graph-based representation, users can discuss and edit the ontology. Social elements utilizing gamification techniques are included to encourage users to create and collaborate on stored knowledge as part of a web community. An evaluation by 30 users comparing MOI with OpenCyc’s original interface showed significant improvements in user understanding of the ontology, although full testing of the interface’s social elements lies in the future