Semantic Annotation of Objects

2011 ◽  
pp. 223-238
Author(s):  
Petr Kremen ◽  
Miroslav Blaško ◽  
Zdenek Kouba
Keyword(s):  
Knowledge Management ◽  
Semantic Annotation ◽  
Description Logics ◽  
Practical Experience ◽  
Video Clips ◽  
Semantic Annotations ◽  
Multimedia Resources ◽  
Audio Video ◽  
Representation Techniques ◽  
Reasoning Algorithm

Compared to traditional ways of annotating multimedia resources (textual documents, photographs, audio/video clips etc.) by keywords in form of text fragments, semantic annotations are based on tagging such multimedia resources with meaning of objects (like cultural/historical artifacts) the resource is dealing with. The search for multimedia resources stored in a repository enriched with semantic annotations makes use of an appropriate reasoning algorithm. Knowledge management and Semantic Web communities have developed a number of relevant formalisms and methods. This chapter is motivated by practical experience with authoring of semantic annotations of cultural heritage related resources/objects. Keeping this experience in mind, the chapter compares various knowledge representation techniques, like frame-based formalisms, RDF(S), and description logics based formalisms from the viewpoint of their appropriateness for resource annotations and their ability to automatically support the semantic annotation process through advanced inference services, like error explanations and expressive construct modeling, namely n-ary relations.

scholarly journals IoTSAS: An Integrated System for Real-Time Semantic Annotation and Interpretation of IoT Sensor Stream Data

Computers ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 127
Author(s):  
Besmir Sejdiu ◽  
Florije Ismaili ◽  
Lule Ahmedi
Keyword(s):  
Air Quality ◽  
Real Time ◽  
Contextual Information ◽  
Semantic Annotation ◽  
Weather Conditions ◽  
Water Quality Monitoring ◽  
Integrated System ◽  
Quality Monitoring ◽  
Stream Data ◽  
Semantic Annotations

Sensors and other Internet of Things (IoT) technologies are increasingly finding application in various fields, such as air quality monitoring, weather alerts monitoring, water quality monitoring, healthcare monitoring, etc. IoT sensors continuously generate large volumes of observed stream data; therefore, processing requires a special approach. Extracting the contextual information essential for situational knowledge from sensor stream data is very difficult, especially when processing and interpretation of these data are required in real time. This paper focuses on processing and interpreting sensor stream data in real time by integrating different semantic annotations. In this context, a system named IoT Semantic Annotations System (IoTSAS) is developed. Furthermore, the performance of the IoTSAS System is presented by testing air quality and weather alerts monitoring IoT domains by extending the Open Geospatial Consortium (OGC) standards and the Sensor Observations Service (SOS) standards, respectively. The developed system provides information in real time to citizens about the health implications from air pollution and weather conditions, e.g., blizzard, flurry, etc.

The Complex Issue of Measuring KM Performance

2011 ◽  
pp. 208-230 ◽  
Author(s):  
Enrico Scarso ◽  
Ettore Bolisani ◽  
Antonella Padova
Keyword(s):  
Knowledge Management ◽  
Performance Evaluation ◽  
Performance Measurement ◽  
Practical Experience ◽  
Complex Issue ◽  
Daily Experience ◽  
Business Context ◽  
Evaluation Approaches ◽  
The Impact ◽  
Puzzling Problem

Most companies that are deeply investing in Knowledge Management (KM) initiatives encounter substantial difficulties in assessing the effectiveness of these programmes. Actually, measuring the impact of KM projects is still a puzzling problem both at the conceptual and operative level. However, measuring their performance is necessary for monitoring their progress and for successfully managing and allocating resources, as well as to maintain the support and commitment by the top management. Although several KM performance evaluation approaches have been proposed in literature, they are still far from becoming an established practice. The chapter aims at discussing this issue by placing it in a business context. First, the literature on KM performance evaluation is briefly reviewed, and the main methods currently used are classified. Then, the practical experience of a multinational company is discussed, with the purpose to describe the problems that practitioners face in their daily experience, and provide insights into the possible improvements of KM performance measurement.

scholarly journals Creating explorable extended reality environments with semantic annotations

2020 ◽  
Author(s):  
Jakub Flotyński
Keyword(s):  
Domain Knowledge ◽  
Description Logics ◽  
Main Element ◽  
Arbitrary Domain ◽  
Aspect Oriented Programming ◽  
Domain Experts ◽  
Semantic Annotations ◽  
Knowledge Based ◽  
And Control ◽  
3D Content

Abstract The main element of extended reality (XR) environments is behavior-rich 3D content consisting of objects that act and interact with one another as well as with users. Such actions and interactions constitute the evolution of the content over time. Multiple application domains of XR, e.g., education, training, marketing, merchandising, and design, could benefit from the analysis of 3D content changes based on general or domain knowledge comprehensible to average users or domain experts. Such analysis can be intended, in particular, to monitor, comprehend, examine, and control XR environments as well as users’ skills, experience, interests and preferences, and XR objects’ features. However, it is difficult to achieve as long as XR environments are developed with methods and tools that focus on programming and 3D modeling rather than expressing domain knowledge accompanying content users and objects, and their behavior. The main contribution of this paper is an approach to creating explorable knowledge-based XR environments with semantic annotations. The approach combines description logics with aspect-oriented programming, which enables knowledge representation in an arbitrary domain as well as transformation of available environments with minimal users’ effort. We have implemented the approach using well-established development tools and exemplify it with an explorable immersive car showroom. The approach enables efficient creation of explorable XR environments and knowledge acquisition from XR.

Approaches to Semantics in Knowledge Management

2009 ◽  
pp. 257-281
Author(s):  
Cristiano Fugazza ◽  
Stefano David ◽  
Anna Montesanto ◽  
Cesare Rocchi
Keyword(s):  
Neural Networks ◽  
Knowledge Management ◽  
Artificial Neural Networks ◽  
Database Management ◽  
Description Logics ◽  
Open World ◽  
Open World Assumption ◽  
Relational Database Management ◽  
Static Nature ◽  
Relational Database Management Systems

There are different approaches to modeling a computational system, each providing different semantics. We present a comparison among different approaches to semantics and we aim at identifying which peculiarities are needed to provide a system with uniquely interpretable semantics. We discuss different approaches, namely, Description Logics, Artificial Neural Networks, and relational database management systems. We identify classification (the process of building a taxonomy) as common trait. However, in this chapter we also argue that classification is not enough to provide a system with a Semantics, which emerges only when relations among classes are established and used among instances. Our contribution also analyses additional features of the formalisms that distinguish the approaches: closed versus. open world assumption, dynamic versus. static nature of knowledge, the management of knowledge, and the learning process.

scholarly journals Exploiting Semantic Annotations andQ-Learning for Constructing an Efficient Hierarchy/Graph Texts Organization

2015 ◽  
Vol 2015 ◽  
pp. 1-19 ◽  
Author(s):  
Asmaa M. El-Said ◽  
Ali I. Eldesoky ◽  
Hesham A. Arafat
Keyword(s):  
Text Mining ◽  
Key Words ◽  
Experimental Evaluation ◽  
Semantic Annotation ◽  
Text Documents ◽  
Frequency Distributions ◽  
Semantic Annotations ◽  
Representation Scheme ◽  
Effective Development

Tremendous growth in the number of textual documents has produced daily requirements for effective development to explore, analyze, and discover knowledge from these textual documents. Conventional text mining and managing systems mainly use the presence or absence of key words to discover and analyze useful information from textual documents. However, simple word counts and frequency distributions of term appearances do not capture the meaning behind the words, which results in limiting the ability to mine the texts. This paper proposes an efficient methodology for constructing hierarchy/graph-based texts organization and representation scheme based on semantic annotation andQ-learning. This methodology is based on semantic notions to represent the text in documents, to infer unknown dependencies and relationships among concepts in a text, to measure the relatedness between text documents, and to apply mining processes using the representation and the relatedness measure. The representation scheme reflects the existing relationships among concepts and facilitates accurate relatedness measurements that result in a better mining performance. An extensive experimental evaluation is conducted on real datasets from various domains, indicating the importance of the proposed approach.

scholarly journals SEMANTIC ANNOTATIONS ON HERITAGE MODELS: 2D/3D APPROACHES AND FUTURE RESEARCH CHALLENGES

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 829-836
Author(s):  
V. Croce ◽  
G. Caroti ◽  
L. De Luca ◽  
A. Piemonte ◽  
P. Véron
Keyword(s):  
Cultural Heritage ◽  
Contextual Information ◽  
Semantic Annotation ◽  
External Information ◽  
Future Research ◽  
Digital Information ◽  
Semantic Annotations ◽  
Related Information ◽  
Research Challenges ◽  
Application Fields

Abstract. Research in the field of Cultural Heritage is increasingly moving towards the creation of digital information systems, in which the geometric representation of an artifact is linked to some external information, through meaningful tags. The process of attributing additional and structured information to various elements in a given digital model is customarily identified with the term semantic annotation; the added contextual information is associated, for instance, to analysis and conservation terms. Starting from the existing literature, aim of this work is to discuss how semantic annotations are used, in digital architectural heritage models, to link the geometrical representation of an artefact with knowledge-related information. Most consolidated methods -such as traditional mapping on 2D media, are compared with more recent approaches making the most of 3D representation. Reference is made, in particular, to Heritage-BIM techniques and to collaborative reality-based platforms, such as Aïoli (http://aioli.cloud). Potentialities and limits of the different solutions proposed in literature are critically discussed, also addressing future research challenges in Cultural Heritage application fields.

scholarly journals What's in this Collection Dataset? Semantic Annotation with GATE

2019 ◽  
Vol 3 ◽  
Author(s):  
Felicitas Löffler ◽  
Birgitta König-Ries
Keyword(s):  
Machine Learning ◽  
Information Extraction ◽  
Semantic Annotation ◽  
Biological Processes ◽  
Learning Approaches ◽  
Precise Information ◽  
Semantic Annotations ◽  
Semantic Techniques ◽  
Semantic Resources ◽  
Tools And Techniques

Semantic annotations of datasets are very useful to support quality assurance, discovery, interpretability, linking and integration of datasets. However, providing such annotations manually is often a time-consuming task . If the process is to be at least partially automated and still provide good semantic annotations, precise information extraction is needed. The recognition of entity names (e.g., person, organization, location) from textual resources is the first step before linking the identified term or phrase to other semantic resources such as concepts in ontologies. A multitude of tools and techniques have been developed for information extraction. One of the big players is the text mining framework GATE (Cunningham et al. 2013) that supports annotation rules, semantic techniques and machine learning approaches. We will run GATE's default ANNIE pipeline on collection datasets to automatically detect persons, locations and time. We will also present extensions to extract organisms (Naderi et al. 2011), environmental terms, data parameters and biological processes and how to link them to ontologies and LOD resources, e.g., DBPedia (Sateli and Witte 2015). We would like to discuss the results with the conference participants and welcome comments and feedbacks on the current solution. The audience is also welcome to provide their own datasets in preparation for this session.

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