Knowledge Management Toolkit

In the last decades, the use of ontologies in information systems has become more and more popular in various fields, such as web technologies, database integration, multi agent systems, natural language processing, etc. Artificial intelligent researchers have initially borrowed the word “ontology” from Philosophy, then the word spread in many scientific domain and ontologies are now used in several developments.

/pdf/an-introduction-to-ontologies-and-ontology-engineering-4e9aznxycs.pdf

an Introduction to Ontologies and Ontology Engineering

A new ontology based approach is proposed to model and operate chatbots (OntBot). OntBot uses appropriate mapping technique to transform ontologies and knowledge into relational database and then use that knowledge to drive its chats. The proposed approach overcomes a number of traditional chatbots drawbacks including: the need to learn and use chatbot specific language such as AIML, high botmaster interference, and the use of non-matured technology. OntBot has the additional power of easy users interactions using their natural language, and the seamless support of different application domains. This gives the proposed approach a number of unique scalability and interoperability properties that are going to be evaluated in future phases of this research project.

OntBot: Ontology based chatbot

Introduction 104
The Story as it is Told from the Business Perspective 104
The Story as it is Told from the Technology Perspective 107Data Challenges 107
Volume 107
Variety, Combining Multiple Data Sets 108
Velocity 108
Veracity, Data Quality, Data Availability 109
Data Discovery 109
Quality and Relevance 109
Data Comprehensiveness 109
Personally Identifiable Information 109
Data Dogmatism 110
Scalability 110Process Challenges 110
Management Challenges 110
Big Data Platforms Technology: Current State of the Art 111Take the Analysis to the Data! 111
What Is Apache Hadoop? 111
Who Are the Hadoop Users? 112
An Example of an Advanced User: Amazon 113
Big Data in Data Warehouse or in Hadoop? 113
Big Data in the Database World (Early 1980s Till Now) 113
Big Data in the Systems World (Late 1990s Till Now) 113
Enterprise Search 115
Big Data “Dichotomy” 115
Hadoop and the Cloud 116
Hadoop Pros 116
Hadoop Cons 116Technological Solutions for Big Data Analytics 118
Scalability and Performance at eBay 122
Unstructured Data 123
Cloud Computing and Open Source 123Every day, 2.5 quintillion bytes of data are created. These data come from 
digital pictures, videos, posts to social media sites, intelligent sensors, purchase transaction records, cell phone GPS signals, to name a few. This is 
known as Big Data.

http://www.odbms.org/wp-content/uploads/2013/07/Big-Data.Zicari.pdf

Big Data: Challenges and Opportunities

In this article we present a review of selected literature of context-aware pervasive computing while integrating theory and practice from various disciplines in order to construct a theoretical grounding and a technical follow-up path for our future research. This paper is not meant to provide an extensive review of the literature, but rather to integrate and extend fundamental and promising theoretical and technical aspects found in the literature. Our purpose is to use the constructed theory and practice in order to enable anywhere and anytime adaptive e-learning environments. We particularly elaborate on context, adaptivity, context-aware systems, ontologies and software development issues. Furthermore, we represent our view point for context-aware pervasive application development particularly based on higher abstraction where ontologies and semantic web activities, also web itself, are of crucial.

/pdf/context-and-adaptivity-in-pervasive-computing-environments-1h3avi5m0a.pdf

Context and Adaptivity in Pervasive Computing Environments: Links with Software Engineering and Ontological Engineering

Relational databases are often used as a basis for persistent storage of ontologies to facilitate rapid operations such as search and retrieval, and to utilize the benefits of relational databases management systems such as transaction management, security and integrity control. On the other hand, there appear more and more OWL files that contain ontologies. Therefore, this paper proposes to extract ontologies from OWL files and then store them in relational databases. A prerequisite for this storing is transformation of ontologies to relational databases, which is the purpose of this paper. Keywords—Ontologies, relational databases, SQL, and OWL.

Storing OWL Ontologies in SQL Relational Databases

eGovernment in Estonia got started by developing a functional architecture that includes secure data transport backbone X-Road, distributed software systems and different hardware components like portals, elements of public key infrastructure (PKI), governmental databases and information systems. This is the very basis of hundreds of services that have been created today. The recent success with eGoverment services and the common architecture of eGovernment will be described hereunder.

http://ieeexplore.ieee.org/iel5/10096/32329/01509730.pdf

eGovernment in Estonia: best practices

When a large amount of data is stored in OWL files, it is not efficient to maintain and query those data The OWL syntax is based on XML, which is a meta-markup language Thus, it is suitable for data description and data exchange, rather than for data storage and data management Furthermore, enabling multiple users to work with the same ontology in parallel and make modifications mandates the use of databases (eg object-relational) for storing ontologies Therefore, this paper proposes to extract data from OWL files and store those data in object-relational databases Object-relational databases are good for data storage and data management But OWL is semantically richer than SQL3 As a result, some of the semantics captured in ontologies can be lost when ontologies are stored in object-relational databases In addition, some of the structures of ontologies can be lost Eg a class in an ontology can be stored as it were an atomic undifferentiated piece of data in a single CLOB column in an object-relational database When the ontology is stored in such a way, the query and scalability features of the object-relational database cannot be fully exploited to access the data

http://www.wseas.us/e-library/conferences/2008/rhodes/aic/aic15.pdf

Storing OWL ontologies in SQL3 object-relational databases

The Internet consists of web sites that employ different kinds of structures as the backbone of their build-up. However, users are browsing the web according to its content, regardless of the structure. In this paper we discuss the possibilities of applying ontologies in exploring the web sites' structures and usage for producing viewing recommendations for the visitors. A special log system for capturing access data is introduced as well as techniques applied for data mining. Ontology of user profiles is constructed by exploiting the user locality model.

/pdf/applying-user-profile-ontology-for-mining-web-site-16zwrym4wl.pdf

Applying User Profile Ontology for Mining Web Site Adaptation Recommendations.

The development of the Internet has made it possible to move studies to e-environments, where time and distance are not obstacles any more. The paper presents an overview of an e-learning system and discusses some Web-based tools for teaching the basics of digital logic in such a system.

Ahto Kalja

Papers

Storing OWL Ontologies in SQL Relational Databases

eGovernment in Estonia: best practices

Storing OWL ontologies in SQL3 object-relational databases

Applying User Profile Ontology for Mining Web Site Adaptation Recommendations.

Applying e-Environments in Teaching the Basics of Digital Logic