An intelligent robot agent based on domain ontology, machine learning mechanism, and Fuzzy Markup Language (FML) for students and robot co-learning is presented in this paper. The machine-human co-learning model is established to help various students learn the mathematical concepts based on their learning ability and performance. Meanwhile, the robot acts as a teacher's assistant to co-learn with children in the class. The FML-based knowledge base and rule base are embedded in the robot so that the teachers can get feedback from the robot on whether students make progress or not. Next, we inferred students' learning performance based on learning content's difficulty and students' ability, concentration level, as well as teamwork sprit in the class. Experimental results show that learning with the robot is helpful for disadvantaged and below-basic children. Moreover, the accuracy of the intelligent FML-based agent for student learning is increased after machine learning mechanism.

There are endless discussions on the databases arena about which DBMS is best suited for operational or data warehousing analytics, which one is the most efficient for online transaction processing, or which one is suitable for semantic integration. Recently graph databases are growing in popularity, especially in the enterprise space, and perhaps that adds more headache on those vendors that try to differentiate from competition and on those clients that are completely uncertain how to embrace this database technology. Recently Bloor published a report about Graph and RDF Databases. The author, Philip Howard, claims that "the difference between a true graph product and a triple store is that the former supports index free adjacency (which means you can traverse a graph without needing an index) and the latter doesn't". On the contrary Weinberger, CEO of ArrangoDB, argues that this is not a fundamental criterion on what is a graph database.

Personal use of this material is permitted. Abstract --The introduction of automated vehicles without permanent human supervision demands a functional system description, including functional system boundaries and a comprehensive safety analysis. These inputs to the technical development can be identified and analyzed by a scenario-based approach. Furthermore, to establish an economical test and release process, a large number of scenarios must be identified to obtain meaningful test results. Experts are doing well to identify scenarios that are difficult to handle or unlikely to happen. However, experts are unlikely to identify all scenarios possible based on the knowledge they have on hand. Expert knowledge modeled for computer aided processing may help for the purpose of providing a wide range of scenarios. This contribution reviews ontologies as knowledge-based systems in the field of automated vehicles, and proposes a generation of traffic scenes in natural language as a basis for a scenario creation. Safety assessment of automated driving functions is an emerging topic in the automotive industry. Several research and development projects show prototypes of automated vehicles in well-defined showcases. When it comes to series production, the ISO 26262 standard defines a state-of-the-art development process to ensure functional safety. Automated vehicles will have to fulfill a safe driving task in a high number of operating scenarios. To comply with the hazard analysis and risk assessment demanded by the ISO 26262 standard, hazardous events "shall be determined systematically by using adequate techniques" [1, Part 3].

A semantics-preserving exchange of information between two software applications requires mappings between logically equivalent concepts in the ontology of each application. The challenge of semantic integration is therefore equivalent to the problem of generating such mappings, determining that they are correct, and providing a vehicle for executing the mappings, thus translating terms from one ontology into another. This article presents an approach toward this goal using techniques that exploit the model-theoretic structures underlying ontologies. With these as inputs, semiautomated and automated components may be used to create mappings between ontologies and perform translations. A major barrier to such interoperability is semantic heterogeneity: different applications, databases, and agents may ascribe disparate meanings to the same terms or use distinct terms to convey the same meaning.

Building Large Knowledge-Based Systems (Addison-Wesley, Reading, Massachusetts, 1990, 372 pages, $39.75, ISBN O-201-51752-3) by Douglas B. Lenat and R. V. Guha is an interim report on the Microelectronic and Computer Technology Corporation (MCC) Cyc project. Cyc is an ambitious lo-year effort whose goal is to overcome the brittleness of contemporary expert systems by capturing the millions of facts and heuristics that MCC researchers consider to be the consensus reality that all intelligent beings share and that leads to common sense. As the authors state in their preface, "There are deep, important issues that must be addressed if we are ever to have a large intelligent knowledge-based program: What ontological categories would make up an adequate set for carving up the universe? What are the important things most human beings today know about solid objects? This book does an admirable job of presenting their research.

Building Large Knowledge-Based Systems (Addison-Wesley, Reading, Massachusetts, 1990, 372 pages, $39.75, ISBN O-201-51752-3) by Douglas B. Lenat and R. V. Guha is an interim report on the Microelectronic and Computer Technology Corporation (MCC) Cyc project. Cyc is an ambitious lo-year effort whose goal is to overcome the brittleness of contemporary expert systems by capturing the millions of facts and heuristics that MCC researchers consider to be the consensus reality that all intelligent beings share and that leads to common sense. As the authors state in their preface, "There are deep, important issues that must be addressed if we are ever to have a large intelligent knowledge-based program: What ontological categories would make up an adequate set for carving up the universe? What are the important things most human beings today know about solid objects? This book does an admirable job of presenting their research.

If you are interested in writing a review, contact chandra@cis. It is intended to be a "general textbook of knowledge-base analysis and design" (p. Its great strength is recognizing the need for an interdisciplinary approach, and the attempt at presenting the logical and philosophical foundations of knowledge representation under a unified view. Its great weakness is a lack of consistent rigor, which is needed in a textbook for newcomers to a subject. After some historical remarks and a first introductory chapter devoted to logic, Sowa immediately attacks the hard problems involved in choosing ontological categories, which lie at the heart of any knowledge representation project.

However, interoperability among these manufacturing applications is hindered because the applications use different terminology and representations of the domain. These problems arise most acutely for systems that must manage the heterogeneity inherent in various domains and integrate models of different domains into coherent frameworks (figure 1). For example, such integration occurs in businessprocess reengineering, where enterprise models integrate processes, organizations, goals, and customers. Even when applications use the same terminology, they often associate different semantics with the terms. This clash over the meaning of the terms prevents the seamless exchange of information among the applications.

It is the science of what is, the kinds and structures of objects, properties, events, processes, and relations in every area of reality. Ontology is, put simply, about existence. Like so many things, the term was borrowed by computer science and is rapidly becoming a buzzword in industry, tossed about by salesfolk, like all buzzwords, as if it were something everyone knew about. As it turns out, of course, very few people who use the word actually know what it means, and as a result, the actual meaning has changed, and is changing, over time. All computer scientists who claim allegiance to this field are constantly peppered with the same question, "What is an ontology?"

This ease has spurred an increasing interest from professionals, the general public, and consequently politicians to make publicly available the tremendous wealth of information kept in museums, archives, and libraries--the so-called memory organizations. Quite naturally, their development has focused on presentation, such as web sites and interfaces to their local databases. Now with more and more information becoming available, there is an increasing demand for targeted global search, comparative studies, data transfer, and data migration between heterogeneous sources of cultural contents. The reality of semantic interoperability is getting frustrating. In the cultural area alone, dozens of standard and hundreds of proprietary metadata and data structures exist as well as hundreds of terminology systems.