DOCSLIB.ORG

A Method of Ontology Integration for Designing Intelligent Problem Solvers

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

applied sciences Article A Method of Ontology Integration for Designing Intelligent Problem Solvers y y Nhon V. Do 1, , Hien D. Nguyen 2,*, and Thanh T. Mai 1 1 Faculty of Information Technology, Ho Chi Minh city Open University, Ho Chi Minh City 700000, Vietnam; [email protected] (N.V.D.); [email protected] (T.T.M.) 2 Faculty of Computer Science, University of Information Technology, VNU-HCM, Ho Chi Minh City 700000, Vietnam * Correspondence: [email protected] y Equal contribution. Received: 31 July 2019; Accepted: 3 September 2019; Published: 10 September 2019 Featured Application: In this paper, we present a method to integrate the knowledge-based systems based on ontology approach. Specially, this method can be used to design an integrated knowledge-based system for solving problems that involve the knowledge from multiple domains, such as Linear Algebra and Graph Theory. Given a specific problem that requires the knowledge from both domains, the system can reason upon the appropriate knowledge in the scope of the problem and generate a step-by-step solution which is very similar to that of humans. Therefore, this knowledge-based system can assist students in learning how to solve problems in many courses, thus meeting the requirements of an Intelligent Problem Solver in education. Abstract: Nowadays, designing knowledge-based systems which involve knowledge from different domains requires deep research of methods and techniques for knowledge integration, and ontology integration has become the foundation for many recent knowledge integration methods. To meet the requirements of real-world applications, methods of ontology integration need to be studied and developed. In this paper, an ontology model used as the knowledge kernel is presented, consisting of concepts, relationships between concepts, and inference rules. Additionally, this kernel is also added to other knowledge, such as knowledge of operators and functions, to form an integrated knowledge-based system. The mechanism of this integration method works upon the integration of the knowledge components in the ontology structure. Besides this, problems and the reasoning method to solve them on the integrated knowledge domain are also studied. Many related problems in the integrated knowledge domain and the reasoning method for solving them are also studied. Such an integrated model can represent the real-world knowledge domain about operators and functions with high accuracy and effectiveness. The ontology model can also be applied to build knowledge bases for intelligent problem solvers (IPS) in many mathematical courses in college, such as linear algebra and graph theory. These IPSs have great potential in helping students perform beer in those college courses. Keywords: knowledge integration; ontology integration; knowledge-based system; knowledge engineering; intelligent problems solver; intelligent software 1. Introduction Nowadays, the knowledge from several sources needs to be integrated in order for machines to accomplish different tasks in a more intelligent way than conventional systems [1]. Knowledge integration is important in intelligent software development [2]. In order to achieve this, knowledge Appl. Sci. 2019, 9, 3793; doi:10.3390/app9183793 www.mdpi.com/journal/applsci Appl. Sci. 2019, 9, 3793 2 of 27 has to be processed and synthesized into knowledge bases. Many intelligent systems have introduced knowledge integration to increase their power. Wolfram|Alpha is an engine for computing answers and providing knowledge [3]. The knowledge of this system is integrated from multiple knowledge domains, such as mathematics, science and technology, and society. The IMS Learning Information Services is a tool to share data about learning [4]. This service supports the exchange of information about courses and learning outcomes between users. It is a combined knowledge-integrated system of learning management platforms, student record systems, and personnel systems. Thus, knowledge integration is an imperative need for designing knowledge-based systems. Knowledge integration is the combination of multiple models for representing knowledge domains into a common model [1,5]. This integration has to meet some requirements, as follows: • Practicality: The method for knowledge integration must be able to represent the real-world knowledge domain in a knowledge base, produce an inference engine that reasons upon the knowledge base, and solve practical problems via a similar reasoning process to that of humans; • Accuracy: The components of the knowledge domain must be represented precisely and fully using the knowledge integration method, in a way that simulates human acquisition. Ontology design and ontology integration are a potential approach to solve the problems of the integration of heterogeneous knowledge [6]. They provide sophisticated knowledge about the environment for task execution [7]. They allow the users to organize information on the taxonomies of concepts, with their own aributes, to describe relationships between the concepts. When data are represented by means of ontologies, software agents can beer understand the content of the data and messages [8]. Domain-based knowledge can be modeled in ontology using ontology markup languages and various ontology tools, like Protege, OILed [9], PDDL (Planning Domain Definition Language)[10], and NDDL (New Domain Definition Language) [11]. The structure of an ontology consists of these basic components: concepts, relationships, and rules [12]. Concepts are the foundation for building the knowledge base, they make the representation clearer and more exact. Relationships on ontology perform connections between concepts. Inference rules are mechanisms for the reasoning to solve problems of the knowledge domain. The model for the integration of knowledge bases needs a knowledge kernel as ontology. Besides, solving a problem with the integrated model may require the knowledge of another pre-solved problem in the knowledge kernel. For example, in the knowledge domain of linear algebra, the knowledge of matrixes is the knowledge kernel. When solving a linear equations system, problems on the matrix, such as row transformation and column tranformation, have to be solved first [13]. Similarly, before solving a problem about vector spaces, it has to be converted to a matrix problem. Hence, the ontology-based model for integrating knowledge-based systems is a combination of the kernel ontology and other knowledge. In this paper, an ontology model used as the knowledge kernel is presented. It includes concepts, relationships between concepts, and inference rules. Some problems for this kernel have been proposed and solved. This kernel is integrated with other knowledge, such as the knowledge of operators and functions, to form an integrated knowledge-based system. The integrating method works by integrating the knowledge components in the ontology structure. The problems in the integrated knowledge domain and the reasoning method to solve them are also studied. With such an integrated model, a real-world knowledge domain about operators and functions can be represented more accurately and effectively. These models are also applied to build the knowledge bases of intelligent problem solvers (IPS) in linear algebra and graph theory courses in university. Appl. Sci. 2019, 9, 3793 3 of 27 2. Related Works There are various ontology-based methods for knowledge integration, most of which focus on basic kinds of ontology and are mainly used for information searching. They have not yet met the requirements of knowledge integration. The Semanticscience Integrated Ontology (SIO) is an ontology for facilitating biomedical knowledge discovery [14]. SIO provides an ontological foundation for the Bio2RDF linked data for the life sciences project and is used for semantic integration and discovery for SADI-based semantic web services (Semantic Automated Discovery and Integration - SADI). However, the concepts of SIO are only basic information for searching. Ontology-based knowledge integration is also used for semantic web services. Ontology WSMO is built based on the Web Service Modeling Framework (WSMO) [15]. WSMO defines four top level elements as the main concepts which have to be described in order to describe the semantic web services: ontologies, services, mediators, and goals. These methods only solve the integration of ontology as information but cannot support solving decision problems. Fuzzy ontology integration is used for the representation of uncertain knowledge on the semantic web [16,17]. The author in [16] used description logic and fuzzy set theory to represent fuzzy logic and reason on it. The study in [17] presented a method to integrate fuzzy ontology based on consensus theory. Nonetheless, those methods are just theoretical and cannot be applied in the complex knowledge domains in practice. Ontology COKB (Computational Objects Knowledge Base) is a useful ontology to represent complex knowledge domains [12]. This ontology can be used to describe many kinds of knowledge, such as knowledge about relationships, operators, and functions. It can be applied to build intelligent educational systems [18]. However, ontology COKB is too general to represent a specific knowledge domain, so it is very difficult to apply. Furthermore, the combination problems on the knowledge components in COKB have not yet been mentioned. Ontology is also a technique model for information retrieval via the processing and translation of ontological
Recommended publications
  • Download Slides

    Download Slides

    a platform for all that we know savas parastatidis http://savas.me savasp transition from web to apps increasing focus on information (& knowledge) rise of personal digital assistants importance of near-real time processing http://aptito.com/blog/wp-content/uploads/2012/05/smartphone-apps.jpg today... storing computing computers are huge amounts great tools for of data managing indexing example google and microsoft both have copies of the entire web (and more) for indexing purposes tomorrow... storing computing computers are huge amounts great tools for of data managing indexing acquisition discovery aggregation organization we would like computers to of the world’s information also help with the automatic correlation analysis and knowledge interpretation inference data information knowledge intelligence wisdom expert systems watson freebase wolframalpha rdbms google now web indexing data is symbols (bits, numbers, characters) information adds meaning to data through the introduction of relationship - it answers questions such as “who”, “what”, “where”, and “when” knowledge is a description of how the world works - it’s the application of data and information in order to answer “how” questions G. Bellinger, D. Castro, and A. Mills, “Data, Information, Knowledge, and Wisdom,” Inform. pp. 1–4, 2004 web – the data platform web – the information platform web – the knowledge platform foundation for new experiences “wisdom is not a product of schooling but of the lifelong attempt to acquire it” representative examples wolframalpha watson source:
  • Ontologies and Languages for Representing Mathematical Knowledge on the Semantic Web

    Ontologies and Languages for Representing Mathematical Knowledge on the Semantic Web

    Ontologies and Languages for Representing Mathematical Knowledge on the Semantic Web Editor(s): Aldo Gangemi, ISTC-CNR Rome, Italy Solicited review(s): Claudio Sacerdoti Coen, University of Bologna, Italy; Alexandre Passant, DERI, National University of Galway, Ireland; Aldo Gangemi, ISTC-CNR Rome, Italy Christoph Lange data vocabularies and domain knowledge from pure and ap- plied mathematics. FB 3 (Mathematics and Computer Science), Many fields of mathematics have not yet been imple- University of Bremen, Germany mented as proper Semantic Web ontologies; however, we Computer Science, Jacobs University Bremen, show that MathML and OpenMath, the standard XML-based exchange languages for mathematical knowledge, can be Germany fully integrated with RDF representations in order to con- E-mail: [email protected] tribute existing mathematical knowledge to the Web of Data. We conclude with a roadmap for getting the mathematical Web of Data started: what datasets to publish, how to inter- link them, and how to take advantage of these new connec- tions. Abstract. Mathematics is a ubiquitous foundation of sci- Keywords: mathematics, mathematical knowledge manage- ence, technology, and engineering. Specific areas of mathe- ment, ontologies, knowledge representation, formalization, matics, such as numeric and symbolic computation or logics, linked data, XML enjoy considerable software support. Working mathemati- cians have recently started to adopt Web 2.0 environments, such as blogs and wikis, but these systems lack machine sup- 1. Introduction: Mathematics on the Web – State port for knowledge organization and reuse, and they are dis- of the Art and Challenges connected from tools such as computer algebra systems or interactive proof assistants.
  • Datatone: Managing Ambiguity in Natural Language Interfaces for Data Visualization Tong Gao1, Mira Dontcheva2, Eytan Adar1, Zhicheng Liu2, Karrie Karahalios3

    Datatone: Managing Ambiguity in Natural Language Interfaces for Data Visualization Tong Gao1, Mira Dontcheva2, Eytan Adar1, Zhicheng Liu2, Karrie Karahalios3

    DataTone: Managing Ambiguity in Natural Language Interfaces for Data Visualization Tong Gao1, Mira Dontcheva2, Eytan Adar1, Zhicheng Liu2, Karrie Karahalios3 1University of Michigan, 2Adobe Research 3University of Illinois, Ann Arbor, MI San Francisco, CA Urbana Champaign, IL fgaotong,[email protected] fmirad,[email protected] [email protected] ABSTRACT to be both flexible and easy to use. General purpose spread- Answering questions with data is a difficult and time- sheet tools, such as Microsoft Excel, focus largely on offer- consuming process. Visual dashboards and templates make ing rich data transformation operations. Visualizations are it easy to get started, but asking more sophisticated questions merely output to the calculations in the spreadsheet. Asking often requires learning a tool designed for expert analysts. a “visual question” requires users to translate their questions Natural language interaction allows users to ask questions di- into operations on the spreadsheet rather than operations on rectly in complex programs without having to learn how to the visualization. In contrast, visual analysis tools, such as use an interface. However, natural language is often ambigu- Tableau,1 creates visualizations automatically based on vari- ous. In this work we propose a mixed-initiative approach to ables of interest, allowing users to ask questions interactively managing ambiguity in natural language interfaces for data through the visualizations. However, because these tools are visualization. We model ambiguity throughout the process of often intended for domain specialists, they have complex in- turning a natural language query into a visualization and use terfaces and a steep learning curve. algorithmic disambiguation coupled with interactive ambigu- Natural language interaction offers a compelling complement ity widgets.
  • Arxiv:1910.13561V1 [Cs.LG] 29 Oct 2019 E-Mail: Mohamed.Gaber@Bcu.Ac.Uk M

    Arxiv:1910.13561V1 [Cs.LG] 29 Oct 2019 E-Mail: [email protected] M

    Noname manuscript No. (will be inserted by the editor) A Heuristically Modified FP-Tree for Ontology Learning with Applications in Education Safwan Shatnawi · Mohamed Medhat Gaber ∗ · Mihaela Cocea Received: date / Accepted: date Abstract We propose a heuristically modified FP-Tree for ontology learning from text. Unlike previous research, for concept extraction, we use a regular expression parser approach widely adopted in compiler construction, i.e., deterministic finite automata (DFA). Thus, the concepts are extracted from unstructured documents. For ontology learning, we use a frequent pattern mining approach and employ a rule mining heuristic function to enhance its quality. This process does not rely on predefined lexico-syntactic patterns, thus, it is applicable for different subjects. We employ the ontology in a question-answering system for students' content-related questions. For validation, we used textbook questions/answers and questions from online course forums. Subject experts rated the quality of the system's answers on a subset of questions and their ratings were used to identify the most appropriate automatic semantic text similarity metric to use as a validation metric for all answers. The Latent Semantic Analysis was identified as the closest to the experts' ratings. We compared the use of our ontology with the use of Text2Onto for the question-answering system and found that with our ontology 80% of the questions were answered, while with Text2Onto only 28.4% were answered, thanks to the finer grained hierarchy our approach is able to produce. Keywords Ontologies · Frequent pattern mining · Ontology learning · Question answering · MOOCs S. Shatnawi College of Applied Studies, University of Bahrain, Sakhair Campus, Zallaq, Bahrain E-mail: [email protected] M.
  • Building Dialogue Structure from Discourse Tree of a Question

    Building Dialogue Structure from Discourse Tree of a Question

    The Workshops of the Thirty-Second AAAI Conference on Artificial Intelligence Building Dialogue Structure from Discourse Tree of a Question Boris Galitsky Oracle Corp. Redwood Shores CA USA [email protected] Abstract ed, chat bot’s capability to maintain the cohesive flow, We propose a reasoning-based approach to a dialogue man- style and merits of conversation is an underexplored area. agement for a customer support chat bot. To build a dia- When a question is detailed and includes multiple sen- logue scenario, we analyze the discourse tree (DT) of an ini- tences, there are certain expectations concerning the style tial query of a customer support dialogue that is frequently of an answer. Although a topical agreement between ques- complex and multi-sentence. We then enforce what we call tions and answers have been extensively addressed, a cor- complementarity relation between DT of the initial query respondence in style and suitability for the given step of a and that of the answers, requests and responses. The chat bot finds answers, which are not only relevant by topic but dialogue between questions and answers has not been thor- also suitable for a given step of a conversation and match oughly explored. In this study we focus on assessment of the question by style, argumentation patterns, communica- cohesiveness of question/answer (Q/A) flow, which is im- tion means, experience level and other domain-independent portant for a chat bots supporting longer conversation. attributes. We evaluate a performance of proposed algo- When an answer is in a style disagreement with a question, rithm in car repair domain and observe a 5 to 10% im- a user can find this answer inappropriate even when a topi- provement for single and three-step dialogues respectively, in comparison with baseline approaches to dialogue man- cal relevance is high.
  • A New Kind of Science

    A New Kind of Science

    Wolfram|Alpha, A New Kind of Science A New Kind of Science Wolfram|Alpha, A New Kind of Science by Bruce Walters April 18, 2011 Research Paper for Spring 2012 INFSY 556 Data Warehousing Professor Rhoda Joseph, Ph.D. Penn State University at Harrisburg Wolfram|Alpha, A New Kind of Science Page 2 of 8 Abstract The core mission of Wolfram|Alpha is “to take expert-level knowledge, and create a system that can apply it automatically whenever and wherever it’s needed” says Stephen Wolfram, the technologies inventor (Wolfram, 2009-02). This paper examines Wolfram|Alpha in its present form. Introduction As the internet became available to the world mass population, British computer scientist Tim Berners-Lee provided “hypertext” as a means for its general consumption, and coined the phrase World Wide Web. The World Wide Web is often referred to simply as the Web, and Web 1.0 transformed how we communicate. Now, with Web 2.0 firmly entrenched in our being and going with us wherever we go, can 3.0 be far behind? Web 3.0, the semantic web, is a web that endeavors to understand meaning rather than syntactically precise commands (Andersen, 2010). Enter Wolfram|Alpha. Wolfram Alpha, officially launched in May 2009, is a rapidly evolving "computational search engine,” but rather than searching pre‐existing documents, it actually computes the answer, every time (Andersen, 2010). Wolfram|Alpha relies on a knowledgebase of data in order to perform these computations, which despite efforts to date, is still only a fraction of world’s knowledge. Scientist, author, and inventor Stephen Wolfram refers to the world’s knowledge this way: “It’s a sad but true fact that most data that’s generated or collected, even with considerable effort, never gets any kind of serious analysis” (Wolfram, 2009-02).
  • Problem of Extracting the Knowledge of Experts Fkom the Perspective of Experimental Psychology

    Problem of Extracting the Knowledge of Experts Fkom the Perspective of Experimental Psychology

    AI Magazine Volume 8 Number 2 (1987) (© AAAI) The ‘Problem of Extracting the Knowledge of Experts fkom the Perspective of Experimental Psychology RobertR.Hoffman or perceptual and conceptual and represent their special knowledge The first step in the development of an problems requiring the skills of . [It] may take several months of the expert system is the extraction and charac- an expert, expertise is rare, the expert’s time and even more of the terization of the knowledge and skills of an expert’s knowledge is extremely system builder’s” (p. 264). Three years expert. This step is widely regarded as the detailed and interconnected, and our later, Duda and Shortliffe (1983) major bottleneck in the system develop- scientific understanding of the echoed this lament: “The identifica- ment process To assist knowledge engi- expert’s perceptual and conceptual tion and encoding of knowledge is one neers and others who might be interested in the development of an expert system, I processes is limited. Research on the of the most complex and arduous offer (1) a working classification of meth- skills of experts in any domain affords tasks encountered in the construction ods for extracting an expert’s knowledge, an excellent opportunity for both of an expert system” (p. 265). (2) some ideas about the types of data that basic and practical experimentation. Some common phrases that occur the methods yield, and (3) a set of criteria My investigations fall on the experi- in the literature are “knowledge acqui- by which the methods can be compared mental psychology side of expert sys- sition is the time-critical component” relative to the needs of the system develop- tem engineering, specifically the prob- (Freiling et al.
  • Towards Unsupervised Knowledge Extraction

    Towards Unsupervised Knowledge Extraction

    Towards Unsupervised Knowledge Extraction Dorothea Tsatsoua,b, Konstantinos Karageorgosa, Anastasios Dimoua, Javier Carbob, Jose M. Molinab and Petros Darasa aInformation Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH), 6th km Charilaou-Thermi Road, 57001, Thermi, Thessaloniki, Greece bComputer Science Department, University Carlos III of Madrid, Av. Universidad 30, Leganes, Madrid 28911, Spain Abstract Integration of symbolic and sub-symbolic approaches is rapidly emerging as an Artificial Intelligence (AI) paradigm. This paper presents a proof-of-concept approach towards an unsupervised learning method, based on Restricted Boltzmann Machines (RBMs), for extracting semantic associations among prominent entities within data. Validation of the approach is performed in two datasets that connect lan- guage and vision, namely Visual Genome and GQA. A methodology to formally structure the extracted knowledge for subsequent use through reasoning engines is also offered. Keywords knowledge extraction, unsupervised learning, spectral analysis, formal knowledge representation, symbolic AI, sub-symbolic AI, neuro-symbolic integration 1. Introduction Nowadays, artificial intelligence (AI) is linked mostly to machine learning (ML) solutions, enabling machines to learn from data and subsequently make predictions based on unidentified patterns in data, taking advantage of neural network (NN)-based methods. However, AI is still far from encompassing human-like cognitive capacities, which include not only learning but also understanding1, abstracting, planning, representing knowledge and logically reasoning over it. On the other hand, Knowledge Representation and Reasoning (KRR) techniques allow machines to reason about structured knowledge, in order to perform human-like complex problem solving and decision-making. AI foundations propose that all the aforementioned cognitive processes (learning, abstracting, representing, reasoning) need to be integrated under a unified strategy, in order to advance to In A.
  • RIO: an AI Based Virtual Assistant

    RIO: an AI Based Virtual Assistant

    International Journal of Computer Applications (0975 – 8887) Volume 180 – No.45, May 2018 RIO: An AI based Virtual Assistant Samruddhi S. Sawant Abhinav A. Bapat Komal K. Sheth Department of Information Department of Information Department of Information Technology Technology Technology NBN Sinhgad Technical Institute NBN Sinhgad Technical Institute NBN Sinhgad Technical Institute Campus Campus Campus Pune, India Pune, India Pune, India Swapnadip B. Kumbhar Rahul M. Samant Department of Information Technology Professor NBN Sinhgad Technical Institute Campus Department of Information Technology Pune, India NBN Sinhgad Technical Institute Campus Pune, India ABSTRACT benefitted by such virtual assistants. The rise of messaging In this world of corporate companies, a lot of importance is apps, the explosion of the app ecosystem, advancements in being given to Human Resources. Human Capital artificial intelligence (AI) and cognitive technologies, a Management (HCM) is an approach of Human Resource fascination with conversational user interfaces and a wider Management that connotes to viewing of employees as assets reach of automation are all driving the chatbot trend. A that can be invested in and managed to maximize business chatbot can be deployed over various platforms namely value. In this paper, we build a chatbot to manage all the Facebook messenger, Slack, Skype, Kik, etc. The most functions of HRM namely -- core HR, Talent Management preferred platform among businesses seems to be Facebook and Workforce management. A chatbot is a service, powered messenger (92%). There are around 80% of businesses that by rules and sometimes artificial intelligence that you interact would like to host their chatbot on their own website.
  • Ontology and Information Systems

    Ontology and Information Systems

    Ontology and Information Systems 1 Barry Smith Philosophical Ontology Ontology as a branch of philosophy is the science of what is, of the kinds and structures of objects, properties, events, processes and relations in every area of reality. ‘Ontology’ is often used by philosophers as a synonym for ‘metaphysics’ (literally: ‘what comes after the Physics’), a term which was used by early students of Aristotle to refer to what Aristotle himself called ‘first philosophy’.2 The term ‘ontology’ (or ontologia) was itself coined in 1613, independently, by two philosophers, Rudolf Göckel (Goclenius), in his Lexicon philosophicum and Jacob Lorhard (Lorhardus), in his Theatrum philosophicum. The first occurrence in English recorded by the OED appears in Bailey’s dictionary of 1721, which defines ontology as ‘an Account of being in the Abstract’. Methods and Goals of Philosophical Ontology The methods of philosophical ontology are the methods of philosophy in general. They include the development of theories of wider or narrower scope and the testing and refinement of such theories by measuring them up, either against difficult 1 This paper is based upon work supported by the National Science Foundation under Grant No. BCS-9975557 (“Ontology and Geographic Categories”) and by the Alexander von Humboldt Foundation under the auspices of its Wolfgang Paul Program. Thanks go to Thomas Bittner, Olivier Bodenreider, Anita Burgun, Charles Dement, Andrew Frank, Angelika Franzke, Wolfgang Grassl, Pierre Grenon, Nicola Guarino, Patrick Hayes, Kathleen Hornsby, Ingvar Johansson, Fritz Lehmann, Chris Menzel, Kevin Mulligan, Chris Partridge, David W. Smith, William Rapaport, Daniel von Wachter, Chris Welty and Graham White for helpful comments.
  • Ontology to Appear in the Encyclopedia of Database Systems, Ling Liu and M

    Ontology to Appear in the Encyclopedia of Database Systems, Ling Liu and M

    Ontology to appear in the Encyclopedia of Database Systems, Ling Liu and M. Tamer Özsu (Eds.), Springer-Verlag, 2008. TITLE OF ENTRY Ontology BYLINE Tom Gruber, http://tomgruber.org. Formerly of Stanford University, Intraspect Software, and RealTravel.com. SYNONYMS computational ontology, semantic data model, ontological engineering DEFINITION In the context of computer and information sciences, an ontology defines a set of representational primitives with which to model a domain of knowledge or discourse. The representational primitives are typically classes (or sets), attributes (or properties), and relationships (or relations among class members). The definitions of the representational primitives include information about their meaning and constraints on their logically consistent application. In the context of database systems, ontology can be viewed as a level of abstraction of data models, analogous to hierarchical and relational models, but intended for modeling knowledge about individuals, their attributes, and their relationships to other individuals. Ontologies are typically specified in languages that allow abstraction away from data structures and implementation strategies; in practice, the languages of ontologies are closer in expressive power to first-order logic than languages used to model databases. For this reason, ontologies are said to be at the "semantic" level, whereas database schema are models of data at the "logical" or "physical" level. Due to their independence from lower level data models, ontologies are used for integrating heterogeneous databases, enabling interoperability among disparate systems, and specifying interfaces to independent, knowledge-based services. In the technology stack of the Semantic Web standards [1], ontologies are called out as an explicit layer. There are now standard languages and a variety of commercial and open source tools for creating and working with ontologies.
  • Surviving the AI Hype – Fundamental Concepts to Understand Artificial Intelligence

    Surviving the AI Hype – Fundamental Concepts to Understand Artificial Intelligence

    WHITEPAPEr_ Surviving the AI Hype – Fund amental concepts to understand Artificial Intelligence 23.12.2016 luca-d3.com Whitepaper_ Surviving the AI Hype – Fundamental concepts to understand Artificial Intelligence Index 1. Introduction.................................................................................................................................................................................... 3 2. What are the most common definitions of AI? ......................................................................................................................... 3 3. What are the sub areas of AI? ...................................................................................................................................................... 5 4. How “intelligent” can Artificial Intelligence get? ....................................................................................................................... 7 Strong and weak AI ............................................................................................................................................................... 7 The Turing Test ..................................................................................................................................................................... 7 The Chinese Room Argument ............................................................................................................................................. 8 The Intentional Stance ........................................................................................................................................................