DOCSLIB.ORG

Semantic Enterprise

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Semantic Enterprise • Cognizant 20-20 Insights Semantic Enterprise: A Step Toward Agent-Driven Integration Knowledge-driven enterprises can become more adaptable, dynamic and collaborative by using semantic technologies to integrate openly available data into the ecosystem. Executive Summary enting the enterprise. We look at how semantic technologies can enable more agile and automat- Technology waves are rolling in faster than ever, ed approaches to integration, and we highlight and enterprises are being reshaped by emerging some of the challenges associated with semantic Web technologies. Across industries, technology technology adoption. is no longer just a support system but also an integral part of today’s fast-moving, “learn as you Agents and Semantics go” business models. To understand how business is evolving, it is For instance, beyond historical transactional data, important to track the evolution of Web tech- businesses are looking for more current and nologies, as these are currently a key business up-to-date data for informed decision-making. enabler. Even before the social Web (or Web 2.0) Retailers are integrating external blogs, social was fully realized, business leaders began leverag- sites and data from ubiquitous mobile devices ing emerging semantic technologies (Web 3.0) to into their analytics systems to better understand enable data sharing among social networks, with the market pulse. Insurance companies, agents user permission. Then, just as Web 3.0 was learn- and customers are collaborating more effectively ing to walk, Web 4.0, the self-learning and self- by integrating their systems to work online. organizing Web, began incubating. Figure 1, next page, describes these technology waves in detail. But the job of incorporating external data or onboarding external agents today often involves To understand the semantic Web, it’s important to manual processes and development cycles that first grasp two core concepts: semantic technolo- span months. With the advancement of semantic gies and agents. technologies, however, businesses can integrate openly available data into the ecosystem more • Semantic technologies: Data is meaning- effectively. ful only when it is accompanied by structure. While syntax is about grammatical structure, In this white paper, we discuss semantic technol- semantics governs the relationship between ogy adoption and how these capabilities are reori- articles, vowels, consonants, verbs and associat- cognizant 20-20 insights | december 2013 Evolution of the Web Technology Focal Approximate Characteristics Key Enablers Wave Point Timeline Information Rudimentary personal and HTML, directories, 1994-2000 Web 1.0 Web corporate Web sites data silos Personal blogging, social media Social Web CSS, P2P, AJAX 2000-2010 Web 2.0 and networking Distributed social Web, with open SPARQL, OWL, Semantic Web 2010-2020 Web 3.0 data sharing (with permission) semantic search Humans and computer systems Intelligent agents, Intelligent interacting in distributed search, 2020-2030 Web 4.0 Web information symbiosis ubiquity Figure 1 ed rules. Both the syntax and the semantics of a relationships between every object in the world domain need to be defined in machine language and creating a web of data. (For more on this so that the machine or computer systems can topic, see our white paper, “How Semantic interpret that domain. An example is the spell Technology Drives Agile Business.”) checker, a software agent that compiles work • Agents: Agents are complex software systems based on lexical syntax and semantics, artic- that are designed to perform a variety of ulated in machine language. Similarly, it is tasks by interpreting a machine-readable important to define the semantics of a business knowledge base. For example compilers are domain or enterprise so that the IT systems can software agents that work based on the operate more efficiently. syntax and semantics defined for a program- Semantic technologies aim to create a ming language. Similarly, Web crawlers are knowledge base for computer programs to complex agents that retrieve processes and work more intelligently by linking objects harvest data automatically from Web sites. A and building relationships. The knowledge smarter breed of Web crawlers is emerging for base helps computer systems understand harvesting semantic Web content. the context of the work being accomplished Figure 2 illustrates how a computer system can during runtime operations and anticipate the use data to develop wisdom by adding structure necessary actions, with the goal of building and meaning to data and processing it. How Data Leads to Wisdom Wisdom A U.S. citizen is likely to have a Social Security Number. Analytics/AI/Datas/AI/Dat Mining Residents of a country have a personal identifier; Knowledge a Social Security Number is a personal identifier. Add MeaningMea Social Security Number = Information 123-45-6789. Building plan = xyz.jpg Add StructureStruc Data Numbers, text, images, videos, etc. Figure 2 cognizant 20-20 insights 2 Semantic Web Semantic Enterprise The semantic Web is about making it easier The semantic, or knowledge-driven, enterprise for computer systems to interpret content. Its describes content using ontologies by tagging primary focus is tagging the content based on and linking information. This results in an inter- what it “means,” thus adding structure to data. linked, rich information tree of knowledge that The semantic Web is an indirect response to continues to grow over time. The semantic enter- the business need for efficiency and getting the prise provides contextual connections to both most out of its investments, from employees to the identity of the enterprise and the assets that equipment. keep it running, creating a knowledgebase for computer Semantic Web For example, in 2010, the BBC upgraded its systems to interpret the World Cup Web site with semantic Web technolo- meaning of their actions. This databases — gies, curating and interlinking the site’s content, results in more efficient pro- generally known as 1 without employing a large fleet of editors. The cessing and decision-making. knowledgebases result: a highly dynamic, interactive, information- Assets are defined by the rich and user-oriented site, with aggregation at people, process and technol- — can store more many levels (such as player, team, geography and ogy resources associated with sophisticated and group). the enterprise, and identity is referenceable defined by capturing the enter- The richness of the site’s information, which prise vision, mission, strategy metadata than included 700 topical index pages, could never and principles. relational databases. have been produced via traditional methods. Thus, they allow Semantic Web technologies, on the other hand, The Department of Defense added structure to the unstructured information, (DoD) makes use of semantic complex algorithms typically handled by media, through appropriate technology across systems to to directly reason tagging. form an executable, integrated with inferences on and consumable architecture.3 Another key aspect of the Web site’s success was Since 2011, the DoD’s Business the data structures. cross-document relationships; ontology helped Mission Area has mandated capture the complex interlinking of the documents the use of semantic Web technologies as the based on topics, authors, citations and multiple foundational architecture for new integration revisions. Managing these relationships through projects. The organization links disparate infor- traditional relational databases would have been mation systems by overlaying them with semantic cumbersome and inefficient, increasing time to models, which has decreased the time it takes to market. Semantic Web databases — generally get a new enterprise system up and running from known as knowledge bases — can store more six to nine months to less than 90 days. sophisticated and referenceable metadata than relational databases. Thus, they allow complex Current tools enable enterprise modeling to algorithms to directly reason with inferences on define the relationships among various enterprise the data structures. entities. When these models are maintained, the semantic enterprise can distinguish the present The interlinked, metadata-driven nature of the state from the past, add constraints, guide the semantic Web enables enterprises to stay abreast present and predict the future. of constantly changing usage patterns. The standardized metadata helps computer systems However, current enterprise architecture tools decipher meaning and act on it. Agents, thus, can are not flexible enough for extending the model run complex algorithms to directly reason with with respect to newer entities. For example, these inferences on the data structures. This is why tools cannot be extended to capture physical semantic Web languages are a key part of the assets along with the technology assets. Because knowledge representation of artificial intelligence these tools are not semantically aware, they (AI). provide a model that can neither be interpreted by other software nor be exchanged with the Other organizations have created production extended enterprise or the external world. As systems with semantic Web technologies, as well, such, it becomes an uphill task for the enterprise including Time, Inc., Elsevier and the Library of to adapt to the dynamics of change. Congress.2 cognizant 20-20 insights 3 Semantic Technologies and and perfumes) and services (such as search, Agent-Driven Integration
Load more

Recommended publications
  • Poolparty Semantic Suite Functional Overview
    PoolParty Semantic Suite Functional Overview Andreas Blumauer CEO & Managing Partner Semantic Web Company / PoolParty Semantic Suite 2 Semantic Web Company (SWC) ▸ Founded in 2004, based in Vienna ▸ Privately held 3 ▸ 50 FTE ▸ Software Engineers & Consultants for Introducing NLP, Semantics and Machine learning Semantic Semantic Web AI ▸ Developer & Vendor of Company PoolParty Semantic Suite ▸ Participating in projects with €2.5 million funding for R&D ▸ ~30% revenue growth/year ▸ SWC named to KMWorld’s ‘100 Companies That Matter in Knowledge Management’ in 2016, 2017 and 2018 ▸ www.semantic-web.com 2017 2016 PoolParty Semantic Suite ▸ Most complete Semantic Middleware on the Global Market ▸ Semantic AI: Fusion of Knowledge 4 Graphs, NLP, and Machine Learning ▸ W3C standards compliant Fact sheet: PoolParty ▸ First release in 2009 ▸ Current version 7.0 ▸ On-premises or cloud-based ▸ Over 200 installations world-wide ▸ Named as Sample Vendor in Gartner’s Hype Cycle for AI 2018 ▸ KMWorld listed PoolParty as Trend-Setting Product 2015, 2016, 2017, and 2018 ▸ www.poolparty.biz We work with Global Fortune Companies, and with some of the largest GOs and NGOs from over 20 countries. SWC head- US UK quarters West US Selected Customer References East 5 ● Credit Suisse ● Boehringer Ingelheim Selected ● Roche ● adidas Customer ● The Pokémon Company ● Fluor AUS/ References ● Harvard Business School NZL ● Wolters Kluwer and Partners ● Philips ● Nestlé ● Electronic Arts Selected Partners ● Springer Nature ● Pearson - Always Learning ● Enterprise Knowledge ● Healthdirect Australia ● Mekon Intelligent Content Solutions ● World Bank Group ● Soitron ● Canadian Broadcasting Corporation ● Accenture ● Oxford University Press ● EPAM Systems ● International Atomic Energy Agency ● BAON Enterprises ● Siemens ● Findwise ● Singapore Academy of Law ● Tellura Semantics ● Inter-American Development Bank ● HPC ● Council of the E.U.
    [Show full text]
  • Semantic Technologies for Systems Engineering (ST4SE)
    Semantic Technologies for Systems Engineering (ST4SE) Update at INCOSE IW, 27 Jan 2019, Torrance, CA, USA Hans Peter de Koning European Space Agency, ESA/ESTEC, Noordwijk, The Netherlands Based on earlier presentations and contributions by other ST4SE Core Team Members Objectives of the ST4SE Foundation To promote and champion the open-source development and utilization of ontologies and semantic technologies to support system engineering practice, education, and research 1. Provide a semantically rich language to communicate among systems engineers and other stakeholders 2. Define patterns that can be used to check for consistency and completeness 3. Support querying of information from model 4. Focus on adding value by balancing the expected benefits from being formal and the cost of being formal ST4SE Update | INCOSE IW | 2019-01-27, Torrance, CA, USA 2 MBSE Challenge – 3 Kinds of Communication . Person ↔ Person . Machine ↔ Machine . Person ↔ Machine Person . All bi-directional (of course) . All need to work flawlessly Machine ST4SE Update | INCOSE IW | 2019-01-27, Torrance, CA, USA 3 Outline . Background on Semantic Technologies • Knowledge representation, reasoning, querying . Semantic Technologies for System Engineering • Motivation • Scope and focus • Relationship between ST4SE and SysML 2.0 . ST4SE Approach • Open-source foundation • Bootstrapping: (best) practices for defining, demonstrating and documenting patterns ST4SE Update | INCOSE IW | 2019-01-27, Torrance, CA, USA 4 Increasing levels of semantic precision (and understanding
    [Show full text]
  • Is It an Agent, Or Just a Program?: a Taxonomy for Autonomous Agents
    Agent or Program http://www.msci.memphis.edu/~franklin/AgentProg.html Is it an Agent, or just a Program?: A Taxonomy for Autonomous Agents Stan Franklin and Art Graesser Institute for Intelligent Systems University of Memphis Proceedings of the Third International Workshop on Agent Theories, Architectures, and Languages, Springer-Verlag, 1996. Abstract The advent of software agents gave rise to much discussion of just what such an agent is, and of how they differ from programs in general. Here we propose a formal definition of an autonomous agent which clearly distinguishes a software agent from just any program. We also offer the beginnings of a natural kinds taxonomy of autonomous agents, and discuss possibilities for further classification. Finally, we discuss subagents and multiagent systems. Introduction On meeting a friend or colleague that we haven't seen for a while, or a new acquaintance, some version of the following conversation often ensues: What are you working on these days? Control structures for autonomous agents. Autonomous agents? What do you mean by that? A brief explanation is then followed by: But agents sound just like computer programs. How are they different? This elicits a more satisfying explanation that distinguishes between agent and program. The nature of this "more satisfying explanation" motivates this essay. After a review of some of the many ways the term "agent" has been used within the context of autonomous agents, we'll propose and defend a notion of agent that is clearly distinct from a program. This discussion will lead us to a discussion of possible classifications for autonomous agents.
    [Show full text]
  • Semantic Science: Ontologies, Data and Probabilistic Theories
    Semantic Science: Ontologies, Data and Probabilistic Theories David Poole1, Clinton Smyth2, and Rita Sharma2 1 Department of Computer Science, University of British Columbia http://www.cs.ubc.ca/spider/poole/ 2 Georeference Online Ltd., http://www.georeferenceonline.com/ Abstract. This chapter overviews work on semantic science. The idea is that, using rich ontologies, both observational data and theories that make (probabilistic) predictions on data are published for the purposes of improving or comparing the theories, and for making predictions in new cases. This paper concentrates on issues and progress in having machine accessible scientific theories that can be used in this way. This paper presents the grand vision, issues that have arisen in building such systems for the geological domain (minerals exploration and geohazards), and sketches the formal foundations that underlie this vision. The aim is to get to the stage where: any new scientific theory can be tested on all available data; any new data can be used to evaluate all existing theories that make predictions on that data; and when someone has a new case they can use the best theories that make predictions on that case. 1 Introduction The aim of the semantic web (Berners-Lee and Fischetti, 1999; Berners-Lee et al., 2001) is that the world's information is available in a machine-understandable form. This chapter overviews what we call semantic science, the application of semantic technology and reasoning under uncertainty to the practice of science. Semantic science requires machine-understandable information of three sorts: on- tologies to define vocabulary, data about observations of the world, and theories that make predictions on such data.
    [Show full text]
  • Usage of Ontologies and Software Agents for Knowledge-Based Design of Mechatronic Systems
    INTERNATIONAL CONFERENCE ON ENGINEERING DESIGN, ICED’07 28 - 31 AUGUST 2007, CITE DES SCIENCES ET DE L'INDUSTRIE, PARIS, FRANCE USAGE OF ONTOLOGIES AND SOFTWARE AGENTS FOR KNOWLEDGE-BASED DESIGN OF MECHATRONIC SYSTEMS Ewald G. Welp1, Patrick Labenda1 and Christian Bludau2 1Ruhr-University Bochum, Germany 2Behr-Hella Thermocontrol GmbH, Lippstadt, Germany ABSTRACT Already in [1, 2 and 3] the newly developed Semantic Web Service Platform SEMEC (SEMantic and MEChatronics) has been introduced and explained. It forms an interconnection between semantic web, semantic web services and software agents, offering tools and methods for a knowledge-based design of mechatronic systems. Their development is complex and connected to a high information and knowledge need on the part of the engineers involved in it. Most of the tools nowadays available cannot meet this need to an adequate degree and in the demanded quality. The developed platform focuses on the design of mechatronic products supported by Semantic web services under use of the Semantic web as a dynamic and natural language knowledge base. The platform itself can also be deployed for the development of homogenous, i.e. mechanical and electronical systems. Of special scientific interest is the connection to the internet and semantic web, respectively, and its utilization within a development process. The platform can be used to support interdisciplinary design teams at an early phase in the development process by offering context- sensitive knowledge and by this to concretize as well as improve mechatronic concepts [1]. Essential components of this platform are a design environment, a domain ontology mechatronics as well as a software agent.
    [Show full text]
  • Building Semantic Enterprise Architecture Solutions with Topbraid Suite™
    Towards Executable Enterprise Models: Building Semantic Enterprise Architecture Solutions with TopBraid Suite™ Towards Executable Enterprise Models: Building Semantic Enterprise Architecture Solutions with TopBraid Suite™ What is Enterprise Architecture and Why is it Important to Business? Enterprise Architecture (EA) captures “what is happening” in the enterprise: how the enterprise’s activities, processes, capabilities, systems and components, information resources and technologies relate to the enterprise’s missions, goals and measurement system. To do this, EA expresses the interrelated structure of an enterprise’s processes, capabilities systems, information resources and technology dependencies. The objective of EA is to be able to understand the relationships between these elements; analyze and continuously adjust them for alignment with the business strategy, improved effectiveness and quality of service. When a Business Executive asks "what is an enterprise architecture and why is it useful?" a typical answer is “a model of how the enterprise works.” Useful, because it explains how business activities and processes use capabilities; how capabilities map to competencies and IT systems; how systems depend on technologies; and, how value is generated with measurable results that realize business goals. Increasingly, the demands of modern enterprises to both optimize their day to day operations and develop strategic capabilities and resources in their organizations, processes and infrastructures are driving the need for higher levels of Enterprise Architecture (EA) solutions. EA has emerged from a pure IT discipline to an embedded business tool that can focus on business-centered capability management. To support the business needs of complex, distributed enterprises, Enterprise Architecture practice and enabling technology/tools are evolving from Reference-only (text, models) to Interoperable (standards based) to Executable (federated, model-based, semantic-enabled).
    [Show full text]
  • A Software System for Agent-Assisted Ontology Building
    A SOFTWARE SYSTEM FOR AGENT-ASSISTED ONTOLOGY BUILDING by Denish M umbaiwala B.Eng. Electronics The Maharaja Sayajirao University of Baroda, 2007 THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN COMPUTER SCIENCE UNIVERSITY OF NORTHERN BRITISH COLUMBIA March 2016 © Denish Mumbaiwala, 2016 Abstract This thesis investigates how one can design a team of intelligent software agents that helps its human partner develop a formal ontology from a relational database and enhance it with higher-level abstractions. The resulting efficiency of ontology devel- opment could facilitate the building of intelligent decision support systems that allow: high-level semantic queries on legacy relational databases; autonomous implementa- tion within a host organization; and incremental deployment without affecting the underlying database or its conventional use. We introduce a set of design principles, formulate the prototype system requirements and architecture, elaborate agent roles and interactions, develop suitable design techniques, and test the approach through practical implementation of selected features. We endow each agent with model meta- ontology, which enables it to reason and communicate about ontology, and planning meta-ontology, which captures the role-specific know-how of the ontology building method. We also assess the maturity of development tools for a larger-scale imple- mentation. Contents Abstract List of Figures vi Acknowledgements viii Dedication 1x 1 Introduction 1 2 Background and Related Work 6 2.1 Decision Support Systems (DSS) . 6 2.1.1 DS Based on Conventional Representation 7 2.1.2 DS Based on Knowledge Representation 8 2.2 The Semantic Web . 9 2.2.1 The Resource Description Framework (RDF) .
    [Show full text]
  • Oracle Database Semantic Technologies Overview
    <Insert Picture Here> Oracle Database Semantic Technologies Overview 1 Oracle Semantic Technologies Agenda • Semantic technologies for the enterprise • Why use Oracle Database as a semantic data store • Customer examples • Features Overview 2 Semantic Technologies for the Enterprise • Designed to represent knowledge in a distributed world • A method to decompose knowledge into small pieces, with rules about the semantics of those pieces • RDF data is self-describing; it “means” something • Allows you to model and integrate DBMS schemas • Allows you to integrate data from different sources without custom programming • Supports decentralized data management • Infer implicit relationships across data This presentation on Oracle Spatial 11g semantic technologies assumes some knowledge of basic principles of semantics. Semantic technologies provide a metadata repository to access other data •Semantics abstracts unstructured content by extracting meaning from entities in underlying data and structuring it so it can be queried in a meaningful way •RDF (Resource Description Framework) is the standard for encoding this metadata into a flexible triples data model (subject- object –predicate) •RDF can also be used to model and integrate relational data •So RDF can be used to describe relationships across a variety of data sources w/o custom programming •RDF allows you to manage metadata models centrally w/o bringing all of the data into one place •RDF Schema & OWL takes an RDF store to the next level with the ability to create inferences or new
    [Show full text]
  • Intelligent Software Agents in Accounting: an Evolving Scenario
    Intelligent Software Agents In Accounting: an evolving scenario MIKLOS A. VASARHELYI AND RANI HOITASH Rutgers University Faculty of Management Executive Summary Intelligent agent technology is one of the fastest growing areas of research and Internet related commercial endeavors. It is , however, an ill defined field, with many overstated claims and few specific areas of applications. Both accounting and finance have great potential as fields of application. However, at this stage there are very few, if any, available applications. Most of the applications are still in the primary research stage. For further development of the field, it is necessary to create an operational definition of the field, understand its extant composition, and to postulate a program of research and application development. Such a theoretical work should be of great value as a foundation for an emerging field. Intelligent Agents today claim some functional "intelligence" where they will perform tasks on the behalf of a user. This paper, explores the spectrum of software agency; from the automated "softbots" that are presently being implemented, to the concepts and projects of the future that are more accurately described as intelligent agents. The operational definition section should provide some assessment of the current state of intelligent agent technology and who some of the key players are, to-date. The analysis focuses on academic and commercial research. The paper describes the basic mechanics for agency and how agent developers are tackling the challenge of intelligent agents within networked computing environments. The state-of-the-art section explores the commercial agent landscape. Commercial efforts are just the beginning of the capabilities and potential of intelligent agents.
    [Show full text]
  • Introduction of Semantic Technology for SAS® Programmers Kevin Lee, Clindata Insight, Moraga, CA
    PharmaSUG 2016 – Paper IB02 Introduction of Semantic Technology for SAS® programmers Kevin Lee, Clindata Insight, Moraga, CA ABSTRACT There is a new technology to express and search the data that can provide more meaning and relationship – semantic technology. The semantic technology can easily add, change and implement the meaning and relationship to the current data. Companies such as Facebook and Google are currently using the semantic technology. For example, Facebook Graph Search use semantic technology to enhance more meaningful search for users. The paper will introduce the basic concepts of semantic technology and its graph data model, Resource Description Framework (RDF). RDF can link data elements in a self-describing way with elements and property: subject, predicate and object. The paper will introduce the application and examples of RDF elements. The paper will also introduce three different representation of RDF: RDF/XML representation, turtle representation and N-triple representation. The paper will also introduce “CDISC standards RDF representation, Reference and Review Guide” published by CDISC and PhUSE CSS. The paper will discuss RDF representation, reference and review guide and show how CDISC standards are represented and displayed in RDF format. The paper will also introduce Simple Protocol RDF Query Language (SPARQL) that can retrieve and manipulate data in RDF format. The paper will show how programmers can use SPARQL to re-represent RDF format of CDISC standards metadata into structured tabular format. Finally, paper will discuss the benefits and futures of semantic technology. The paper will also discuss what semantic technology means to SAS programmers and how programmers take an advantage of this new technology.
    [Show full text]
  • Semantic (Web) Technology in Action: Ontology Driven Information Systems for Search, Integration, and Analysis
    Wright State University CORE Scholar The Ohio Center of Excellence in Knowledge- Kno.e.sis Publications Enabled Computing (Kno.e.sis) 12-2003 Semantic (Web) Technology in Action: Ontology Driven Information Systems for Search, Integration, and Analysis Amit P. Sheth Wright State University - Main Campus, [email protected] Cartic Ramakrishnan Wright State University - Main Campus Follow this and additional works at: https://corescholar.libraries.wright.edu/knoesis Part of the Bioinformatics Commons, Communication Technology and New Media Commons, Databases and Information Systems Commons, OS and Networks Commons, and the Science and Technology Studies Commons Repository Citation Sheth, A. P., & Ramakrishnan, C. (2003). Semantic (Web) Technology in Action: Ontology Driven Information Systems for Search, Integration, and Analysis. IEEE Data Engineering Bulletin, 26 (4), 40-48. https://corescholar.libraries.wright.edu/knoesis/970 This Article is brought to you for free and open access by the The Ohio Center of Excellence in Knowledge-Enabled Computing (Kno.e.sis) at CORE Scholar. It has been accepted for inclusion in Kno.e.sis Publications by an authorized administrator of CORE Scholar. For more information, please contact [email protected]. Slightly abridged version appears in IEEE Data Engineering Bulletin, Special issue on Making the Semantic Web Real, U. Dayal, H. Kuno, and K. Wilkinson, Eds. December 2003. Semantic (Web) Technology In Action: Ontology Driven Information Systems for Search, Integration and Analysis Amit Sheth1,2 and Cartic Ramakrishnan2 1Semagix and 2LSDIS lab, University of Georgia Abstract Semantics is seen as the key ingredient in the next phase of the Web infrastructure as well as the next generation of information systems applications.
    [Show full text]
  • Semantic Web: a Review of the Field Pascal Hitzler [email protected] Kansas State University Manhattan, Kansas, USA
    Semantic Web: A Review Of The Field Pascal Hitzler [email protected] Kansas State University Manhattan, Kansas, USA ABSTRACT which would probably produce a rather different narrative of the We review two decades of Semantic Web research and applica- history and the current state of the art of the field. I therefore do tions, discuss relationships to some other disciplines, and current not strive to achieve the impossible task of presenting something challenges in the field. close to a consensus – such a thing seems still elusive. However I do point out here, and sometimes within the narrative, that there CCS CONCEPTS are a good number of alternative perspectives. The review is also necessarily very selective, because Semantic • Information systems → Graph-based database models; In- Web is a rich field of diverse research and applications, borrowing formation integration; Semantic web description languages; from many disciplines within or adjacent to computer science, Ontologies; • Computing methodologies → Description log- and a brief review like this one cannot possibly be exhaustive or ics; Ontology engineering. give due credit to all important individual contributions. I do hope KEYWORDS that I have captured what many would consider key areas of the Semantic Web field. For the reader interested in obtaining amore Semantic Web, ontology, knowledge graph, linked data detailed overview, I recommend perusing the major publication ACM Reference Format: outlets in the field: The Semantic Web journal,1 the Journal of Pascal Hitzler. 2020. Semantic Web: A Review Of The Field. In Proceedings Web Semantics,2 and the proceedings of the annual International of . ACM, New York, NY, USA, 7 pages.
    [Show full text]