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  1. Haas, M.: Methoden der künstlichen Intelligenz in betriebswirtschaftlichen Anwendungen (2006) 0.03 
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    Content
    Diplomarbeit zur Erlangung des Grades eines Diplom-Wirtschaftsinformatikers (FH) der Hochschule Wismar. Vgl.: http://www.wi.hs-wismar.de/~cleve/vorl/projects/da/DA-FS-Haas.pdf.
  2. Tudhope, D.; Hodge, G.: Terminology registries (2007) 0.02 
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    Abstract
    A discussion on current initiatives regarding terminology registries.
    Date
    26.12.2011 13:22:07
  3. Hauff-Hartig, S.: Wissensrepräsentation durch RDF: Drei angewandte Forschungsbeispiele : Bitte recht vielfältig: Wie Wissensgraphen, Disco und FaBiO Struktur in Mangas und die Humanities bringen (2021) 0.01 
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    Date
    22. 5.2021 12:43:05
    Type
    a
  4. Priss, U.: Faceted knowledge representation (1999) 0.01 
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    Abstract
    Faceted Knowledge Representation provides a formalism for implementing knowledge systems. The basic notions of faceted knowledge representation are "unit", "relation", "facet" and "interpretation". Units are atomic elements and can be abstract elements or refer to external objects in an application. Relations are sequences or matrices of 0 and 1's (binary matrices). Facets are relational structures that combine units and relations. Each facet represents an aspect or viewpoint of a knowledge system. Interpretations are mappings that can be used to translate between different representations. This paper introduces the basic notions of faceted knowledge representation. The formalism is applied here to an abstract modeling of a faceted thesaurus as used in information retrieval.
    Date
    22. 1.2016 17:30:31
    Type
    a
  5. Teutsch, K.: ¬Die Welt ist doch eine Scheibe : Google-Herausforderer eyePlorer (2009) 0.01 
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    Content
    Eine neue visuelle Ordnung Martin Hirsch ist der Enkel des Nobelpreisträgers Werner Heisenberg. Außerdem ist er Hirnforscher und beschäftigt sich seit Jahren mit der Frage: Was tut mein Kopf eigentlich, während ich hirnforsche? Ralf von Grafenstein ist Marketingexperte und spezialisiert auf Dienstleistungen im Internet. Zusammen haben sie also am 1. Dezember 2008 eine Firma in Berlin gegründet, deren Heiliger Gral besagte Scheibe ist, auf der - das ist die Idee - bald die ganze Welt, die Internetwelt zumindest, Platz finden soll. Die Scheibe heißt eyePlorer, was sich als Aufforderung an ihre Nutzer versteht. Die sollen auf einer neuartigen, eben scheibenförmigen Plattform die unermesslichen Datensätze des Internets in eine neue visuelle Ordnung bringen. Der Schlüssel dafür, da waren sich Hirsch und von Grafenstein sicher, liegt in der Hirnforschung, denn warum nicht die assoziativen Fähigkeiten des Menschen auf Suchmaschinen übertragen? Anbieter wie Google lassen von solchen Ansätzen bislang die Finger. Hier setzt man dafür auf Volltextprogramme, also sprachbegabte Systeme, die letztlich aber, genau wie die Schlagwortsuche, nur zu opak gerankten Linksammlungen führen. Weiter als auf Seite zwei des Suchergebnisses wagt sich der träge Nutzer meistens nicht vor. Weil sie niemals wahrgenommen wird, fällt eine Menge möglicherweise kostbare Information unter den Tisch.
    Einstein, Weizsäcker und Hitler Zu Demonstrationszwecken wird die eyePlorer-Scheibe an die Wand projiziert. Gibt man im kleinen Suchfeld in der Mitte den Namen Werner Heisenberg ein, verwandelt sich die Scheibe in einen Tortenboden. Die einzelnen Stücke entsprechen Kategorien wie "Person", "Technologie" oder "Organisation". Sie selbst sind mit bunten Knöpfen bedeckt, unter denen sich die Informationen verbergen. So kommt es, dass man beim Thema Heisenberg nicht nur auf die Kollegen Einstein, Weizsäcker und Schrödinger trifft, sondern auch auf Adolf Hitler. Ein Klick auf den entsprechenden Button stellt unter anderem heraus: Heisenberg kam 1933 unter Beschuss der SS, weil er sich nicht vor den Karren einer antisemitischen Physikbewegung spannen ließ. Nach diesem Prinzip spült die frei assoziierende Maschine vollautomatisch immer wieder neue Fakten an, um die der Nutzer zwar nicht gebeten hat, die ihn bei seiner Recherche aber möglicherweise unterstützen und die er später - die Maschine ist noch ausbaubedürftig - auch modellieren darf. Aber will man das, sich von einer Maschine beraten lassen? "Google ist wie ein Zoo", sekundiert Ralf von Grafenstein. "In einem Gehege steht eine Giraffe, im anderen ein Raubtier, aber die sind klar getrennt voneinander durch Gitter und Wege. Es gibt keine Möglichkeit, sie zusammen anzuschauen. Da kommen wir ins Spiel. Wir können Äpfel mit Birnen vergleichen!" Die Welt ist eine Scheibe oder die Scheibe eben eine Welt, auf der vieles mit vielem zusammenhängt und manches auch mit nichts. Der Vorteil dieser Maschine ist, dass sie in Zukunft Sinn stiften könnte, wo andere nur spröde auf Quellen verweisen. "Google ist ja ein unheimlich heterogenes Erlebnis mit ständigen Wartezeiten und Mausklicks dazwischen. Das kostet mich viel zu viel Metagedankenkraft", sagt Hirsch. "Wir wollten eine Maschine mit einer ästhetisch ansprechenden Umgebung bauen, aus der ich mich kaum wegbewege, denn sie liefert mir Informationen in meinen Gedanken hinein."
    Wenn die Maschine denkt Zur Hybris des Projekts passt, dass der eyePlorer ursprünglich HAL heißen sollte - wie der außer Rand und Band geratene Bordcomputer aus Kubricks "2001: Odyssee im Weltraum". Wenn man die Buchstaben aber jeweils um eine Alphabetposition nach rechts verrückt, ergibt sich IBM. Was passiert mit unserem Wissen, wenn die Maschine selbst anfängt zu denken? Ralf von Grafenstein macht ein ernstes Gesicht. "Es ist nicht unser Ansinnen, sie alleinzulassen. Es geht bei uns ja nicht nur darum, zu finden, sondern auch mitzumachen. Die Community ist wichtig. Der Dialog ist beiderseitig." Der Lotse soll in Form einer wachsamen Gemeinschaft also an Bord bleiben. Begünstigt wird diese Annahme auch durch die aufkommenden Anfasstechnologien, mit denen das iPhone derzeit so erfolgreich ist: "Allein zehn Prozent der menschlichen Gehirnleistung gehen auf den Pinzettengriff zurück." Martin Hirsch wundert sich, dass diese Erkenntnis von der IT-Branche erst jetzt berücksichtigt wird. Auf berührungssensiblen Bildschirmen sollen die Nutzer mit wenigen Handgriffen bald spielerisch Inhalte schaffen und dem System zur Verfügung stellen. So wird aus der Suchmaschine ein "Sparringspartner" und aus einem Informationsknopf ein "Knowledge Nugget". Wie auch immer man die Erkenntniszutaten des Internetgroßmarkts serviert: Wissen als Zeitwort ist ein länglicher Prozess. Im Moment sei die Maschine noch auf dem Stand eines Zweijährigen, sagen ihre Schöpfer. Sozialisiert werden soll sie demnächst im Internet, ihre Erziehung erfolgt dann durch die Nutzer. Als er Martin Hirsch mit seiner Scheibe zum ersten Mal gesehen habe, dachte Ralf von Grafenstein: "Das ist überfällig! Das wird kommen! Das muss raus!" Jetzt ist es da, klein, unschuldig und unscheinbar. Man findet es bei Google."
  6. Priss, U.: Description logic and faceted knowledge representation (1999) 0.01 
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    Abstract
    The term "facet" was introduced into the field of library classification systems by Ranganathan in the 1930's [Ranganathan, 1962]. A facet is a viewpoint or aspect. In contrast to traditional classification systems, faceted systems are modular in that a domain is analyzed in terms of baseline facets which are then synthesized. In this paper, the term "facet" is used in a broader meaning. Facets can describe different aspects on the same level of abstraction or the same aspect on different levels of abstraction. The notion of facets is related to database views, multicontexts and conceptual scaling in formal concept analysis [Ganter and Wille, 1999], polymorphism in object-oriented design, aspect-oriented programming, views and contexts in description logic and semantic networks. This paper presents a definition of facets in terms of faceted knowledge representation that incorporates the traditional narrower notion of facets and potentially facilitates translation between different knowledge representation formalisms. A goal of this approach is a modular, machine-aided knowledge base design mechanism. A possible application is faceted thesaurus construction for information retrieval and data mining. Reasoning complexity depends on the size of the modules (facets). A more general analysis of complexity will be left for future research.
    Date
    22. 1.2016 17:30:31
    Type
    a
  7. Beppler, F.D.; Fonseca, F.T.; Pacheco, R.C.S.: Hermeneus: an architecture for an ontology-enabled information retrieval (2008) 0.01 
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    Abstract
    Ontologies improve IR systems regarding its retrieval and presentation of information, which make the task of finding information more effective, efficient, and interactive. In this paper we argue that ontologies also greatly improve the engineering of such systems. We created a framework that uses ontology to drive the process of engineering an IR system. We developed a prototype that shows how a domain specialist without knowledge in the IR field can build an IR system with interactive components. The resulting system provides support for users not only to find their information needs but also to extend their state of knowledge. This way, our approach to ontology-enabled information retrieval addresses both the engineering aspect described here and also the usability aspect described elsewhere.
    Date
    28.11.2016 12:43:22
    Type
    a
  8. Definition of the CIDOC Conceptual Reference Model (2003) 0.01 
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    Abstract
    This document is the formal definition of the CIDOC Conceptual Reference Model ("CRM"), a formal ontology intended to facilitate the integration, mediation and interchange of heterogeneous cultural heritage information. The CRM is the culmination of more than a decade of standards development work by the International Committee for Documentation (CIDOC) of the International Council of Museums (ICOM). Work on the CRM itself began in 1996 under the auspices of the ICOM-CIDOC Documentation Standards Working Group. Since 2000, development of the CRM has been officially delegated by ICOM-CIDOC to the CIDOC CRM Special Interest Group, which collaborates with the ISO working group ISO/TC46/SC4/WG9 to bring the CRM to the form and status of an International Standard.
    Date
    6. 8.2010 14:22:28
  9. Hollink, L.; Assem, M. van: Estimating the relevance of search results in the Culture-Web : a study of semantic distance measures (2010) 0.01 
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    Abstract
    More and more cultural heritage institutions publish their collections, vocabularies and metadata on the Web. The resulting Web of linked cultural data opens up exciting new possibilities for searching and browsing through these cultural heritage collections. We report on ongoing work in which we investigate the estimation of relevance in this Web of Culture. We study existing measures of semantic distance and how they apply to two use cases. The use cases relate to the structured, multilingual and multimodal nature of the Culture Web. We distinguish between measures using the Web, such as Google distance and PMI, and measures using the Linked Data Web, i.e. the semantic structure of metadata vocabularies. We perform a small study in which we compare these semantic distance measures to human judgements of relevance. Although it is too early to draw any definitive conclusions, the study provides new insights into the applicability of semantic distance measures to the Web of Culture, and clear starting points for further research.
    Date
    26.12.2011 13:40:22
  10. Bittner, T.; Donnelly, M.; Winter, S.: Ontology and semantic interoperability (2006) 0.01 
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    Date
    3.12.2016 18:39:22
    Type
    a
  11. Monireh, E.; Sarker, M.K.; Bianchi, F.; Hitzler, P.; Doran, D.; Xie, N.: Reasoning over RDF knowledge bases using deep learning (2018) 0.01 
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    Abstract
    Semantic Web knowledge representation standards, and in particular RDF and OWL, often come endowed with a formal semantics which is considered to be of fundamental importance for the field. Reasoning, i.e., the drawing of logical inferences from knowledge expressed in such standards, is traditionally based on logical deductive methods and algorithms which can be proven to be sound and complete and terminating, i.e. correct in a very strong sense. For various reasons, though, in particular the scalability issues arising from the ever increasing amounts of Semantic Web data available and the inability of deductive algorithms to deal with noise in the data, it has been argued that alternative means of reasoning should be investigated which bear high promise for high scalability and better robustness. From this perspective, deductive algorithms can be considered the gold standard regarding correctness against which alternative methods need to be tested. In this paper, we show that it is possible to train a Deep Learning system on RDF knowledge graphs, such that it is able to perform reasoning over new RDF knowledge graphs, with high precision and recall compared to the deductive gold standard.
    Date
    16.11.2018 14:22:01
    Type
    a
  12. Drewer, P.; Massion, F; Pulitano, D: Was haben Wissensmodellierung, Wissensstrukturierung, künstliche Intelligenz und Terminologie miteinander zu tun? (2017) 0.01 
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    Date
    13.12.2017 14:17:22
  13. OWL Web Ontology Language Test Cases (2004) 0.01 
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    Date
    14. 8.2011 13:33:22
  14. Knorz, G.; Rein, B.: Semantische Suche in einer Hochschulontologie : Ontologie-basiertes Information-Filtering und -Retrieval mit relationalen Datenbanken (2005) 0.01 
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    11. 2.2011 18:22:25
  15. Mayfield, J.; Finin, T.: Information retrieval on the Semantic Web : integrating inference and retrieval 0.01 
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    12. 2.2011 17:35:22
  16. Kottmann, N.; Studer, T.: Improving semantic query answering (2006) 0.00 
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    Abstract
    The retrieval problem is one of the main reasoning tasks for knowledge base systems. Given a knowledge base K and a concept C, the retrieval problem consists of finding all individuals a for which K logically entails C(a). We present an approach to answer retrieval queries over (a restriction of) OWL ontologies. Our solution is based on reducing the retrieval problem to a problem of evaluating an SQL query over a database constructed from the original knowledge base. We provide complete answers to retrieval problems. Still, our system performs very well as is shown by a standard benchmark.
  17. Pankowski, T.: Ontological databases with faceted queries (2022) 0.00 
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    Abstract
    The success of the use of ontology-based systems depends on efficient and user-friendly methods of formulating queries against the ontology. We propose a method to query a class of ontologies, called facet ontologies ( fac-ontologies ), using a faceted human-oriented approach. A fac-ontology has two important features: (a) a hierarchical view of it can be defined as a nested facet over this ontology and the view can be used as a faceted interface to create queries and to explore the ontology; (b) the ontology can be converted into an ontological database , the ABox of which is stored in a database, and the faceted queries are evaluated against this database. We show that the proposed faceted interface makes it possible to formulate queries that are semantically equivalent to $${\mathcal {SROIQ}}^{Fac}$$ SROIQ Fac , a limited version of the $${\mathcal {SROIQ}}$$ SROIQ description logic. The TBox of a fac-ontology is divided into a set of rules defining intensional predicates and a set of constraint rules to be satisfied by the database. We identify a class of so-called reflexive weak cycles in a set of constraint rules and propose a method to deal with them in the chase procedure. The considerations are illustrated with solutions implemented in the DAFO system ( data access based on faceted queries over ontologies ).
    Type
    a
  18. Broughton, V.: Facet analysis as a fundamental theory for structuring subject organization tools (2007) 0.00 
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    Abstract
    The presentation will examine the potential of facet analysis as a basis for determining status and relationships of concepts in subject based tools using a controlled vocabulary, and the extent to which it can be used as a general theory of knowledge organization as opposed to a methodology for structuring classifications only.
  19. Hesse, W.; Verrijn-Stuart, A.: Towards a theory of information systems : the FRISCO approach (1999) 0.00 
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    Abstract
    Information Systems (IS) is among the most widespread terms in the Computer Science field but a well founded, widely accepted theory of IS is still missing. With the Internet publication of the FRISCO report, the IFIP task group "FRamework of Information System COncepts" has taken a first step towards such a theory. Among the major achievements of this report are: (1) it builds on a solid basis formed by semiotics and ontology, (2) it defines a compendium of about 100 core IS concepts in a coherent and consistent way, (3) it goes beyond the common narrow view of information systems as pure technical artefacts by adopting an interdisciplinary, socio-technical view on them. In the autumn of 1999, a first review of the report and its impact was undertaken at the ISCO-4 conference in Leiden. In a workshop specifically devoted to the subject, the original aims and goals of FRISCO were confirmed to be still valid and the overall approach and achievements of the report were acknowledged. On the other hand, the workshop revealed some misconceptions, errors and weaknesses of the report in its present form, which are to be removed through a comprehensive revision now under way. This paper reports on the results of the Leiden conference and the current revision activities. It also points out some important consequences of the FRISCO approach as a whole.
  20. Prieto-Díaz, R.: ¬A faceted approach to building ontologies (2002) 0.00 
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    Abstract
    An ontology is "an explicit conceptualization of a domain of discourse, and thus provides a shared and common understanding of the domain." We have been producing ontologies for millennia to understand and explain our rationale and environment. From Plato's philosophical framework to modern day classification systems, ontologies are, in most cases, the product of extensive analysis and categorization. Only recently has the process of building ontologies become a research topic of interest. Today, ontologies are built very much ad-hoc. A terminology is first developed providing a controlled vocabulary for the subject area or domain of interest, then it is organized into a taxonomy where key concepts are identified, and finally these concepts are defined and related to create an ontology. The intent of this paper is to show that domain analysis methods can be used for building ontologies. Domain analysis aims at generic models that represent groups of similar systems within an application domain. In this sense, it deals with categorization of common objects and operations, with clear, unambiguous definitions of them and with defining their relationships.
    Type
    a

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