Ontology Markup for Web Forms Generation
|
|
- Mervin Parker
- 5 years ago
- Views:
Transcription
1 Ontology Markup for Web Forms Generation Marlon Dumas 1 Lachlan Aldred 1 Mitra Heravizadeh 1,2 Arthur H.M. ter Hofstede 1 1 Centre for Information Technology Innovation 2 GBST Holdings Pty Ltd Queensland University of Technology PO Box 1511 GPO Box 2434, Brisbane QLD 4001, Australia Milton QLD 4064, Australia {m.dumas, l.aldred, a.terhofstede}@qut.edu.au mitra.heravizadeh@gbst.com Abstract Ontologies are promising to become the keystones of the Semantic Web. The realisation of this promise requires that adequate approaches to model, represent, and mark up ontologies on the Web are developed. This paper presents an approach to model ontologies as populated conceptual schemas and to implement them using XML tools and relational databases. The issue of marking up these ontologies with presentation information is then addressed. The approach is applied to the generation of Web forms from marked up ontologies, in such a way that the generated forms encode the constraints expressed in the ontology. 1 Introduction The explosive development of the Web has brought forward the need for machine processable representations of semantically rich information: a vision at the heart of the Semantic Web [1]. At present, the concept of ontology is being put forward as a potential enabler of this vision [9]. In a nutshell, an ontology is a shared understanding of a domain that can be communicated between people and application systems [5]. Ontologies provide a consensual conceptualisation through which heterogeneous programs can communicate with each other and with their users. Potential applications of ontologies are site organisation and browsing support, heterogeneous data sources integration, products and services advertisement, etc. Since ontologies are intended (among other things) to support the communication between application programs and their users, there needs to be a way to mark them up with presentation information. In particular, it should be possible to generate from a given ontology with presentation markups, user interfaces supporting tasks such as designating or displaying one or several elements of this ontology. This paper describes an approach to model and represent marked up ontologies in a way that enables their processing through mature and scalable technologies. We specifically study the case where ontologies are marked up to generate Web forms allowing users to designate a specific term of an ontology (e.g., designating a car in a car ontology, or a wine in a wine ontology). In the proposed approach, an ontology is modelled as a populated conceptual schema and implemented as an XML schema and a relational database. Eventually, this populated WWW 02 Workshop on Real-World Applications of RDF and the Semantic Web 1
2 conceptual schema is marked up by a forms developer and the resulting marked up ontology is translated into presentation annotations over the underlying XML schema. These annotations are used to generate the XHTML/Javascript code for the form, that is ultimately displayed by a browser. The data entered by the user in the form is then parsed by a servlet which creates an XML document conforming to the XML schema of the marked up ontology. In the rest of the paper, we successively introduce our approach to model and mark up ontologies for generating Web forms (Sections 2 and 3), and we describe an ongoing implementation (Section 4). This description is followed by a review of related work (Section 5) and concluding remarks (Section 6). 2 Modelling ontologies An ontology can be seen as a set of terms, relationships between these terms, and rules governing these relationships. For tasks that do not involve complex reasoning, the rules captured in an ontology can be limited to those found in conceptual modelling languages such as UML (Unified Modelling Language) [10] and ORM (Object-Role Modelling) [7]. In order to ground the discussion, we make use of ORM, although the results can certainly be adapted to UML. For the purposes of this paper, the basic concepts of ORM are those of entity, role and fact. Entities are the things being modelled (e.g., the terms of an ontology), while facts are the statements that are made about entities (e.g., their relationships). A fact relates two or more entities, each of them playing a different role in the fact. Entities are grouped into entity types (graphically denoted by circles) and facts are grouped into fact types made up of one or more roles (denoted by rectangles with a line pointing to an entity type). The special fact type Subtyping, denoted by a directed arrow, captures a notion of inheritance. If an entity type ET1 is a subtype of an entity type ET2, then any entity of ET1 is also an entity of ET2, and consequently, all the fact types involving ET2 also involve ET1. If ET1 is a subtype of ET2, ET1 will typically have a subtyping condition expressed in terms of the fact types in which ET2 is involved. All the instances of ET2 which satisfy this condition are in ET1. An example ORM diagram is given in Figure 1. This diagram involves the entity types Item, Car, Book, Model, Make, and Year, where Car and Book are subtypes of Item. In addition to these entity types, the diagram features 5 fact types: ItemCategory, CarYear, CarMake, CarModel, and MakeModel. ItemCategory is used to define the subtyping conditions (see bottom left corner of the diagram). For example, a car is an item related to the category Car through ItemCategory. The fact types CarYear, CarMake, and CarModel capture the relationship between cars on the one hand, and makes, models, and years on the other. The fact type MakeModel captures the relationship between models and makes. The following types of constraints (among others) can be captured in an ORM diagram: Role optionality: states whether all the entities of a type must take a given role or not. Mandatory roles are denoted by filled circles at the extremities of the role. In Figure 1, the roles is of, has model, has make and has year are mandatory. WWW 02 Workshop on Real-World Applications of RDF and the Semantic Web 2
3 Item is of includes Category ItemCategory CarYear u CarModel Year (AD) Car is year of has year has model (ID) is model of Model (Name) Book (ISBN) { } CarMake has make is make of makes is made by MakeModel Subtyping conditions Car is Item is of Category "Car" Book is Item is of Category "Book" Make (Name) Populations (non exhaustive list) population(carmodel) = { (C1, Civic), (C2, Legend), (C3, Falcon), (C4, Mustang),... } population(makemodel) = { (Honda, Civic), (Honda, Legend), (Ford, Falcon), (Ford, Mustang),... } Figure 1: Fragment of a populated ORM schema modelling an ontology. Role disjointness: states that an entity of a type can play only one among a set of roles Role uniqueness: states whether a binary fact type captures a 1:1, 1:N or N:M relationship. In Figure 1, the fact type CarModel has a uniqueness constraint on the role has model (indicated by a double arrow on top of the role) meaning that a car has only one model. There is no uniqueness constraint on the role is model of, so several cars can have the same model. Hence, the fact type CarModel captures a 1:N relationship. The full definition of role uniqueness takes into account higher arity fact types. External uniqueness constraint: specifies uniqueness constraints over combinations of roles belonging to different fact types. In the example of Figure 1, there is an external uniqueness constraint involving the roles is year of and is model of. This means that a type of car is uniquely identifier by a year and a model (e.g., Mustang 1988). Value constraint: enumerates in extension or through ranges, the values that a value type can take. In Figure 1 for example, there is a value constraint over the type AD stating that the ontology only deals with years greater than or equal to When describing ontologies, it is important to consider not only the structure of the Universe of Discourse (its schema) but also its population. Thus, in order to capture an ontology, a conceptual schema must be accompanied by a population, leading to what we call a populated conceptual schema. The population of an ORM schema is composed of the populations of the fact types of the schema. The population of a fact type is simply a set of WWW 02 Workshop on Real-World Applications of RDF and the Semantic Web 3
4 tuples. For example, the population of the fact type MakeModel is a set of pairs composed of a name of a model and a name of a make as exemplified at the bottom of Figure 1. For further details about the ORM notation, the reader is referred to [7]. 3 Marking up ontologies Once an ontology has been specified as a populated conceptual schema, it can be marked up with presentation information. Below, we sketch a method for marking up ontologies in order to generate Web forms for designating an entity O of a given type T within an ontology. In the context of the car ontology (Figure 1), this form would allow a user to designate a particular type of car (e.g., Honda Legend 1986 ) by specifying a make, a model, and a year. To simplify the discussion, we assume that all fact types are binary. The extension to ternary and higher arity fact types is straightforward. The marking up process works as follows. First, a subset of the fact types in which T participates are selected as those for which an input element should be created in the form. These fact types must be chosen in such a way that specifying the entity to which entity O is related through each of these fact types leads to an unambiguous designation of O. In the context of designating a car in the ontology of Figure 1, the fact types CarModel and CarYear are sufficient to perform this designation. Indeed, as discussed in the previous section, the presence of the external uniqueness constraint over the roles is year of and is model of entails that there is no need to take into account the fact type CarMake when designating a car. Still, the developer may decide to include the fact type CarMake in the form, with the idea that this might help the user in finding the model of the car to be designated. In the second step of the marking up process, the following items are specified for each of the fact types selected in the first step: 1. The role played by the designated object O is this fact type. 2. The external name (as it is to appear in the form) of the above role. For example, the external name for the role has make can be Make. 3. A documentation of the fact type s meaning, to be included or linked to the form. For example the documentation of the fact type CarMake could be: The make of a car is the brand given to it by its manufacturer. Examples of makes are Honda and Ford. 4. Whether the entity O to which O is related through this fact type is to be designated: By an identifier (e.g., an agreed-upon label or an URI) By means of a sub-form These two cases are respectively called designation by identifier and designation by sub-form. In the example of Figure 1, the developer will certainly use designation by identifier for all the 3 fact types involving the type Car, since there is no other means of designating a make, a model or a year, other than by an identifier (at least according to the WWW 02 Workshop on Real-World Applications of RDF and the Semantic Web 4
5 ontology s schema). Sub-forms would be useful for example in the case of an ontology involving a type Hotel related to a type Address. In this setting, a form for designating an object of the type Hotel is likely to involve a sub-form for designating the address of the hotel. This sub-form would contain an input element for each of the fact types involving the type Address (e.g., street number, street name, city). 5. In the case of designation by identifier, the type of graphical element used to enter the identifier (text field, pull-down menu, check-box, slider, etc.). In the working example, the fact types CarModel and CarMake can be marked up so that a pull-down menu is used to represent them, while the fact type CarYear can be marked up as a text field. For each fact type designated by sub-form, the marking up process is recursively repeated, this time taking as starting point the entity type that plays the inverse of the role specified in item 1 above. In the working example (i.e., designating a car), no designation by sub-form occurs, so the process is stopped at this point. In the example of marking up an ontology to designate a hotel, the marking up process would need to be repeated taking as starting point the type Address (to which the entity type Hotel is related). The role optionality, role disjointness, and role uniqueness information contained in the ORM schema of an ontology are exploited during the form generation. Role optionality is used to determine whether an input element in a form is optional or not. Role disjointness is used to detect that if the user fills in an input element, (s)he does not need to fill another one anymore. Finally, role uniqueness is used to determine whether the term appearing in an input element of a form fully or partially determines the term(s) that must appear in another element. With respect to the example of Figure 1, if the ontology is used to generate a form to designate a type of car, and at some point the user selects a given make of car, only the car models of the selected make will appear as options in the pull-down menu for the CarModel fact type. This is because the fact type MakeModel captures a 1:N relationship. Similarly, the form will detect that once an input element corresponding to a type T is filled in, all other input elements corresponding to entity types that are in a 1:1 relationship with T through a fact type, can be automatically filled in. Following this principle of exploiting the ontology information during the form generation, the value constraints appearing in the ORM schema are translated into range checks in the code of the generated form. In the working example, a range check will be performed by the form to ensure that the value entered for the year is greater than or equal to The population of the ontology is also exploited during the form generation. Specifically, if a fact type is mapped into a pull-down menu, the options that appear in this menu are derived from the population of one of the roles in this fact type. With respect to the example of Figure 1, if the population of the role is make of is { Honda, Ford, Toyota }, the pulldown menu for the make of a car will contain these values as its options. Similarly, in the case of a text field, the population of the corresponding role is used to check that the term entered by the user in this field actually exists in the ontology. WWW 02 Workshop on Real-World Applications of RDF and the Semantic Web 5
6 4 Implementation aspects An overview of the ontology-based forms generation system is given in Figure 2. In this figure, the circles denote system modules, the boxes denote inputs and outputs of these modules, and the dashed lines denote interactions between the system and the users. Ontology Ontology Editor Ontology Developer Ontology schema (Serialized ORM) Ontology population (relational database) Forms Developer Ontology Marker Document schema (XML schema) Presentation markups (XML) XSLT XSLT JDBC Legend instance of Form implementation (XHTML + Javascript) Forms Generator System module Output Web Server XML Document Forms Processing Servlet Input End User User interaction External module Browser Figure 2: Ontology-based forms generation system. The process begins when the ontology developer(s) interact(s) with the ontology editor to design an ontology 1. The ontology editor is essentially an ORM diagram drawing tool in which populations can be specified by attaching textual annotations to the entity and fact types 2. The output of the ontology edition is an ontology schema, specified using a serialised syntax of ORM (e.g., based on XML), and a relational database. There are well-known techniques to map ORM diagrams into relational tables [7]. 1 Alternatively, the ontology could be obtained through a translation from another ontology language. 2 The populations can be stored in external files or databases and linked to the diagram through a reference. WWW 02 Workshop on Real-World Applications of RDF and the Semantic Web 6
7 At this point of the process, a developer of a Web page can import the ontology schema into the Ontology Marker in order to specify the structure of a form by specifying presentation markup information. The output of this marking up process is an XML schema containing the marked up portion of the ontology s schema, and an XML document containing the presentation markup information. This markup information describes how the elements of the schema are mapped into elements of the form, how the elements of the form are constrained and inter-related, and how the contents of the form elements (e.g., the options of the pull-down menus) are extracted from the ontology population. The presentation markup information is then given as input to the Forms Generator module, which uses this together with the schema population contained in the database, to produce an XHTML <form> element with embedded Javascript code. Javascript is used to code the variable parts of the pages, such as the correlated pull-down menus. In the example discussed in Section 3, the pull-down menus for the make and the model will be correlated since there exists a relationship between these two items. As a result, each time that the user will select a given make of car, the pull-down menu corresponding to the model will be updated so that it displays only the models of that particular make. The form element generated in the previous process can be included within the body of an XHTML page. Eventually, this XHTML page is served to an end user who fills up the form. The data entered by the user is then submitted to the Forms Processing Servlet, which formats these data as an XML document. By construction, this XML document will be conform to (i.e., will be an instance of) the XML schema generated by the Ontology Marker. This XML document can then be used by any application program. A developer can mark up the same ontology in different ways. The resulting marked up ontologies are then processed separately by the Forms Generator, leading to different XHTML form elements. As an example, we consider the case of an ontology capturing a set of product categories, where each category is modelled as a type within a type hierarchy: the type Item appearing at the top of the hierarchy, and the most specific product types such as Car, Mobile Phone, or PDA appearing as leaves. Each type inherits the roles of its super-type(s) while having its own roles (e.g., Mobile Phone will inherit the role has price from Item while having its own roles such as has model and has network ). Given this ontology, a forms developer can generate a separate Web form for each of the leaf categories (e.g., one for Mobile Phone, one for PDA, etc.), so that an end user looking for to designate a mobile phone will get a different form than another user looking to designate a PDA. At present, we have a working prototype that implements the Forms Generator and the Forms Processing Servlet. The presentation markup information and the schema population are assumed to be given. The forms generator uses XSLT to process the presentation markup information and JDBC to access the database containing the population. 5 Related work The use of Knowledge Representation (KR) languages to describe ontologies has recently gained a considerable momentum, as evidenced by the ongoing activity of the W3C Web Ontology Working Group [14]. KR languages such as DAML+OIL [4] are indeed powerful WWW 02 Workshop on Real-World Applications of RDF and the Semantic Web 7
8 enough to capture structural constraints (classes, properties), formal is-a relationships, value restrictions, and general logical constraints, everything within a unified framework. General logical constraints can be exploited by intelligent agents in order to reason about the elements of the ontology. While acknowledging their advantages, we believe that in the near future, the use of KR languages to describe ontologies at the implementation level is not likely to lead to mature and scalable solutions. Indeed, KR languages rely on inference engines that are either in a prototype stage, or do not yet scale well to large amounts of facts and rules. This is true even for inference engines supporting tractable KR languages such as those based on Description Logics [6], especially when the size of the ontology is such that secondary memory processing techniques are required. Hence, alternative approaches to ontology modelling and representation need to be explored in order to enable the first stages of the Semantic Web. In these early stages, ontologies will not necessarily be used to conduct complex reasoning, but rather as a means to enable meaningful communication between humans and heterogeneous application programs. The need to explore means for representing ontologies at the implementation level has been indirectly brought forward in [8]. The authors present a method for mapping the schema part of an ontology expressed in OIL [2] into an XML schema. Each instance of the ontology can then be represented as an XML document conforming to this XML schema. This differs from our approach where the set of instances of the ontology are stored in a relational database. Our proposal also differs from the above, in that it addresses the issue of marking up ontologies with information that allows their processing for a specific purpose (the generation of Web forms). A methodology for modeling Web pages using the UML is proposed in [3]. Specifically, the author describes a collection of UML stereotypes and icons for modelling Web forms. These include a <<Form>> stereotype to declare that a class models a form, a <<Submit>> stereotype to declare that a given method will process the submission of a form, and other presentation stereotypes such as <<Text>>, <<Checkbox>>, etc. This approach differs from ours in that the generation of forms only takes into account the schema, but not a given population. So for example the forms generated using the above approach will not contain information about the instances of the schema types, such as the models and makes of cars in the example of Figure 1. The Schema Adjunct Framework (SAF) [11] is an XML-based language used to associate domain-specific data with XML schemas (or DTDs) and their instances. In a nutshell, a schema adjunct is a set of of data items associated with the elements of an XML schema that describe a particular kind of processing. Schema adjuncts can be used for example to describe a mapping of an XML schema into a relational database, or a transformation method from an XML schema into a Web form. In particular, the presentation markups generated by the Ontology Marker in our architecture (see Figure 2) could very well be represented as a schema adjunct. The SAF API could then be used to process these markups. XForms [13] is an XML-based framework for specifying Web forms in a way that separates the purpose from the presentation. The framework includes a language for specifying deviceindependent forms, a language for representing the data gathered from a form, and a protocol WWW 02 Workshop on Real-World Applications of RDF and the Semantic Web 8
9 for submitting these data. With respect to our proposal, XForms can be seen as an attractive alternative to XHTML-based forms as the target language of the form generation. 6 Conclusion The main contributions of this paper are: An approach to model ontologies as populated conceptual schemas. An implementation of this approach using XML tools and relational databases. An approach to mark up ontologies for the purpose of generating Web forms that encode the constraints captured by the ontology. Regarding this last item, a direction for further work is to explore other applications of ontology markup, including the generation of structured and comparative search interfaces. Another direction for future work is to study the synergies between ontology modelling and representation approaches based on populated conceptual models, and approaches based on KR languages. In particular, the development of mappings from ORM and UML diagrams to KR languages such as DAML+OIL, and vice-versa, could lead to a seamless approach to ontology-based systems development, whereby populated conceptual models are used for simple tasks, whereas full-fledged ontology descriptions with general logical rules are used for tasks requiring inference capabilities. Finally, the possibility of using RDF(S) [12] and its associated technologies as an implementation platform in lieu of XML tools and relational databases as we currently do, is also worth consideration. The use of RDF(S) would probably simplify the current implementation by providing a higher-level model for handling the complex relationships captured by a marked up ontology. Acknowledgment This work was supported by the Australian Research Council SPIRT Grant Self-describing transactions operating in a large, open, heterogeneous, distributed environment involving QUT, UNSW and GBST Holdings Pty Ltd. References [1] T. Berners-Lee, J. Hendler, and O. Lassila. The Semantic Web. Scientific American, May [2] J. Broekstra, M. Klein, S. Decker, D. Fensel, F. van Harmelen, and I. Horrocks. Enabling knowledge representation on the Web by extending RDF schema. In Proceedings of the 10th International World Wide Web Conference (WWW10), Hong Kong, China, May [3] J. Conallen. Modeling Web applications architecture with UML. Communications of the ACM, 42(10), October WWW 02 Workshop on Real-World Applications of RDF and the Semantic Web 9
10 [4] D. Connolly, F. van Harmelen, I. Horrocks, D.L. McGuinness, P.F. Patel-Schneider, and L.A. Stein. DAML+OIL (March 2001) Reference Description. Note, W3C Consortium, December Accessed from on 11 March [5] D. Fensel. Ontologies: Silver Bullet for Knowledge Management and Electronic Commerce. Springer Verlag, [6] J. Gonzalez-Castillo, D. Trastour, and C. Bartolini. Description logics for matchmaking services. In Workshop on Applications of Description Logics, Vienna, Austria, September Accessed from html on 8 March [7] T. Halpin. Information Modeling and Relational Databases. Morgan Kaufmann, [8] M. Klein, D. Fensel, F. van Harmelen, and I. Horrocks. The relation between ontologies and XML schemas. Electronic Transactions on Artificial Intelligence (ETAI), 6, To appear. [9] D. McGuinness. Ontologies come of age. In D. Fensel, J. Hendler, H. Lieberman, and W. Wahlster, editors, The Semantic Web: Why, What, How. MIT Press, [10] J. Rumbaugh, I. Jacobson, and G. Booch. The Unified Modeling Language Reference Manual. Addison-Wesley, [11] S. Vorthmann, J. Robie, and L. Buck. The Schema Adjunct Framework. Note, W3C Consortium, July Accessed from on 12 March [12] W3C Consortium. Resource Description Framework (RDF), w3.org/rdf/. [13] W3C Consortium. XForms The next generation of Web forms, http: // [14] W3C Consortium. Web ontology working group, WebOnt. WWW 02 Workshop on Real-World Applications of RDF and the Semantic Web 10
Racer: An OWL Reasoning Agent for the Semantic Web
Racer: An OWL Reasoning Agent for the Semantic Web Volker Haarslev and Ralf Möller Concordia University, Montreal, Canada (haarslev@cs.concordia.ca) University of Applied Sciences, Wedel, Germany (rmoeller@fh-wedel.de)
More informationLecture Telecooperation. D. Fensel Leopold-Franzens- Universität Innsbruck
Lecture Telecooperation D. Fensel Leopold-Franzens- Universität Innsbruck First Lecture: Introduction: Semantic Web & Ontology Introduction Semantic Web and Ontology Part I Introduction into the subject
More informationKnowledge Representation, Ontologies, and the Semantic Web
Knowledge Representation, Ontologies, and the Semantic Web Evimaria Terzi 1, Athena Vakali 1, and Mohand-Saïd Hacid 2 1 Informatics Dpt., Aristotle University, 54006 Thessaloniki, Greece evimaria,avakali@csd.auth.gr
More informationDevelopment of an Ontology-Based Portal for Digital Archive Services
Development of an Ontology-Based Portal for Digital Archive Services Ching-Long Yeh Department of Computer Science and Engineering Tatung University 40 Chungshan N. Rd. 3rd Sec. Taipei, 104, Taiwan chingyeh@cse.ttu.edu.tw
More informationOWL Rules, OK? Ian Horrocks Network Inference Carlsbad, CA, USA
OWL Rules, OK? Ian Horrocks Network Inference Carlsbad, CA, USA ian.horrocks@networkinference.com Abstract Although the OWL Web Ontology Language adds considerable expressive power to the Semantic Web
More informationNew Tools for the Semantic Web
New Tools for the Semantic Web Jennifer Golbeck 1, Michael Grove 1, Bijan Parsia 1, Adtiya Kalyanpur 1, and James Hendler 1 1 Maryland Information and Network Dynamics Laboratory University of Maryland,
More informationAdding formal semantics to the Web
Adding formal semantics to the Web building on top of RDF Schema Jeen Broekstra On-To-Knowledge project Context On-To-Knowledge IST project about content-driven knowledge management through evolving ontologies
More informationOntology Development. Qing He
A tutorial report for SENG 609.22 Agent Based Software Engineering Course Instructor: Dr. Behrouz H. Far Ontology Development Qing He 1 Why develop an ontology? In recent years the development of ontologies
More informationAn Evaluation of Geo-Ontology Representation Languages for Supporting Web Retrieval of Geographical Information
An Evaluation of Geo-Ontology Representation Languages for Supporting Web Retrieval of Geographical Information P. Smart, A.I. Abdelmoty and C.B. Jones School of Computer Science, Cardiff University, Cardiff,
More informationSemi-automatic Composition of Web Services using Semantic Descriptions
Semi-automatic Composition of Web Services using Semantic Descriptions Evren Sirin 1, James Hendler 2, and Bijan Parsia 2 1 University of Maryland, Computer Science Department, College Park MD 20742, USA
More informationThe 2 nd Generation Web - Opportunities and Problems
The 2 nd Generation Web - Opportunities and Problems Dr. Uwe Aßmann Research Center for Integrational Software Engineering (RISE) Swedish Semantic Web Initiative (SWEB) Linköpings Universitet Contents
More informationAn Annotation Tool for Semantic Documents
An Annotation Tool for Semantic Documents (System Description) Henrik Eriksson Dept. of Computer and Information Science Linköping University SE-581 83 Linköping, Sweden her@ida.liu.se Abstract. Document
More informationA GML SCHEMA MAPPING APPROACH TO OVERCOME SEMANTIC HETEROGENEITY IN GIS
A GML SCHEMA MAPPING APPROACH TO OVERCOME SEMANTIC HETEROGENEITY IN GIS Manoj Paul, S. K. Ghosh School of Information Technology, Indian Institute of Technology, Kharagpur 721302, India - (mpaul, skg)@sit.iitkgp.ernet.in
More informationXML ALONE IS NOT SUFFICIENT FOR EFFECTIVE WEBEDI
Chapter 18 XML ALONE IS NOT SUFFICIENT FOR EFFECTIVE WEBEDI Fábio Ghignatti Beckenkamp and Wolfgang Pree Abstract: Key words: WebEDI relies on the Internet infrastructure for exchanging documents among
More informationAn Architecture for Semantic Enterprise Application Integration Standards
An Architecture for Semantic Enterprise Application Integration Standards Nenad Anicic 1, 2, Nenad Ivezic 1, Albert Jones 1 1 National Institute of Standards and Technology, 100 Bureau Drive Gaithersburg,
More informationAdaptable and Adaptive Web Information Systems. Lecture 1: Introduction
Adaptable and Adaptive Web Information Systems School of Computer Science and Information Systems Birkbeck College University of London Lecture 1: Introduction George Magoulas gmagoulas@dcs.bbk.ac.uk October
More informationTowards a Data Consistency Modeling and Testing Framework for MOF Defined Languages
Towards a Data Consistency Modeling and Testing Framework for MOF Defined Languages Jan Pettersen Nytun,2 Christian S. Jensen,3 Vladimir A. Oleshchuk Faculty of Engineering and Science, Agder University
More informationSemantic Web Domain Knowledge Representation Using Software Engineering Modeling Technique
Semantic Web Domain Knowledge Representation Using Software Engineering Modeling Technique Minal Bhise DAIICT, Gandhinagar, Gujarat, India 382007 minal_bhise@daiict.ac.in Abstract. The semantic web offers
More informationSemantic Web Technologies
1/57 Introduction and RDF Jos de Bruijn debruijn@inf.unibz.it KRDB Research Group Free University of Bolzano, Italy 3 October 2007 2/57 Outline Organization Semantic Web Limitations of the Web Machine-processable
More informationDescription Logic Systems with Concrete Domains: Applications for the Semantic Web
Description Logic Systems with Concrete Domains: Applications for the Semantic Web Volker Haarslev and Ralf Möller Concordia University, Montreal University of Applied Sciences, Wedel Abstract The Semantic
More informationAgent-oriented Semantic Discovery and Matchmaking of Web Services
Agent-oriented Semantic Discovery and Matchmaking of Web Services Ivan Mećar 1, Alisa Devlić 1, Krunoslav Tržec 2 1 University of Zagreb Faculty of Electrical Engineering and Computing Department of Telecommunications
More informationSemantic agents for location-aware service provisioning in mobile networks
Semantic agents for location-aware service provisioning in mobile networks Alisa Devlić University of Zagreb visiting doctoral student at Wireless@KTH September 9 th 2005. 1 Agenda Research motivation
More informationOntology Exemplification for aspocms in the Semantic Web
Ontology Exemplification for aspocms in the Semantic Web Anand Kumar Department of Computer Science Babasaheb Bhimrao Ambedkar University Lucknow-226025, India e-mail: anand_smsvns@yahoo.co.in Sanjay K.
More informationOntoXpl Exploration of OWL Ontologies
OntoXpl Exploration of OWL Ontologies Volker Haarslev and Ying Lu and Nematollah Shiri Computer Science Department Concordia University, Montreal, Canada haarslev@cs.concordia.ca ying lu@cs.concordia.ca
More informationAnalysing Web Ontology in Alloy: A Military Case Study
Analysing Web Ontology in Alloy: A Military Case Study Jin Song Dong Jun Sun Hai Wang School of Computing, National University of Singapore, {dongjs,sunjing,wangh}@comp.nus.edu.sg Chew Hung Lee Hian Beng
More informationSPARQL Back-end for Contextual Logic Agents
SPARQL Back-end for Contextual Logic Agents Cláudio Fernandes and Salvador Abreu Universidade de Évora Abstract. XPTO is a contextual logic system that can represent and query OWL ontologies from a contextual
More informationa paradigm for the Introduction to Semantic Web Semantic Web Angelica Lo Duca IIT-CNR Linked Open Data:
Introduction to Semantic Web Angelica Lo Duca IIT-CNR angelica.loduca@iit.cnr.it Linked Open Data: a paradigm for the Semantic Web Course Outline Introduction to SW Give a structure to data (RDF Data Model)
More informationAnnotation for the Semantic Web During Website Development
Annotation for the Semantic Web During Website Development Peter Plessers and Olga De Troyer Vrije Universiteit Brussel, Department of Computer Science, WISE, Pleinlaan 2, 1050 Brussel, Belgium {Peter.Plessers,
More informationACKNOWLEDGEMENTS REFERENCES BIOGRAPHY
ACKNOWLEDGEMENTS The work reported in this paper has been funded in part by the Cooperative Research Centres Program through the Department of the Prime Minister and Cabinet of the Commonwealth Government
More informationExperiences with OWL-S, Directions for Service Composition:
Experiences with OWL-S, Directions for Service Composition: The Cashew Position Barry Norton 1 Knowledge Media Institute, Open University, Milton Keynes, UK b.j.norton@open.ac.uk Abstract. Having used
More informationTowards Automatic Selection of Web Services Using Graph Transformation Rules
Towards Automatic Selection of Web Services Using Graph Transformation Rules Jan Hendrik Hausmann, Reiko Heckel, Marc Lohmann Faculty of Computer Science, Electrical Engineering and Mathematics University
More informationSEMANTIC WEB POWERED PORTAL INFRASTRUCTURE
SEMANTIC WEB POWERED PORTAL INFRASTRUCTURE YING DING 1 Digital Enterprise Research Institute Leopold-Franzens Universität Innsbruck Austria DIETER FENSEL Digital Enterprise Research Institute National
More informationOrchestrating Music Queries via the Semantic Web
Orchestrating Music Queries via the Semantic Web Milos Vukicevic, John Galletly American University in Bulgaria Blagoevgrad 2700 Bulgaria +359 73 888 466 milossmi@gmail.com, jgalletly@aubg.bg Abstract
More informationAutomation of Semantic Web based Digital Library using Unified Modeling Language Minal Bhise 1 1
Automation of Semantic Web based Digital Library using Unified Modeling Language Minal Bhise 1 1 Dhirubhai Ambani Institute for Information and Communication Technology, Gandhinagar, Gujarat, India Email:
More informationFrom XML to Semantic Web
From XML to Semantic Web Changqing Li and Tok Wang Ling Department of Computer Science, National University of Singapore {lichangq, lingtw}@comp.nus.edu.sg Abstract. The present web is existing in the
More informationHelmi Ben Hmida Hannover University, Germany
Helmi Ben Hmida Hannover University, Germany 1 Summarizing the Problem: Computers don t understand Meaning My mouse is broken. I need a new one 2 The Semantic Web Vision the idea of having data on the
More informationKnowledge and Ontological Engineering: Directions for the Semantic Web
Knowledge and Ontological Engineering: Directions for the Semantic Web Dana Vaughn and David J. Russomanno Department of Electrical and Computer Engineering The University of Memphis Memphis, TN 38152
More informationThe Semantic Web: A Vision or a Dream?
The Semantic Web: A Vision or a Dream? Ben Weber Department of Computer Science California Polytechnic State University May 15, 2005 Abstract The Semantic Web strives to be a machine readable version of
More informationGenerating and Managing Metadata for Web-Based Information Systems
Generating and Managing Metadata for Web-Based Information Systems Heiner Stuckenschmidt and Frank van Harmelen Department of Mathematics and Computer Science Vrije Universiteit Amsterdam De Boelelaan
More informationIntroduction to modeling. ER modelling
Introduction to modeling ER modelling Slides for this part are based on Chapters 8 from Halpin, T. & Morgan, T. 2008, Information Modeling and Relational Databases, Second Edition (ISBN: 978-0-12-373568-3),
More informationThe Semantic Planetary Data System
The Semantic Planetary Data System J. Steven Hughes 1, Daniel J. Crichton 1, Sean Kelly 1, and Chris Mattmann 1 1 Jet Propulsion Laboratory 4800 Oak Grove Drive Pasadena, CA 91109 USA {steve.hughes, dan.crichton,
More informationContents. G52IWS: The Semantic Web. The Semantic Web. Semantic web elements. Semantic Web technologies. Semantic Web Services
Contents G52IWS: The Semantic Web Chris Greenhalgh 2007-11-10 Introduction to the Semantic Web Semantic Web technologies Overview RDF OWL Semantic Web Services Concluding comments 1 See Developing Semantic
More informationSEMANTIC WEB LANGUAGES - STRENGTHS AND WEAKNESS
SEMANTIC WEB LANGUAGES - STRENGTHS AND WEAKNESS Sinuhé Arroyo Ying Ding Rubén Lara Universität Innsbruck Universität Innsbruck Universität Innsbruck Institut für Informatik Institut für Informatik Institut
More informationA Map-based Integration of Ontologies into an Object-Oriented Programming Language
A Map-based Integration of Ontologies into an Object-Oriented Programming Language 1 Introduction The ontology technology has been widely accepted as an integral part of managing the semantics of information
More informationEQuIKa System: Supporting OWL applications with local closed world assumption
EQuIKa System: Supporting OWL applications with local closed world assumption Anees Mehdi and Jens Wissmann Institute AIFB, Karlsruhe Institute of Technology, DE anees.mehdi@kit.edu Forschungszentrum Informatik
More informationDesign Software Architecture Models using Ontology
Design Software Architecture Models using Ontology Jing Sun 1, Hai H. Wang 2 and Tianming Hu 3 1 Department of Computer Science, The University of Auckland, New Zealand j.sun@cs.auckland.ac.nz 2 School
More informationSubset, Equality, and Exclusion Rules In ORM
Reference: Mustafa Jarrar: Lecture Notes on Subset, Equality, and Exclusion Rules in ORM Birzeit University, Palestine, 2015 Subset, Equality, and Exclusion Rules In ORM (Chapter 6) Mustafa Jarrar Birzeit
More informationProposal for Implementing Linked Open Data on Libraries Catalogue
Submitted on: 16.07.2018 Proposal for Implementing Linked Open Data on Libraries Catalogue Esraa Elsayed Abdelaziz Computer Science, Arab Academy for Science and Technology, Alexandria, Egypt. E-mail address:
More informationGuided Tour: Intelligent Conceptual Modelling in EER and UML-like class diagrams with icom compared to ORM2
Guided Tour: Intelligent Conceptual Modelling in EER and UML-like class diagrams with icom compared to ORM2 Abstract. In this guided tour we illustrate the advantages of intelligent conceptual modelling,
More informationDESIGN AND EVALUATION OF A GENERIC METHOD FOR CREATING XML SCHEMA. 1. Introduction
DESIGN AND EVALUATION OF A GENERIC METHOD FOR CREATING XML SCHEMA Mahmoud Abaza and Catherine Preston Athabasca University and the University of Liverpool mahmouda@athabascau.ca Abstract There are many
More informationObsoletes: 2070, 1980, 1942, 1867, 1866 Category: Informational June 2000
Network Working Group Request for Comments: 2854 Obsoletes: 2070, 1980, 1942, 1867, 1866 Category: Informational D. Connolly World Wide Web Consortium (W3C) L. Masinter AT&T June 2000 The text/html Media
More informationDesign and Implementation of an RDF Triple Store
Design and Implementation of an RDF Triple Store Ching-Long Yeh and Ruei-Feng Lin Department of Computer Science and Engineering Tatung University 40 Chungshan N. Rd., Sec. 3 Taipei, 04 Taiwan E-mail:
More informationSEMANTIC WEB LANGUAGES STRENGTHS AND WEAKNESS
SEMANTIC WEB LANGUAGES STRENGTHS AND WEAKNESS Sinuhé Arroyo, Rubén Lara, Ying Ding, Michael Stollberg, Dieter Fensel Universität Innsbruck Institut für Informatik Technikerstraße 13 6020 Innsbruck, Austria
More informationUML-Based Conceptual Modeling of Pattern-Bases
UML-Based Conceptual Modeling of Pattern-Bases Stefano Rizzi DEIS - University of Bologna Viale Risorgimento, 2 40136 Bologna - Italy srizzi@deis.unibo.it Abstract. The concept of pattern, meant as an
More informationTowards the Semantic Desktop. Dr. Øyvind Hanssen University Library of Tromsø
Towards the Semantic Desktop Dr. Øyvind Hanssen University Library of Tromsø Agenda Background Enabling trends and technologies Desktop computing and The Semantic Web Online Social Networking and P2P Computing
More informationSpinning the Semantic Web
Spinning the Semantic Web Bringing the World Wide Web to Its Full Potential Edited by Dieter Fensel, James Hendler, Henry Lieberman, and Wolfgang Wahlster The MIT Press Cambridge, Massachusetts London,
More informationReasoning on Business Processes and Ontologies in a Logic Programming Environment
Reasoning on Business Processes and Ontologies in a Logic Programming Environment Michele Missikoff 1, Maurizio Proietti 1, Fabrizio Smith 1,2 1 IASI-CNR, Viale Manzoni 30, 00185, Rome, Italy 2 DIEI, Università
More informationIntegrating e-commerce standards and initiatives in a multi-layered ontology
Integrating e-commerce standards and initiatives in a multi-layered ontology Oscar Corcho, Asunción Gómez-Pérez Facultad de Informática, Universidad Politécnica de Madrid. Campus de Montegancedo s/n. Boadilla
More informationWhere is the Semantics on the Semantic Web?
Where is the Semantics on the Semantic Web? Ontologies and Agents Workshop Autonomous Agents Montreal, 29 May 2001 Mike Uschold Mathematics and Computing Technology Boeing Phantom Works Acknowledgements
More informationUsing ESML in a Semantic Web Approach for Improved Earth Science Data Usability
Using in a Semantic Web Approach for Improved Earth Science Data Usability Rahul Ramachandran, Helen Conover, Sunil Movva and Sara Graves Information Technology and Systems Center University of Alabama
More informationMapping between Digital Identity Ontologies through SISM
Mapping between Digital Identity Ontologies through SISM Matthew Rowe The OAK Group, Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello Street, Sheffield S1 4DP, UK m.rowe@dcs.shef.ac.uk
More informationInformation mining and information retrieval : methods and applications
Information mining and information retrieval : methods and applications J. Mothe, C. Chrisment Institut de Recherche en Informatique de Toulouse Université Paul Sabatier, 118 Route de Narbonne, 31062 Toulouse
More informationUsing Semantic Web Technologies for context-aware Information Providing to Mobile Devices
Using Semantic Web Technologies for context-aware Information Providing to Mobile Devices Fabian Abel Institute Knowledge Based Systems Appelstr. 4-30167 Hannover Germany Fabian.Abel@gmx.de Jan Brase L3S
More informationInterpreting XML via an RDF Schema
Interpreting XML via an RDF Schema Michel Klein 1 Abstract. One of the major problems in the realization of the vision of the Semantic Web is the transformation of existing web data into sources that can
More informationCHAPTER 1. Topic: UML Overview. CHAPTER 1: Topic 1. Topic: UML Overview
CHAPTER 1 Topic: UML Overview After studying this Chapter, students should be able to: Describe the goals of UML. Analyze the History of UML. Evaluate the use of UML in an area of interest. CHAPTER 1:
More informationFalcon-AO: Aligning Ontologies with Falcon
Falcon-AO: Aligning Ontologies with Falcon Ningsheng Jian, Wei Hu, Gong Cheng, Yuzhong Qu Department of Computer Science and Engineering Southeast University Nanjing 210096, P. R. China {nsjian, whu, gcheng,
More informationSemantic Web Systems Introduction Jacques Fleuriot School of Informatics
Semantic Web Systems Introduction Jacques Fleuriot School of Informatics 11 th January 2015 Semantic Web Systems: Introduction The World Wide Web 2 Requirements of the WWW l The internet already there
More informationSemantic Retrieval System Based on Ontology
Proceedings of the 5th WSEAS Int. Conference on Information Security and Privacy, Venice, Italy, November 20-22, 2006 124 Semantic Retrieval System Based on Ontology JIANBO XU 1, ZHONGLIN XU 2, JIAXUN
More informationA Tool for Storing OWL Using Database Technology
A Tool for Storing OWL Using Database Technology Maria del Mar Roldan-Garcia and Jose F. Aldana-Montes University of Malaga, Computer Languages and Computing Science Department Malaga 29071, Spain, (mmar,jfam)@lcc.uma.es,
More informationChapter 8: Enhanced ER Model
Chapter 8: Enhanced ER Model Subclasses, Superclasses, and Inheritance Specialization and Generalization Constraints and Characteristics of Specialization and Generalization Hierarchies Modeling of UNION
More informationGeneralized Document Data Model for Integrating Autonomous Applications
6 th International Conference on Applied Informatics Eger, Hungary, January 27 31, 2004. Generalized Document Data Model for Integrating Autonomous Applications Zsolt Hernáth, Zoltán Vincellér Abstract
More informationOntology Development Tools and Languages: A Review
Ontology Development Tools and Languages: A Review Parveen 1, Dheeraj Kumar Sahni 2, Dhiraj Khurana 3, Rainu Nandal 4 1,2 M.Tech. (CSE), UIET, MDU, Rohtak, Haryana 3,4 Asst. Professor, UIET, MDU, Rohtak,
More informationOWL an Ontology Language for the Semantic Web
OWL an Ontology Language for the Semantic Web Ian Horrocks horrocks@cs.man.ac.uk University of Manchester Manchester, UK OWL p. 1/27 Talk Outline OWL p. 2/27 Talk Outline The Semantic Web OWL p. 2/27 Talk
More informationSemantic Exploitation of Engineering Models: An Application to Oilfield Models
Semantic Exploitation of Engineering Models: An Application to Oilfield Models Laura Silveira Mastella 1,YamineAït-Ameur 2,Stéphane Jean 2, Michel Perrin 1, and Jean-François Rainaud 3 1 Ecole des Mines
More informationSpemmet - A Tool for Modeling Software Processes with SPEM
Spemmet - A Tool for Modeling Software Processes with SPEM Tuomas Mäkilä tuomas.makila@it.utu.fi Antero Järvi antero.jarvi@it.utu.fi Abstract: The software development process has many unique attributes
More informationPOMELo: A PML Online Editor
POMELo: A PML Online Editor Alvaro Graves Tetherless World Constellation Department of Cognitive Sciences Rensselaer Polytechnic Institute Troy, NY 12180 gravea3@rpi.edu Abstract. This paper introduces
More informationSemantic Web Knowledge Representation in the Web Context. CS 431 March 24, 2008 Carl Lagoze Cornell University
Semantic Web Knowledge Representation in the Web Context CS 431 March 24, 2008 Carl Lagoze Cornell University Acknowledgements for various slides and ideas Ian Horrocks (Manchester U.K.) Eric Miller (W3C)
More informationOntological Modeling: Part 14
Ontological Modeling: Part 14 Terry Halpin INTI International University This is the fourteenth in a series of articles on ontology-based approaches to modeling. The main focus is on popular ontology languages
More information[MS-XHTML]: Internet Explorer Extensible HyperText Markup Language (XHTML) Standards Support Document
[MS-XHTML]: Internet Explorer Extensible HyperText Markup Language (XHTML) Standards Support Document Intellectual Property Rights Notice for Open Specifications Documentation Technical Documentation.
More informationDesign concepts for data-intensive applications
6 th International Conference on Applied Informatics Eger, Hungary, January 27 31, 2004. Design concepts for data-intensive applications Attila Adamkó Department of Information Technology, Institute of
More informationGraphical Notation for Topic Maps (GTM)
Graphical Notation for Topic Maps (GTM) 2005.11.12 Jaeho Lee University of Seoul jaeho@uos.ac.kr 1 Outline 2 Motivation Requirements for GTM Goals, Scope, Constraints, and Issues Survey on existing approaches
More informationMethodologies for Web Information System Design
Methodologies for Web Information System Design Peter Barna, Flavius Frasincar, Geert-Jan Houben, and Richard Vdovjak Technische Universiteit Eindhoven PO Box 53, NL-5600 MB Eindhoven, The Netherlands
More informationSubset, Equality, and Exclusion Rules In ORM
Reference: Mustafa Jarrar: Lecture Notes on Subset, Equality, and Exclusion Rules in ORM University of Birzeit, Palestine, 2015 Subset, Equality, and Exclusion Rules In ORM (Chapter 6) Dr. Mustafa Jarrar
More informationInternational Journal of Computer Science Trends and Technology (IJCST) Volume 3 Issue 4, Jul-Aug 2015
RESEARCH ARTICLE OPEN ACCESS Multi-Lingual Ontology Server (MOS) For Discovering Web Services Abdelrahman Abbas Ibrahim [1], Dr. Nael Salman [2] Department of Software Engineering [1] Sudan University
More informationChapter 2 Entity-Relationship Data Modeling: Tools and Techniques. Fundamentals, Design, and Implementation, 9/e
Chapter 2 Entity-Relationship Data Modeling: Tools and Techniques Fundamentals, Design, and Implementation, 9/e Three Schema Model ANSI/SPARC introduced the three schema model in 1975 It provides a framework
More informationA SEMANTIC MATCHMAKER SERVICE ON THE GRID
DERI DIGITAL ENTERPRISE RESEARCH INSTITUTE A SEMANTIC MATCHMAKER SERVICE ON THE GRID Andreas Harth Yu He Hongsuda Tangmunarunkit Stefan Decker Carl Kesselman DERI TECHNICAL REPORT 2004-05-18 MAY 2004 DERI
More informationOntological Modeling: Part 7
Ontological Modeling: Part 7 Terry Halpin LogicBlox and INTI International University This is the seventh in a series of articles on ontology-based approaches to modeling. The main focus is on popular
More informationDCMI Abstract Model - DRAFT Update
1 of 7 9/19/2006 7:02 PM Architecture Working Group > AMDraftUpdate User UserPreferences Site Page Actions Search Title: Text: AttachFile DeletePage LikePages LocalSiteMap SpellCheck DCMI Abstract Model
More informationEnvisioning Semantic Web Technology Solutions for the Arts
Information Integration Intelligence Solutions Envisioning Semantic Web Technology Solutions for the Arts Semantic Web and CIDOC CRM Workshop Ralph Hodgson, CTO, TopQuadrant National Museum of the American
More informationOntological Modeling: Part 8
Ontological Modeling: Part 8 Terry Halpin LogicBlox and INTI International University This is the eighth in a series of articles on ontology-based approaches to modeling. The main focus is on popular ontology
More informationFedX: A Federation Layer for Distributed Query Processing on Linked Open Data
FedX: A Federation Layer for Distributed Query Processing on Linked Open Data Andreas Schwarte 1, Peter Haase 1,KatjaHose 2, Ralf Schenkel 2, and Michael Schmidt 1 1 fluid Operations AG, Walldorf, Germany
More informationAgenda. Introduction. Semantic Web Architectural Overview Motivations / Goals Design Conclusion. Jaya Pradha Avvaru
Semantic Web for E-Government Services Jaya Pradha Avvaru 91.514, Fall 2002 University of Massachusetts Lowell November 25, 2002 Introduction Agenda Semantic Web Architectural Overview Motivations / Goals
More informationTowards the Semantic Web
Towards the Semantic Web Ora Lassila Research Fellow, Nokia Research Center (Boston) Chief Scientist, Nokia Venture Partners LLP Advisory Board Member, W3C XML Finland, October 2002 1 NOKIA 10/27/02 -
More informationSemantic Web Technology Evaluation Ontology (SWETO): A test bed for evaluating tools and benchmarking semantic applications
Semantic Web Technology Evaluation Ontology (SWETO): A test bed for evaluating tools and benchmarking semantic applications WWW2004 (New York, May 22, 2004) Semantic Web Track, Developers Day Boanerges
More informationSemantic enrichment of a web legal information retrieval system
Semantic enrichment of a web legal information retrieval system José Saias and Paulo Quaresma Departamento de Informática, Universidade de Évora, 7000 Évora, Portugal jsaias pq@di.uevora.pt Abstract. Intelligent
More informationUniqueness and Identity Rules in ORM
Reference: Mustafa Jarrar: Lecture Notes on Uniqueness and Identity Rules in ORM Birzeit University, Palestine, 2015 Uniqueness and Identity Rules in ORM (Chapter 4) Mustafa Jarrar Birzeit University,
More informationSemantic Web Technology Evaluation Ontology (SWETO): A Test Bed for Evaluating Tools and Benchmarking Applications
Wright State University CORE Scholar Kno.e.sis Publications The Ohio Center of Excellence in Knowledge- Enabled Computing (Kno.e.sis) 5-22-2004 Semantic Web Technology Evaluation Ontology (SWETO): A Test
More informationBrowsing the Semantic Web
Proceedings of the 7 th International Conference on Applied Informatics Eger, Hungary, January 28 31, 2007. Vol. 2. pp. 237 245. Browsing the Semantic Web Peter Jeszenszky Faculty of Informatics, University
More informationOn Querying Ontologies with Contextual Logic Programming
On Querying Ontologies with Contextual Logic Programming Cláudio Fernandes, Nuno Lopes, and Salvador Abreu Universidade de Évora Abstract. We describe a system in which Contextual Logic Programming is
More informationSchema Equivalence and Optimization
Reference: Mustafa Jarrar: Lecture Notes on Schema Equivalence and Optimization in ORM Birzeit University, Palestine, 2015 Schema Equivalence and Optimization Mustafa Jarrar Birzeit University, Palestine
More information