Mining Class Hierarchies from XML Data: Representation Techniques

Size: px
Start display at page:

Download "Mining Class Hierarchies from XML Data: Representation Techniques"

Transcription

1 Mining Class Hierarchies from XML Data: Representation Techniques Paolo Ceravolo 1 and Ernesto Damiani 1 Department of Information Technology University of Milan Via Bramante, Crema (Italy) damiani, ceravolo@dti.unimi.it 1 Introduction In this paper, we describe a technique for extracting patterns to a XML data flow; then, we show how such patterns can be developed into an ontology of classes. Also, we discuss the impact of different fuzzy representation techniques for XML data on the outcome of our procedure. One might wonder why all this is needed, since the semantics of XML data could in principle be satisfactorily represented via their associated XML schemata ComplexTypes. Unfortunately it turns out that standard XML schema definitions need to cover a wide repertoire of possible attributes. For this reason, optional elements are widely used, thus decreasing the expressiveness of XML schemata as descriptors of the content of single instances. Our approach relies on comparing fuzzy encodings of XML fragments. This comparison will allow us to define typical sets of attributes, that we shall consider hints to possible meaningful classes. Then, we shall evaluate fuzzy overlapping between candidate cluster heads in order to define a tentative class hierarchy. Our fuzzy modelling assumes that a domain expert has associated an importance degree in the [0, 1] interval to vocabulary elements (i.e. tag names). As we shall see in the remainder of the paper, this burden is not excessive, since this importance assessment only needs to be carried out once, looking at the schema. At run time, each incoming XML fragment is mapped into a fuzzy set whose elements are the tag names[?]. Each element membership is computed by aggregating the vocabulary importance values of the tags lying on the path from it to the root. The topology of the individual XML tree is modelled by using an aggregation that takes into account nodes nesting level or nodes occurrence. Our procedure consists of the following steps: Identify and choose the right fuzzy comparison function for a given XML data flow; Classify the authentication data flow according to it;

2 2 Paolo Ceravolo and Ernesto Damiani Build a tentative is-a hierarchy based on classes overlapping. The comparison function M will be based on the degree of inclusion of the fuzzy sets modelling the XML fragments. These functions have been extensively studied in the literature [1], [4], [3]. The comparison function must to maintain a semantical coherence with assignment function adopted in evaluating the nodes membership values. For this reason in Section 3 we discuss on the right way of coupling weight assignments and comparison function. In most cases comparison function are not symmetrical and lack the mathematical properties of a distance 1. As a consequence, no standard clustering technique can be used. Instead, our technique: Periodically scans portions of the incoming flow to select typical XML fragments as tentative clusterheads representing classes. Computes the comparison of each incoming fragment with the current clusterheads and add it to the class of the closest match. After creating the classes, we evaluate the quality of the classification by computing each class cohesion, i.e. the comparison of the elements of a class with the elements in other classes whose distances from their own clusterhead is maximum. If the classification is satisfactory, i.e. the cohesion of all classes is high enough, we apply a suitable comparison measure M applied to clusterheads in order to estimate class overlapping. When the comparison between two classes A and B is higher than a pre-set threshold we assume a generalization relation exists, directed along increasing comparison score. Finally, the generalization hierarchy obtained in the previous steps is first converted into a metadata format suitable for ontologies like RDF [5] of OWL [6] and then presented to the user via a standard ontology editor. The paper is organized as follows: in Section 2, we introduce the basic notions of fuzzy representation of XML data and outline their application in the framework hierarchy mining. In Section 3, we discuss in further detail the role of weighting and comparison functions when XML data are represented as fuzzy sets. Section 4 discusses how fragments can be used to build a class hierarchy and, eventually, a complete ontology. Finally in Section 5 we draw the conclusion and give an outline of our future work. 2 A Fuzzy Pattern Extraction Technique In this section we represent XML data items as flat fuzzy sets. For transforming an XML tree into a fuzzy set, we need to evaluate its structure for assigning relevance values to nodes in a way reminiscent of their original positions in the XML tree topology. In other words, we use the fuzzy membership 1 This lack of symmetry is reasonable, since these functions model object comparison rather than a generic similarity.

3 Mining Class Hierarchies from XML Data: Representation Techniques 3 degree for expressing the relevance of each XML node as it is suggested by its structural position in the original XML tree. In a XML Schema a node can occur repetitively and in different positions of the tree. A collection of elements which may contain duplicates is called a bag (also called multiset). We use the standard notation for crisp bags, as follows: A =< a, b, b, c, d, d >=< 1 a, 2 b, 1 c, 2 d >. Extending this notation to fuzzy bags, we obtain: A = {{0.64/2} a, {0.66/1, 0.82/1} b, {0.49/1, 0.71/1} c} Where we used the standard notation putting each element s membership value before the slash character, followed by the number of occurrences associated to that value. We are now ready to describe the basic steps of our technique for comparing XML fragments; in the next sections, we shall propose a coupling of membership functions and matching measures suitable to take into account XML data items topology. Before computing matching between XML fragment representations, however, we have to: Represent XML trees in term of fuzzy sets. During this step we need to assign weight to nodes. Evaluating each node we assign two relevance values to it: - an explicit one, according to the relevance of its tag label in the current domain vocabulary. - an implicit one, taking into account its position in the tree structure. Aggregate these two values, obtaining a single membership degree. Match objects and get a measure of resemblance between them. According to the application requirements, different techniques can be used for each step. In the following Table 1 we outline the main options: Weighting Nodes Depth Fan-out Domain Expert Content Size/Type Type Topology Topology Tag Tag Table 1. Weight Assignment Techniques Table 1 distinguishes among Topology- and Tag-based weight assignment techniques. Indeed, depth and fan-out are two simple techniques for evaluating the relevance of a node based on its topological properties. If relevance has to be estimated based on the node content two approaches are possible. First, one could ask a domain expert to assign the nodes weights; secondly, weights

4 4 Paolo Ceravolo and Ernesto Damiani can be computed based on the node content size and/or according to serialized content type [3]. Topology and Tag-based weights must be aggregated in a single value using a standard t-norm. An aggregation function allows us to tune the relevance we want to attribute to the tag position in the topology rather than to the node tag itself. In [3] a list of aggregation function are proposed. In this paper, we adopt a parametrical average-based norm, supporting fine tuning. Namely, we set: F k (x, y) = k(x + y xy) + (1 k)(xy) (1) Each XML data item can now be represented as a fuzzy set where elements membership is given by the combination of weights described above. This allows us to use a standard functions for objects comparison to compute clusters to be later refined into classes (Sect. 4). Fuzzy logic versions of standard comparison functions have been extensively studied in the literature. In [4], Damiani, Bosc, and Fugini describe comparison measures based on different type of implication; Bouchon-Meunier and Rifqi [1] propose a complete classification of comparison measures distinguish among measures of en satisfiability, resemblance and inclusion. In the remainder of the paper, we will discuss coupling between some weighting and comparison measures depending on the semantics of XML file structure. 3 Combining Weighting and Comparison Functions Let us ground our discussion in an example. An XML data item describing book references can have different structures. If you are describing a book for an e-business service you will use a closed set of information for each instance. If you are describing a book for a bibliography search engine you are interested in the number of citations per book, and this kind of information is not predetermined for all instances. Obviously, different weighting functions must be used in these two cases. Also, comparing bibliography entries is an entirely different matter than comparing a publisher s book catalog entries. In this section, we will propose some associations between membership and comparison functions, depending on the purpose of their utilization. 3.1 Focusing on Nodes Depth In the first case, we have to compare XML files where the structure is used for distinguishing general elements, i.e. internal nodes, from data level elements, i.e. tree leaves. From a schema point of view, high-level elements (i.e. the ones closer to the root) are more relevant than low-level ones. Fig. 1 shows an example of such an XML file 2. 2 For the sake of clarity we omit the XML elements content, as it is not relevant to our discussion

5 Mining Class Hierarchies from XML Data: Representation Techniques 5 <DOC> <BOOK> <TITLE></TITLE> <AUTHOR></AUTHOR> </BOOK> <EDITION> <PUBLISHER></PUBLISHER> <EDITOR></EDITOR> <ISBN></ISBN> <PRICE></PRICE> </EDITION> </DOC> Fig. 1. The book references XML data item Such an XML tree can be represented assigning weights to elements according to a pre-classification of the domain vocabulary and to a weighting function W expressing their distance from the root. The domain vocabulary used in this example is shown in the second column of the Table 2. A simple weighting function is the following: W = d max l d max where d max is the longest path in the tree from the root to a leaf node. The level weighting column in Table 2 shows weight assignments according to the nodes depth function. Now, values have to be aggregated by means of our aggregation function F k, as explained in Section 2. The third column of the table shows the results of the function assuming to set parameter k to 0.4, i.e. giving more emphasis to the tag position in the tree than to the tag name pre-set importance stored in the vocabulary. Element Term Weighting Level Weighting Aggregation Label Function Book B Title T Author A Edition En Publisher Pu Editor Er ISBN I Price Pr Table 2. Elements Weighting to the XML in Fig. 1

6 6 Paolo Ceravolo and Ernesto Damiani We can now provide a representation of the XML file in Fig. 1 as a fuzzy set: F 1 = {0.8/B, 0.6/T, 0.55/A, 0.46/En, 0.24/P u, 0.2/Er, 0.32/I, 0.24/P r} Following the same procedure, a second XML file (Fig. 2), with same tags but with the element Price in a different position, would be represented as follows: F 2 = {0.8/B, 0.6/T, 0.55/A, 0.46/En, 0.24/P u, 0.2/Er, 0.32/I, 0.41/P r} <DOC> <BOOK> <TITLE></TITLE> <AUTHOR></AUTHOR> <EDITION> <PUBLISHER></PUBLISHER> <EDITOR></EDITOR> <ISBN></ISBN> </EDITION> </BOOK> <PRICE></PRICE> </DOC> Fig. 2. Another XML data item 3.2 Comparing XML Data Items Representations Based on Nodes Depth Comparing fuzzy sets F 1 and F 2, we are interested in differences due to the different topological structures of the XML trees they encode. Disregarding pre-set importance values of tags stored in the vocabulary 3, our comparison function has to return a different score when we compare a set generated by a shallow XML tree to a set generated by a deeply nested one than viceversa. In other words, we need an asymmetrical comparison. Suppose that two fuzzy sets obtained encoding XML trees, A and B, have the same support, i.e. their difference is only due to the original topology of the trees they encode. Suppose also that the only difference between the two sets is due to an element x having a higher membership value in A, e.g. because node x was positioned 3 In the remainder of the section, we assume all elements have the same pre-set importance and that all differences in membership are due to topology.

7 Mining Class Hierarchies from XML Data: Representation Techniques 7 in tree(a) closer to the root than it was in tree(b) 4. Then, when x has a higher membership in A than in B, we need M(A, B) < M(B, A). Note that = M(A, B) M(B, A) should depend on the types of elements involved, i.e. should depend on the type of x 5 Specifically, measure M allows us to cluster objects aiming to a hierarchy construction. In fact M(A, B) returns us a different value then M(B, A), and can be used to estimate a direction in the hierarchy. Following Bouchon-Meunier [1] we can define one basic measures satisfying our requirements. Inclusion: M(A, B) = A B A = A B (A B) + (A B) That can be represented as follows (2) min(µa (x), µ b (x)) min(µa (x), µ b (x)) + max(0, µ a (x) µ b (x) (3) Note that Inclusion is a typical monotonic measure. Using for instance the Inclusion measure for our example we get: F 1 F 2 = {0.7/B, 0.4/T, 0.36/A, 0.46/En, 0.24/P u, 0.2/Er, 0.32/I, 0.24/P r} Hence F 1 F 2 = {0.1/B, 0.2/T, 0.19/A, 0/En, 0/P u, 0/Er, 0/I, 0/P r} M(F 1, F 2) = 1.49 In order to normalize this result we can use a simple function such as 1 x, getting a final value equal to At contrary M(F 2, F 1) produces a score of We informally write tree(x) to denote a (not unique) XML tree encoded by fuzzy set X, given a vocabulary and a value of k 5 Of course, the opposite choice could also be made under different assumptions. For instance in a Satisfiability measure we have M(A, B) > M(B, A): M(A, B) = A B B = A B (A B)+(B A). To put it more clearly, a Satisfiability measure represents the fact that a phone is more included in a computer than a computer in a phone, because most phone features are also held by the computer, but the viceversa is not true. The strategy described in the paper is the one usually adopted for O-O schema design, i.e. the simpler structure is more general, and therefore more similar to the more specific ones than these specializations are similar to it.

8 8 Paolo Ceravolo and Ernesto Damiani 3.3 Focusing on Nodes Fan-out In some cases we want to compare XML files giving more emphasis to elements occurrence than to the original trees topology. With reference to our running example about books, we assume to work with a corpus of XML files describing bibliography information. In this application the number of citations per book, and the number of cited books are very important. For representing our objects we use a different domain vocabulary; also, we employ a different function for defining nodes relevance into the XML data item. We can think of all kind of functions taking into account the number of sub-nodes starting from a given node. In our example, we use a very simple function, dividing the number of children of a node on the basis of a given parameter. F (x) = F anout(x) F anout(x) + β Taking β equal to 1 we represent the fuzzy set version of XML files in Fig. 3 and Fig. 4 according to the Table 3. <DOC> <BOOK> <TITLE></TITLE> <AUTHOR></AUTHOR> <CITED> </CITED> <CITATION> <CITATION> </BOOK> </DOC> Fig. 3. A Book Citation XML File It is also possible to use a fuzzy bag representation, taking into account multiple occurrences of the same element. In this case, we can express the XML file in Fig. 3 as follows:

9 Mining Class Hierarchies from XML Data: Representation Techniques 9 <DOC> <BOOK> <TITLE></TITLE> <AUTHOR></AUTHOR> <CITED> </CITED> <CITATION> <CITATION> </BOOK> </DOC> Fig. 4. A Book Citation XML File Element Term Weighting Level Weighting Aggregation Label Function Book B Title T Author A Cited Cd Citation Cn Book B Title T Author A Table 3. Elements Weighting to the XML in Fig. 3 F 3 = {{0.97/1, 0.8/6} B, {0.4/7} T, {0.36/7} A, {0.8/2} Cd, {0.78/4} Cn} Much in the same way, it is possible represents the XML in Fig. 4 as reported below. F 3 = {{0.97/1, 0.8/7} B, {0.4/8} T, {0.36/8} A, {0.8/6} Cd, {0.78/1} Cn}

10 10 Paolo Ceravolo and Ernesto Damiani 3.4 Comparing XML Files Representations Based on Nodes Fan-out Comparing F 3 and F 4 we are interested extracting a difference on the basis of elements occurrences. An object A, represented by a bag where some elements have a high number of occurrences, must be similar to another object B, with the number of occurrences is roughly the same and the result of comparison have to be equal regardless if comparing A to B or B to A. In other words we need a symmetric measure of similarity. Following again [1], the following measure can be used: Similarity: M(A, B) = A B A B = A B (A B) + (A B) + (B A) that is, in a fuzzy representation: min(µa (x), µ b (x)) min(µa (x), µ b (x)) + max(0, µ a (x) µ b (x) + max(0, µ b (x) µ a (x) (4) Using this function, the similarity degree between our sample sets F 3 and F 4 is equal to Building the Hierarchy Once a set of patterns has been identified by computing the similarity values between all XML documents, building a hierarchy is not difficult. Similarity allows for defining clusters composed of documents whose representation is close to a pattern 6. After creating the classes, we evaluate the quality of the classification we obtained by computing each class cohesion, i.e. the comparison score of the elements of a class with the elements in other classes whose distances from their own clusterhead is maximum. If the classification is satisfactory, i.e. the cohesion of all classes is high enough, we apply a suitable comparison function M applied to clusterheads in order to estimate class overlapping. When the comparison score between two classes A and B is higher than the threshold 6 Here we loosely use the term cluster, even if this is not completely correct. This term is only appropriate for symmetrical comparison functions. When we use a depth-based comparison function, our blocks are not the result of clustering, since the chosen similarity lacks the mathematical property of a distance. Intuitively, however, patterns within a valid cluster are more similar to each other than they are to a pattern belonging to a different cluster

11 Mining Class Hierarchies from XML Data: Representation Techniques 11 α we assume a generalization relation exists between A and B, directed along increasing comparison score. The next step is to assign meaning to candidate classes; this task is manually done by domain experts who relate derived classes structure of data to domain theory, and, more specifically to the current problem solving situation. This process can also lead to discovery of new concepts that improve problem solving and extend domain theories. We evaluate clustering validity in term of class cohesion or intra-class similarity. This is defined in terms of how individual documents match the prototypical description of the cluster they are assigned to by algorithm. Classes that exhibit high class cohesion lead to a better classification. Once obtained classes representing our domain the last task of our work is discover relations between them. We represent classes as vertices of a direct graph. Every edge joins the class with lower cardinality with classes with higher cardinality. Labels associated to the edges express the average value between the similarity values resulting from the comparison between the interested classes (Fig. 5(a)). The initial direct cyclic graph has to be converted in a direct acyclic graph that better express inheritance relations. If it is possible, in Fig. 5(b) and Fig. 5 (c) two results of this process are shown. In Fig. 5(b) for example, we show the Directed Acyclic Graph (DAG) corresponding to threshold α = 0.5. This situation shows how not always is possible to obtain a tree as we can instead see in Fig. 5(c), where choosing an higher threshold or based on a decision by a domain expert, the edge between CH3 and CH4 is removed. Fig. 5. (a): the DAG representing classes and relations between them. (b) and (c): the DAG and the tree resulting from the process of extraction of inheritance relations. Finally, the generalization hierarchy obtained in the previous steps is (i) converted into a metadata format suitable for ontologies like RDF [5] of OWL [6] and (ii) presented to the user via a standard ontology editor. Clusters are expressed as constraints on the initial XML schema 7 and are provided to the ontology engineer for a final pruning. In other words such an ontology schema is builded mixing top-down and bottom-up approaches, and using 7 In case of Schema lacking we assume to use the domain expert vocabulary as a first lossy schema.

12 12 Paolo Ceravolo and Ernesto Damiani semi-automated procedures, giving an important support for enabling knowledge representation in fine grained access control applications [2]. Periodically we scan portions of the incoming flow to reselect typical XML fragments as tentative clusterheads representing classes. Computes the comparison score of each incoming fragment with the current clusterheads and add it to the class of the closest match. 5 Conclusions We have outlined a knowledge extraction technique aimed at extracting class hierarchies from flows of XML fragments. We are testing our approach on experimental data and refining the modelling of data items in order to fully preserve the topological and cardinality information associated with tags. This should allow us to produce hierarchies at a finer level of granularity. To this end we plan to model our data using fuzzy bags suitably extending the function definition. 6 Acknowledgments The work reported in this paper has been partially supported by the Italian Ministry of Research FIRB program within the KIWI project. References 1. M. Rifqi B. Bouchon-Meunier and S. Bothorel. Towards general measures of comparison of objects. ACM, Special issue dedicated to the memory of Professor Arnold Kaufmann, 84(2), December J. Davies, D. Fensel, and F. van Harmelen. Towards the Semantic Web: Ontology- Driven Knowledge Management. John Wiley & Sons, Ltd, A. F. Fontana E. Damiani, L. Tanca. Fuzzy xml queries via context-based choice of aggregations. KYBERNETIKA, 36(6): , E. Damiani P. Bosc and M.G. Fugini. Fuzzy service selection in a distributed object-oriented environment. IEEE Transactions on Fuzzy Systems, 9(5), October World Wide Web. RDF Vocabulary Description Language 1.0: RDF Schema, December World Wide Web Consourtium. OWL Web Ontology Language Overview, December

OntoExtractor: A Fuzzy-Based Approach to Content and Structure-Based Metadata Extraction

OntoExtractor: A Fuzzy-Based Approach to Content and Structure-Based Metadata Extraction OntoExtractor: A Fuzzy-Based Approach to Content and Structure-Based Metadata Extraction Paolo Ceravolo, Ernesto Damiani, Marcello Leida, and Marco Viviani Università degli studi di Milano, Dipartimento

More information

Conception of Ontology for Security in Health Care Systems

Conception of Ontology for Security in Health Care Systems Conception of Ontology for Security in Health Care Systems Dr. J. Indumathi Department of Information Science and Technology, Anna University, Chennai, Tamilnadu, India Abstract- The insidious and omnipresent

More information

DATA MODELS FOR SEMISTRUCTURED DATA

DATA MODELS FOR SEMISTRUCTURED DATA Chapter 2 DATA MODELS FOR SEMISTRUCTURED DATA Traditionally, real world semantics are captured in a data model, and mapped to the database schema. The real world semantics are modeled as constraints and

More information

NOTES ON OBJECT-ORIENTED MODELING AND DESIGN

NOTES ON OBJECT-ORIENTED MODELING AND DESIGN NOTES ON OBJECT-ORIENTED MODELING AND DESIGN Stephen W. Clyde Brigham Young University Provo, UT 86402 Abstract: A review of the Object Modeling Technique (OMT) is presented. OMT is an object-oriented

More information

Semantic text features from small world graphs

Semantic text features from small world graphs Semantic text features from small world graphs Jurij Leskovec 1 and John Shawe-Taylor 2 1 Carnegie Mellon University, USA. Jozef Stefan Institute, Slovenia. jure@cs.cmu.edu 2 University of Southampton,UK

More information

ROUGH MEMBERSHIP FUNCTIONS: A TOOL FOR REASONING WITH UNCERTAINTY

ROUGH MEMBERSHIP FUNCTIONS: A TOOL FOR REASONING WITH UNCERTAINTY ALGEBRAIC METHODS IN LOGIC AND IN COMPUTER SCIENCE BANACH CENTER PUBLICATIONS, VOLUME 28 INSTITUTE OF MATHEMATICS POLISH ACADEMY OF SCIENCES WARSZAWA 1993 ROUGH MEMBERSHIP FUNCTIONS: A TOOL FOR REASONING

More information

UML-Based Conceptual Modeling of Pattern-Bases

UML-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 information

An Architecture for Semantic Enterprise Application Integration Standards

An 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 information

Graph Databases. Graph Databases. May 2015 Alberto Abelló & Oscar Romero

Graph Databases. Graph Databases. May 2015 Alberto Abelló & Oscar Romero Graph Databases 1 Knowledge Objectives 1. Describe what a graph database is 2. Explain the basics of the graph data model 3. Enumerate the best use cases for graph databases 4. Name two pros and cons of

More information

Chapter 8: Enhanced ER Model

Chapter 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 information

Ontology Matching with CIDER: Evaluation Report for the OAEI 2008

Ontology Matching with CIDER: Evaluation Report for the OAEI 2008 Ontology Matching with CIDER: Evaluation Report for the OAEI 2008 Jorge Gracia, Eduardo Mena IIS Department, University of Zaragoza, Spain {jogracia,emena}@unizar.es Abstract. Ontology matching, the task

More information

Benchmarking Database Representations of RDF/S Stores

Benchmarking Database Representations of RDF/S Stores Benchmarking Database Representations of RDF/S Stores Yannis Theoharis 1, Vassilis Christophides 1, Grigoris Karvounarakis 2 1 Computer Science Department, University of Crete and Institute of Computer

More information

Fuzzy Set-Theoretical Approach for Comparing Objects with Fuzzy Attributes

Fuzzy Set-Theoretical Approach for Comparing Objects with Fuzzy Attributes Fuzzy Set-Theoretical Approach for Comparing Objects with Fuzzy Attributes Y. Bashon, D. Neagu, M.J. Ridley Department of Computing University of Bradford Bradford, BD7 DP, UK e-mail: {Y.Bashon, D.Neagu,

More information

ResPubliQA 2010

ResPubliQA 2010 SZTAKI @ ResPubliQA 2010 David Mark Nemeskey Computer and Automation Research Institute, Hungarian Academy of Sciences, Budapest, Hungary (SZTAKI) Abstract. This paper summarizes the results of our first

More information

UBL Library Content Methodology

UBL Library Content Methodology UBL Library Content Methodology The purpose of this document is two-fold: 1. To explain how we got to where we are with the UBL vocabulary, we felt it necessary to provide a background to the rationale

More information

Knowledge discovery from XML Database

Knowledge discovery from XML Database Knowledge discovery from XML Database Pravin P. Chothe 1 Prof. S. V. Patil 2 Prof.S. H. Dinde 3 PG Scholar, ADCET, Professor, ADCET Ashta, Professor, SGI, Atigre, Maharashtra, India Maharashtra, India

More information

Ubiquitous Computing and Communication Journal (ISSN )

Ubiquitous Computing and Communication Journal (ISSN ) A STRATEGY TO COMPROMISE HANDWRITTEN DOCUMENTS PROCESSING AND RETRIEVING USING ASSOCIATION RULES MINING Prof. Dr. Alaa H. AL-Hamami, Amman Arab University for Graduate Studies, Amman, Jordan, 2011. Alaa_hamami@yahoo.com

More information

Extracting knowledge from Ontology using Jena for Semantic Web

Extracting knowledge from Ontology using Jena for Semantic Web Extracting knowledge from Ontology using Jena for Semantic Web Ayesha Ameen I.T Department Deccan College of Engineering and Technology Hyderabad A.P, India ameenayesha@gmail.com Khaleel Ur Rahman Khan

More information

Integrating Logistic Regression with Knowledge Discovery Systems

Integrating Logistic Regression with Knowledge Discovery Systems Association for Information Systems AIS Electronic Library (AISeL) AMCIS 1997 Proceedings Americas Conference on Information Systems (AMCIS) 8-15-1997 Integrating Logistic Regression with Knowledge Discovery

More information

Intelligent flexible query answering Using Fuzzy Ontologies

Intelligent flexible query answering Using Fuzzy Ontologies International Conference on Control, Engineering & Information Technology (CEIT 14) Proceedings - Copyright IPCO-2014, pp. 262-277 ISSN 2356-5608 Intelligent flexible query answering Using Fuzzy Ontologies

More information

Database Systems. A Practical Approach to Design, Implementation, and Management. Database Systems. Thomas Connolly Carolyn Begg

Database Systems. A Practical Approach to Design, Implementation, and Management. Database Systems. Thomas Connolly Carolyn Begg Database Systems A Practical Approach to Design, Implementation, and Management For these Global Editions, the editorial team at Pearson has collaborated with educators across the world to address a wide

More information

Which Role for an Ontology of Uncertainty?

Which Role for an Ontology of Uncertainty? Which Role for an Ontology of Uncertainty? Paolo Ceravolo, Ernesto Damiani, Marcello Leida Dipartimento di Tecnologie dell Informazione - Università degli studi di Milano via Bramante, 65-26013 Crema (CR),

More information

Hierarchical Clustering of Process Schemas

Hierarchical Clustering of Process Schemas Hierarchical Clustering of Process Schemas Claudia Diamantini, Domenico Potena Dipartimento di Ingegneria Informatica, Gestionale e dell'automazione M. Panti, Università Politecnica delle Marche - via

More information

Rough Approximations under Level Fuzzy Sets

Rough Approximations under Level Fuzzy Sets Rough Approximations under Level Fuzzy Sets W.-N. Liu J.T. Yao Y.Y.Yao Department of Computer Science, University of Regina Regina, Saskatchewan, Canada S4S 0A2 E-mail: [liuwe200, jtyao, yyao]@cs.uregina.ca

More information

MODELLING DOCUMENT CATEGORIES BY EVOLUTIONARY LEARNING OF TEXT CENTROIDS

MODELLING DOCUMENT CATEGORIES BY EVOLUTIONARY LEARNING OF TEXT CENTROIDS MODELLING DOCUMENT CATEGORIES BY EVOLUTIONARY LEARNING OF TEXT CENTROIDS J.I. Serrano M.D. Del Castillo Instituto de Automática Industrial CSIC. Ctra. Campo Real km.0 200. La Poveda. Arganda del Rey. 28500

More information

The Open Group SOA Ontology Technical Standard. Clive Hatton

The Open Group SOA Ontology Technical Standard. Clive Hatton The Open Group SOA Ontology Technical Standard Clive Hatton The Open Group Releases SOA Ontology Standard To Increase SOA Adoption and Success Rates Ontology Fosters Common Understanding of SOA Concepts

More information

Fuzzy Multilevel Graph Embedding for Recognition, Indexing and Retrieval of Graphic Document Images

Fuzzy Multilevel Graph Embedding for Recognition, Indexing and Retrieval of Graphic Document Images Cotutelle PhD thesis for Recognition, Indexing and Retrieval of Graphic Document Images presented by Muhammad Muzzamil LUQMAN mluqman@{univ-tours.fr, cvc.uab.es} Friday, 2 nd of March 2012 Directors of

More information

A Decision-Theoretic Rough Set Model

A Decision-Theoretic Rough Set Model A Decision-Theoretic Rough Set Model Yiyu Yao and Jingtao Yao Department of Computer Science University of Regina Regina, Saskatchewan, Canada S4S 0A2 {yyao,jtyao}@cs.uregina.ca Special Thanks to Professor

More information

XML Data in (Object-) Relational Databases

XML Data in (Object-) Relational Databases XML Data in (Object-) Relational Databases RNDr. Irena Mlýnková irena.mlynkova@mff.cuni.cz Charles University Faculty of Mathematics and Physics Department of Software Engineering Prague, Czech Republic

More information

Target-driven merging of Taxonomies

Target-driven merging of Taxonomies Target-driven merging of Taxonomies Salvatore Raunich, Erhard Rahm University of Leipzig Germany {raunich, rahm}@informatik.uni-leipzig.de Abstract The proliferation of ontologies and taxonomies in many

More information

Monotone Constraints in Frequent Tree Mining

Monotone Constraints in Frequent Tree Mining Monotone Constraints in Frequent Tree Mining Jeroen De Knijf Ad Feelders Abstract Recent studies show that using constraints that can be pushed into the mining process, substantially improves the performance

More information

Inference in Hierarchical Multidimensional Space

Inference in Hierarchical Multidimensional Space Proc. International Conference on Data Technologies and Applications (DATA 2012), Rome, Italy, 25-27 July 2012, 70-76 Related papers: http://conceptoriented.org/ Inference in Hierarchical Multidimensional

More information

Fausto Giunchiglia and Mattia Fumagalli

Fausto Giunchiglia and Mattia Fumagalli DISI - Via Sommarive 5-38123 Povo - Trento (Italy) http://disi.unitn.it FROM ER MODELS TO THE ENTITY MODEL Fausto Giunchiglia and Mattia Fumagalli Date (2014-October) Technical Report # DISI-14-014 From

More information

Information Granulation and Approximation in a Decision-theoretic Model of Rough Sets

Information Granulation and Approximation in a Decision-theoretic Model of Rough Sets Information Granulation and Approximation in a Decision-theoretic Model of Rough Sets Y.Y. Yao Department of Computer Science University of Regina Regina, Saskatchewan Canada S4S 0A2 E-mail: yyao@cs.uregina.ca

More information

Device Independent Principles for Adapted Content Delivery

Device Independent Principles for Adapted Content Delivery Device Independent Principles for Adapted Content Delivery Tayeb Lemlouma 1 and Nabil Layaïda 2 OPERA Project Zirst 655 Avenue de l Europe - 38330 Montbonnot, Saint Martin, France Tel: +33 4 7661 5281

More information

Generating Cross level Rules: An automated approach

Generating Cross level Rules: An automated approach Generating Cross level Rules: An automated approach Ashok 1, Sonika Dhingra 1 1HOD, Dept of Software Engg.,Bhiwani Institute of Technology, Bhiwani, India 1M.Tech Student, Dept of Software Engg.,Bhiwani

More information

This tutorial has been prepared for computer science graduates to help them understand the basic-to-advanced concepts related to data mining.

This tutorial has been prepared for computer science graduates to help them understand the basic-to-advanced concepts related to data mining. About the Tutorial Data Mining is defined as the procedure of extracting information from huge sets of data. In other words, we can say that data mining is mining knowledge from data. The tutorial starts

More information

Improving Adaptive Hypermedia by Adding Semantics

Improving Adaptive Hypermedia by Adding Semantics Improving Adaptive Hypermedia by Adding Semantics Anton ANDREJKO Slovak University of Technology Faculty of Informatics and Information Technologies Ilkovičova 3, 842 16 Bratislava, Slovak republic andrejko@fiit.stuba.sk

More information

Modelling Structures in Data Mining Techniques

Modelling Structures in Data Mining Techniques Modelling Structures in Data Mining Techniques Ananth Y N 1, Narahari.N.S 2 Associate Professor, Dept of Computer Science, School of Graduate Studies- JainUniversity- J.C.Road, Bangalore, INDIA 1 Professor

More information

Ontology based Model and Procedure Creation for Topic Analysis in Chinese Language

Ontology based Model and Procedure Creation for Topic Analysis in Chinese Language Ontology based Model and Procedure Creation for Topic Analysis in Chinese Language Dong Han and Kilian Stoffel Information Management Institute, University of Neuchâtel Pierre-à-Mazel 7, CH-2000 Neuchâtel,

More information

Enhanced Entity-Relationship (EER) Modeling

Enhanced Entity-Relationship (EER) Modeling CHAPTER 4 Enhanced Entity-Relationship (EER) Modeling Copyright 2017 Ramez Elmasri and Shamkant B. Navathe Slide 1-2 Chapter Outline EER stands for Enhanced ER or Extended ER EER Model Concepts Includes

More information

Granular Computing: A Paradigm in Information Processing Saroj K. Meher Center for Soft Computing Research Indian Statistical Institute, Kolkata

Granular Computing: A Paradigm in Information Processing Saroj K. Meher Center for Soft Computing Research Indian Statistical Institute, Kolkata Granular Computing: A Paradigm in Information Processing Saroj K. Meher Center for Soft Computing Research Indian Statistical Institute, Kolkata Granular computing (GrC): Outline Introduction Definitions

More information

VISO: A Shared, Formal Knowledge Base as a Foundation for Semi-automatic InfoVis Systems

VISO: A Shared, Formal Knowledge Base as a Foundation for Semi-automatic InfoVis Systems VISO: A Shared, Formal Knowledge Base as a Foundation for Semi-automatic InfoVis Systems Jan Polowinski Martin Voigt Technische Universität DresdenTechnische Universität Dresden 01062 Dresden, Germany

More information

The Encoding Complexity of Network Coding

The Encoding Complexity of Network Coding The Encoding Complexity of Network Coding Michael Langberg Alexander Sprintson Jehoshua Bruck California Institute of Technology Email: mikel,spalex,bruck @caltech.edu Abstract In the multicast network

More information

MMT Objects. Florian Rabe. Computer Science, Jacobs University, Bremen, Germany

MMT Objects. Florian Rabe. Computer Science, Jacobs University, Bremen, Germany MMT Objects Florian Rabe Computer Science, Jacobs University, Bremen, Germany Abstract Mmt is a mathematical knowledge representation language, whose object layer is strongly inspired by OpenMath. In fact,

More information

Lesson 5 Web Service Interface Definition (Part II)

Lesson 5 Web Service Interface Definition (Part II) Lesson 5 Web Service Interface Definition (Part II) Service Oriented Architectures Security Module 1 - Basic technologies Unit 3 WSDL Ernesto Damiani Università di Milano Controlling the style (1) The

More information

Hierarchies in a multidimensional model: From conceptual modeling to logical representation

Hierarchies in a multidimensional model: From conceptual modeling to logical representation Data & Knowledge Engineering 59 (2006) 348 377 www.elsevier.com/locate/datak Hierarchies in a multidimensional model: From conceptual modeling to logical representation E. Malinowski *, E. Zimányi Department

More information

SIMILARITY MEASURES FOR MULTI-VALUED ATTRIBUTES FOR DATABASE CLUSTERING

SIMILARITY MEASURES FOR MULTI-VALUED ATTRIBUTES FOR DATABASE CLUSTERING SIMILARITY MEASURES FOR MULTI-VALUED ATTRIBUTES FOR DATABASE CLUSTERING TAE-WAN RYU AND CHRISTOPH F. EICK Department of Computer Science, University of Houston, Houston, Texas 77204-3475 {twryu, ceick}@cs.uh.edu

More information

A FUZZY BASED APPROACH TO TEXT MINING AND DOCUMENT CLUSTERING

A FUZZY BASED APPROACH TO TEXT MINING AND DOCUMENT CLUSTERING A FUZZY BASED APPROACH TO TEXT MINING AND DOCUMENT CLUSTERING Sumit Goswami 1 and Mayank Singh Shishodia 2 1 Indian Institute of Technology-Kharagpur, Kharagpur, India sumit_13@yahoo.com 2 School of Computer

More information

Joint Entity Resolution

Joint Entity Resolution Joint Entity Resolution Steven Euijong Whang, Hector Garcia-Molina Computer Science Department, Stanford University 353 Serra Mall, Stanford, CA 94305, USA {swhang, hector}@cs.stanford.edu No Institute

More information

XML ALONE IS NOT SUFFICIENT FOR EFFECTIVE WEBEDI

XML 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 information

Graph Databases. Guilherme Fetter Damasio. University of Ontario Institute of Technology and IBM Centre for Advanced Studies IBM Corporation

Graph Databases. Guilherme Fetter Damasio. University of Ontario Institute of Technology and IBM Centre for Advanced Studies IBM Corporation Graph Databases Guilherme Fetter Damasio University of Ontario Institute of Technology and IBM Centre for Advanced Studies Outline Introduction Relational Database Graph Database Our Research 2 Introduction

More information

Development of Contents Management System Based on Light-Weight Ontology

Development of Contents Management System Based on Light-Weight Ontology Development of Contents Management System Based on Light-Weight Ontology Kouji Kozaki, Yoshinobu Kitamura, and Riichiro Mizoguchi Abstract In the Structuring Nanotechnology Knowledge project, a material-independent

More information

A multi-step attack-correlation method with privacy protection

A multi-step attack-correlation method with privacy protection A multi-step attack-correlation method with privacy protection Research paper A multi-step attack-correlation method with privacy protection ZHANG Yongtang 1, 2, LUO Xianlu 1, LUO Haibo 1 1. Department

More information

Linguistic Values on Attribute Subdomains in Vague Database Querying

Linguistic Values on Attribute Subdomains in Vague Database Querying Linguistic Values on Attribute Subdomains in Vague Database Querying CORNELIA TUDORIE Department of Computer Science and Engineering University "Dunărea de Jos" Domnească, 82 Galaţi ROMANIA Abstract: -

More information

Inheritance Metrics: What do they Measure?

Inheritance Metrics: What do they Measure? Inheritance Metrics: What do they Measure? G. Sri Krishna and Rushikesh K. Joshi Department of Computer Science and Engineering Indian Institute of Technology Bombay Mumbai, 400 076, India Email:{srikrishna,rkj}@cse.iitb.ac.in

More information

An overview of Graph Categories and Graph Primitives

An overview of Graph Categories and Graph Primitives An overview of Graph Categories and Graph Primitives Dino Ienco (dino.ienco@irstea.fr) https://sites.google.com/site/dinoienco/ Topics I m interested in: Graph Database and Graph Data Mining Social Network

More information

Semantic Web. Ontology Alignment. Morteza Amini. Sharif University of Technology Fall 95-96

Semantic Web. Ontology Alignment. Morteza Amini. Sharif University of Technology Fall 95-96 ه عا ی Semantic Web Ontology Alignment Morteza Amini Sharif University of Technology Fall 95-96 Outline The Problem of Ontologies Ontology Heterogeneity Ontology Alignment Overall Process Similarity (Matching)

More information

Foundations of SPARQL Query Optimization

Foundations of SPARQL Query Optimization Foundations of SPARQL Query Optimization Michael Schmidt, Michael Meier, Georg Lausen Albert-Ludwigs-Universität Freiburg Database and Information Systems Group 13 th International Conference on Database

More information

Hierarchical Online Mining for Associative Rules

Hierarchical Online Mining for Associative Rules Hierarchical Online Mining for Associative Rules Naresh Jotwani Dhirubhai Ambani Institute of Information & Communication Technology Gandhinagar 382009 INDIA naresh_jotwani@da-iict.org Abstract Mining

More information

BMatch: A Quality/Performance Balanced Approach for Large Scale Schema Matching

BMatch: A Quality/Performance Balanced Approach for Large Scale Schema Matching BMatch: A Quality/Performance Balanced Approach for Large Scale Schema Matching Fabien Duchateau 1 and Zohra Bellahsene 1 and Mathieu Roche 1 LIRMM - Université Montpellier 2 161 rue Ada 34000 Montpellier,

More information

Introduction to Machine Learning. Xiaojin Zhu

Introduction to Machine Learning. Xiaojin Zhu Introduction to Machine Learning Xiaojin Zhu jerryzhu@cs.wisc.edu Read Chapter 1 of this book: Xiaojin Zhu and Andrew B. Goldberg. Introduction to Semi- Supervised Learning. http://www.morganclaypool.com/doi/abs/10.2200/s00196ed1v01y200906aim006

More information

HOT asax: A Novel Adaptive Symbolic Representation for Time Series Discords Discovery

HOT asax: A Novel Adaptive Symbolic Representation for Time Series Discords Discovery HOT asax: A Novel Adaptive Symbolic Representation for Time Series Discords Discovery Ninh D. Pham, Quang Loc Le, Tran Khanh Dang Faculty of Computer Science and Engineering, HCM University of Technology,

More information

Evaluation Measures. Sebastian Pölsterl. April 28, Computer Aided Medical Procedures Technische Universität München

Evaluation Measures. Sebastian Pölsterl. April 28, Computer Aided Medical Procedures Technische Universität München Evaluation Measures Sebastian Pölsterl Computer Aided Medical Procedures Technische Universität München April 28, 2015 Outline 1 Classification 1. Confusion Matrix 2. Receiver operating characteristics

More information

A B2B Search Engine. Abstract. Motivation. Challenges. Technical Report

A B2B Search Engine. Abstract. Motivation. Challenges. Technical Report Technical Report A B2B Search Engine Abstract In this report, we describe a business-to-business search engine that allows searching for potential customers with highly-specific queries. Currently over

More information

Reducing Quantization Error and Contextual Bias Problems in Object-Oriented Methods by Applying Fuzzy-Logic Techniques

Reducing Quantization Error and Contextual Bias Problems in Object-Oriented Methods by Applying Fuzzy-Logic Techniques Reducing Quantization Error and Contextual Bias Problems in Object-Oriented Methods by Applying Fuzzy-Logic Techniques Mehmet Aksit and Francesco Marcelloni TRESE project, Department of Computer Science,

More information

detected inference channel is eliminated by redesigning the database schema [Lunt, 1989] or upgrading the paths that lead to the inference [Stickel, 1

detected inference channel is eliminated by redesigning the database schema [Lunt, 1989] or upgrading the paths that lead to the inference [Stickel, 1 THE DESIGN AND IMPLEMENTATION OF A DATA LEVEL DATABASE INFERENCE DETECTION SYSTEM Raymond W. Yip and Karl N. Levitt Abstract: Inference is a way tosubvert access control mechanisms of database systems.

More information

Theme Identification in RDF Graphs

Theme Identification in RDF Graphs Theme Identification in RDF Graphs Hanane Ouksili PRiSM, Univ. Versailles St Quentin, UMR CNRS 8144, Versailles France hanane.ouksili@prism.uvsq.fr Abstract. An increasing number of RDF datasets is published

More information

Value Added Association Rules

Value Added Association Rules Value Added Association Rules T.Y. Lin San Jose State University drlin@sjsu.edu Glossary Association Rule Mining A Association Rule Mining is an exploratory learning task to discover some hidden, dependency

More information

Information Management (IM)

Information Management (IM) 1 2 3 4 5 6 7 8 9 Information Management (IM) Information Management (IM) is primarily concerned with the capture, digitization, representation, organization, transformation, and presentation of information;

More information

Automation 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 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 information

Knowledge libraries and information space

Knowledge libraries and information space University of Wollongong Research Online University of Wollongong Thesis Collection 1954-2016 University of Wollongong Thesis Collections 2009 Knowledge libraries and information space Eric Rayner University

More information

Knowledge Discovery and Data Mining 1 (VO) ( )

Knowledge Discovery and Data Mining 1 (VO) ( ) Knowledge Discovery and Data Mining 1 (VO) (707.003) Data Matrices and Vector Space Model Denis Helic KTI, TU Graz Nov 6, 2014 Denis Helic (KTI, TU Graz) KDDM1 Nov 6, 2014 1 / 55 Big picture: KDDM Probability

More information

Introducing fuzzy quantification in OWL 2 ontologies

Introducing fuzzy quantification in OWL 2 ontologies Introducing fuzzy quantification in OWL 2 ontologies Francesca Alessandra Lisi and Corrado Mencar Dipartimento di Informatica, Centro Interdipartimentale di Logica e Applicazioni Università degli Studi

More information

The k-means Algorithm and Genetic Algorithm

The k-means Algorithm and Genetic Algorithm The k-means Algorithm and Genetic Algorithm k-means algorithm Genetic algorithm Rough set approach Fuzzy set approaches Chapter 8 2 The K-Means Algorithm The K-Means algorithm is a simple yet effective

More information

H1 Spring C. A service-oriented architecture is frequently deployed in practice without a service registry

H1 Spring C. A service-oriented architecture is frequently deployed in practice without a service registry 1. (12 points) Identify all of the following statements that are true about the basics of services. A. Screen scraping may not be effective for large desktops but works perfectly on mobile phones, because

More information

Fuzzy Partitioning with FID3.1

Fuzzy Partitioning with FID3.1 Fuzzy Partitioning with FID3.1 Cezary Z. Janikow Dept. of Mathematics and Computer Science University of Missouri St. Louis St. Louis, Missouri 63121 janikow@umsl.edu Maciej Fajfer Institute of Computing

More information

VIRTUAL PATH LAYOUT DESIGN VIA NETWORK CLUSTERING

VIRTUAL PATH LAYOUT DESIGN VIA NETWORK CLUSTERING VIRTUAL PATH LAYOUT DESIGN VIA NETWORK CLUSTERING MASSIMO ANCONA 1 WALTER CAZZOLA 2 PAOLO RAFFO 3 IOAN BOGDAN VASIAN 4 Area of interest: Telecommunication Networks Management Abstract In this paper two

More information

Proposal for Implementing Linked Open Data on Libraries Catalogue

Proposal 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 information

Chapter 6: Information Retrieval and Web Search. An introduction

Chapter 6: Information Retrieval and Web Search. An introduction Chapter 6: Information Retrieval and Web Search An introduction Introduction n Text mining refers to data mining using text documents as data. n Most text mining tasks use Information Retrieval (IR) methods

More information

Main topics: Presenter: Introduction to OWL Protégé, an ontology editor OWL 2 Semantic reasoner Summary TDT OWL

Main topics: Presenter: Introduction to OWL Protégé, an ontology editor OWL 2 Semantic reasoner Summary TDT OWL 1 TDT4215 Web Intelligence Main topics: Introduction to Web Ontology Language (OWL) Presenter: Stein L. Tomassen 2 Outline Introduction to OWL Protégé, an ontology editor OWL 2 Semantic reasoner Summary

More information

Hierarchical Document Clustering

Hierarchical Document Clustering Hierarchical Document Clustering Benjamin C. M. Fung, Ke Wang, and Martin Ester, Simon Fraser University, Canada INTRODUCTION Document clustering is an automatic grouping of text documents into clusters

More information

Semi-Supervised Clustering with Partial Background Information

Semi-Supervised Clustering with Partial Background Information Semi-Supervised Clustering with Partial Background Information Jing Gao Pang-Ning Tan Haibin Cheng Abstract Incorporating background knowledge into unsupervised clustering algorithms has been the subject

More information

SRM UNIVERSITY. : Batch1: TP1102 Batch2: TP406

SRM UNIVERSITY. : Batch1: TP1102 Batch2: TP406 1 SRM UNIVERSITY FACULTY OF ENGINEERING AND TECHNOLOGY SCHOOL OF COMPUTING DEPARTMENT OF COMPUTERSCIENCE AND ENGINEERING COURSE PLAN Course Code Course Title Semester : 15CS424E : SEMANTIC WEB : V Course

More information

Investigating F# as a development tool for distributed multi-agent systems

Investigating F# as a development tool for distributed multi-agent systems PROCEEDINGS OF THE WORKSHOP ON APPLICATIONS OF SOFTWARE AGENTS ISBN 978-86-7031-188-6, pp. 32-36, 2011 Investigating F# as a development tool for distributed multi-agent systems Extended abstract Alex

More information

Leveraging Data and Structure in Ontology Integration

Leveraging Data and Structure in Ontology Integration Leveraging Data and Structure in Ontology Integration O. Udrea L. Getoor R.J. Miller Group 15 Enrico Savioli Andrea Reale Andrea Sorbini DEIS University of Bologna Searching Information in Large Spaces

More information

FUZZY LOGIC WITH ENGINEERING APPLICATIONS

FUZZY LOGIC WITH ENGINEERING APPLICATIONS FUZZY LOGIC WITH ENGINEERING APPLICATIONS Third Edition Timothy J. Ross University of New Mexico, USA A John Wiley and Sons, Ltd., Publication FUZZY LOGIC WITH ENGINEERING APPLICATIONS Third Edition FUZZY

More information

K-Nearest-Neighbours with a Novel Similarity Measure for Intrusion Detection

K-Nearest-Neighbours with a Novel Similarity Measure for Intrusion Detection K-Nearest-Neighbours with a Novel Similarity Measure for Intrusion Detection Zhenghui Ma School of Computer Science The University of Birmingham Edgbaston, B15 2TT Birmingham, UK Ata Kaban School of Computer

More information

Lecture Telecooperation. D. Fensel Leopold-Franzens- Universität Innsbruck

Lecture 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 information

Computer Vision Group Prof. Daniel Cremers. 4. Probabilistic Graphical Models Directed Models

Computer Vision Group Prof. Daniel Cremers. 4. Probabilistic Graphical Models Directed Models Prof. Daniel Cremers 4. Probabilistic Graphical Models Directed Models The Bayes Filter (Rep.) (Bayes) (Markov) (Tot. prob.) (Markov) (Markov) 2 Graphical Representation (Rep.) We can describe the overall

More information

Nearest Neighbor Search by Branch and Bound

Nearest Neighbor Search by Branch and Bound Nearest Neighbor Search by Branch and Bound Algorithmic Problems Around the Web #2 Yury Lifshits http://yury.name CalTech, Fall 07, CS101.2, http://yury.name/algoweb.html 1 / 30 Outline 1 Short Intro to

More information

A Hierarchical Document Clustering Approach with Frequent Itemsets

A Hierarchical Document Clustering Approach with Frequent Itemsets A Hierarchical Document Clustering Approach with Frequent Itemsets Cheng-Jhe Lee, Chiun-Chieh Hsu, and Da-Ren Chen Abstract In order to effectively retrieve required information from the large amount of

More information

XML RETRIEVAL. Introduction to Information Retrieval CS 150 Donald J. Patterson

XML RETRIEVAL. Introduction to Information Retrieval CS 150 Donald J. Patterson Introduction to Information Retrieval CS 150 Donald J. Patterson Content adapted from Manning, Raghavan, and Schütze http://www.informationretrieval.org OVERVIEW Introduction Basic XML Concepts Challenges

More information

A JAVA-BASED SYSTEM FOR XML DATA PROTECTION* E. Bertino, M. Braun, S. Castano, E. Ferrari, M. Mesiti

A JAVA-BASED SYSTEM FOR XML DATA PROTECTION* E. Bertino, M. Braun, S. Castano, E. Ferrari, M. Mesiti CHAPTER 2 Author- A JAVA-BASED SYSTEM FOR XML DATA PROTECTION* E. Bertino, M. Braun, S. Castano, E. Ferrari, M. Mesiti Abstract Author- is a Java-based system for access control to XML documents. Author-

More information

Creating Ontology Chart Using Economy Domain Ontologies

Creating Ontology Chart Using Economy Domain Ontologies Creating Ontology Chart Using Economy Domain Ontologies Waralak V. Siricharoen *1, Thitima Puttitanun *2 *1, Corresponding author School of Science, University of the Thai Chamber of Commerce, 126/1, Dindeang,

More information

in this web service Cambridge University Press

in this web service Cambridge University Press 978-0-51-85748- - Switching and Finite Automata Theory, Third Edition Part 1 Preliminaries 978-0-51-85748- - Switching and Finite Automata Theory, Third Edition CHAPTER 1 Number systems and codes This

More information

Extending E-R for Modelling XML Keys

Extending E-R for Modelling XML Keys Extending E-R for Modelling XML Keys Martin Necasky Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic martin.necasky@mff.cuni.cz Jaroslav Pokorny Faculty of Mathematics and

More information

Copyright 2016 Ramez Elmasri and Shamkant B. Navathe

Copyright 2016 Ramez Elmasri and Shamkant B. Navathe CHAPTER 4 Enhanced Entity-Relationship (EER) Modeling Slide 1-2 Chapter Outline EER stands for Enhanced ER or Extended ER EER Model Concepts Includes all modeling concepts of basic ER Additional concepts:

More information

UserMap an Enhancing of User-Driven XML-to-Relational Mapping Strategies

UserMap an Enhancing of User-Driven XML-to-Relational Mapping Strategies UserMap an Enhancing of User-Driven XML-to-Relational Mapping Strategies (Technical Report) Irena Mlynkova Charles University Faculty of Mathematics and Physics Department of Software Engineering Malostranske

More information

Part II Workflow discovery algorithms

Part II Workflow discovery algorithms Process Mining Part II Workflow discovery algorithms Induction of Control-Flow Graphs α-algorithm Heuristic Miner Fuzzy Miner Outline Part I Introduction to Process Mining Context, motivation and goal

More information