Top PDF Semantic Query Optimisation with Ontology Simulation

Semantic Query Optimisation with Ontology Simulation

Semantic Query Optimisation with Ontology Simulation

This paper focuses on how the future web might look like and the interaction between the sources of information to yield perfect and real time results with unique power of intelligence by interpreting the best possible solution for the query. Ontologies are the bases of semantic web and can be further expanded. An example ontology was simulated using protégé and results were analysed. The keywords were given priority and bases on that we have discarded certain percentage of web pages hence making the search more compact and efficient. The main idea is to collaborate the search for Indian Universities to be more informative and provide it an intellicense in order to retrieve user oriented results. The future scope of this method is that Firstly, the current ontology can be integrated into large domains resulting in expansion of knowledge base. Secondly, Information of all the universities and their corresponding colleges will be assorted under one ontology hence interoperability as well as interpretability. Thirdly, The web will be a collection of databases with a common vocabulary for exchanging and interpreting information between machines.
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Uncertainty modeling process for semantic technology

Uncertainty modeling process for semantic technology

The process of creating and using a probabilistic ontology typically occurs in three stages: first is modeling the domain; next is populating the model with situation-specific information; and third is using the model and situation-specific information for reasoning. Modeling a domain means constructing a representation of aspects of the domain for purposes of understanding, explaining, predicting, or simulating those aspects. For our purposes, the model represents the kinds of entities that can exist in the domain, their attributes, the relationships they can have to each other, the processes in which they can participate, and the rules that govern their behavior. It also includes uncertainties about all these aspects. There are many sources of uncertainty: e.g., causes may be non- deterministically related to their effects; events may be only indirectly observable through noisy channels; association of observations to the generating events may be unknown; phenomena in the domain may be subject to statistical fluctuation; the structure of and associations among domain entities may exhibit substantial variation; and/or the future behavior of domain entities may be imperfectly predictable (e.g., Schum & Starace, 2001; Laskey & Laskey, 2008; Costa et al., 2012). Once these and other relevant sources of uncertainty are captured in a domain model, the model can be applied to a specific situation by populating it with data about the situation. Finally, the inference engine can be called upon to answer queries about the specific situation. Unlike traditional semantic systems that can handle only deterministic queries, queries with a probabilistic ontology can return soft results. For example, consider a query about whether an inappropriate relationship exists between a procurement official and a bidder. A reasoning system for a standard ontology can return only procurements in which such a relationship can be proven, while a reasoner for a probabilistic ontology can return a probability that such a relationship exists.
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Developing an University Ontology in Education Domain using Protégé for Semantic Web

Developing an University Ontology in Education Domain using Protégé for Semantic Web

A number of ontology editors are available for developing an ontology, e.g, Protégé, SWOOP, OntoEdit, Altova SemanticWorks, OntoStudio, and hence forth. But Protégé is most widely used by researchers, professionals, programmers, and others alike [6]. Protégé is a an open source freely available ontology editor and knowledge base framework [7]. According to a survey conducted by Cardoso, Protégé editor and Education are most widely used for the development of ontologies (see Fig. 1(a) and Fig.1(b)). In this paper, Section 2 highlights Ontology development in education domain. Section 3 illustrates the development of Indraprastha University(Delhi, India) Ontology using protégé editor. It presents the query retrieval using query tab of protégé and also illustrates the TGviz tab which is used to provide the route of the ontology with a graph to reach to any classes or subclasses. C onstruction of a class hierarchy (also called taxonomy) for the University domain is illustrated with the help of code snippets of XML, RDF, and OWL. Section 4 concludes the paper with future scope.
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A Query-specific Reasoning Method for Inconsistent and Uncertain Ontology

A Query-specific Reasoning Method for Inconsistent and Uncertain Ontology

In the area of reasoning with probabilities in knowledge representation, [6] presented a probabilistic extension of the Ontology Language OWL which relied on Bayesian Networks for reasoning. Fuzzy extensions of OWL have been proposed in [7, 8]. [9] defined a confidence function to handle the uncertain ontologies. [10] applied possibility extension on the description logic to achieve reasoning for uncertain ontology. This method removes elements with low certainty degree by using a decrement function, but they don't deal with the "False" answer of queries. [11] defined a vague ontology for semantic information retrieval, which took uncertainty issue of ontologies into consideration, though it didn’t provide a specific solution to solve the issue.
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A New Semantic Similarity Metric for Solving Sparse Data Problem in Ontology based Information Retrieval System

A New Semantic Similarity Metric for Solving Sparse Data Problem in Ontology based Information Retrieval System

This paper has discussed the various approaches that could be used for finding similar concepts in an ontology and between ontologies. We have done a survey to exploit the similarity methods for ontology based query expansion to aid better retrieval effectiveness of Information retrieval models. The experiments conducted by early researches provide better correlation values which gives promising direction of using them in Ontology based retrieval models. A new semantic similarity metric has been introduced which overcomes the shortcomings of existing semantic similarity methods mainly sparse data problem. We are working with the new similarity function which will combine the advantages of the similarity methods discussed in this paper and we will test it with ontologies of particular domain.
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The chemical information ontology: provenance and disambiguation for chemical data on the biological semantic web.

The chemical information ontology: provenance and disambiguation for chemical data on the biological semantic web.

However, as is often the case during the development of a new scientific discipline, this terminology has been developed and formalised by many different groups in many different publications and other forms of communication, creating redundancy, ambiguity and ‘silos’ in the eventual terminological system. While this was not so much of a problem as long as all chemical data was locked away behind commercial firewalls, and each individual company working with chemical data had the task of standardising its own internal terminology, in recent years the tide has started to shift towards open data and open algorithms and toolkits in the chemistry domain. In particular, we are seeing the advent of the Semantic Web [21], a set of standards for representing, publishing, sharing, reusing, querying and reasoning about data using web technologies. Cheminformatics data is being brought onto the semantic web in larger and larger volumes [22]. Bringing data onto the semantic web allows it to be used for data-driven research remotely, without the data having to be downloaded and stored locally on the researcher’s machine. Semantic web-enabled software fetches desired data from distributed repositories that support cross-resource query answering over heterogeneous data sources.
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Comparison of Question Answering Systems Based on Ontology and Semantic Web in Different Environment

Comparison of Question Answering Systems Based on Ontology and Semantic Web in Different Environment

AquaLog (Lopez et al., 2007) is capable of learning the user's jargon in order to improve hisexperience by the time. Their learning mechanism is good in a way that it usesontology reasoning to learn more generic patterns, which could then be reusedfor the questions with similar context. In this system two major models are used as Linguistic Component which is used to convert the NL questions into Query-triple format and Relation Similarity Service (RSS) which takes Query Triple form into Onto-Triple form. The data model is triple based like {Subject, Predicate, Object} type. The Performance is based on Precision, Recall and also failure types are referred separately. At average 63.5 % of successive answers are retrieved from ontology with closed domain environment.
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DAE Tools: equation-based object-oriented modelling, simulation and optimisation software

DAE Tools: equation-based object-oriented modelling, simulation and optimisation software

In this work, DAE Tools modelling, simulation and optimisation software, its programming paradigms and main features are presented. The current approaches to mathematical modelling such as the use of modelling languages and general-purpose programming languages are analysed. The common set of capabilities required by the typical simulation software are discussed, and the shortcomings of the current approaches recognised. A new hybrid approach is introduced, and the modelling languages and the hybrid approach are compared in terms of the grammar, compiler, parser and interpreter requirements, maintainability and portability. The most important characteristics of the new approach are discussed, such as: (1) support for the runtime model generation; (2) support for the runtime simulation set-up; (3) support for complex runtime operating procedures; (4) interoperability with the third party software packages (i.e. NumPy/SciPy); (5) suitability for embedding and use as a web application or software as a service; and (6) code-generation, model exchange and co-simulation capabilities. The benefits of an equation-based approach to modelling, implemented in a fourth generation object-oriented general purpose programming language such as Python are discussed. The architecture and the software implementation details as well as the type of problems that can be solved using DAE Tools software are described. Finally, some applications of the software at different levels of abstraction are presented, and its embedding capabilities and suitability for use as a software as a service is demonstrated.
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A Query Tool for EL with Non-Monotonic Rules

A Query Tool for EL with Non-Monotonic Rules

Finally, we tested the querying time. To this purpose, we randomly generated and handcrafted several queries of different sizes and shapes, some satisfiable while others not, using SNOMED CT with a varying number of non-monotonic rules as described in the previous experiment. More concretely, we tested queries ranging from single atoms to complex conjunctive queries. We also varied the depth of classes and properties in the hierarchies, how many sub-elements the considered classes/properties have themselves (the more they have, the more answers are returned w.r.t. the arbitrarily many created rules and facts), and how variables are connected. E.g., we considered sequences of atoms without any (positive variable) connection between them, thus creating all com- binations of the answers for each atom, but also queries in which properties (or new predicates of arity greater 1) introduce connections or where the same variable appears in different queried classes. In all cases, we observed that the query response time is interactive, mostly significantly below one second, observing longer reply times only if the number of replies is very high because either the queried class contains many sub- classes in the hierarchy or if the arbitrarily generated rules create too many meaningless links, thus in the worst case requiring to compute the entire model. Requesting only one solution avoids this problem. Additionally, the question of realistic randomly generated rule bodies for testing querying time remain an issue of future work.
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Source Code Plagiarism Detection Method Using Protégé Built Ontologies

Source Code Plagiarism Detection Method Using Protégé Built Ontologies

Software plagiarism is a growing and serious problem that affects computer science universities in particular and the quality of education in general. More and more students tend to copy their thesis ’s software from older theses or internet databases. Checking source codes manually, to detect if they are similar or the same, is a laborious and time consuming job, maybe even impossible due to existence of large digital repositories. Ontology is a way of describing a document’s semantic, so it can be easily used for source code files too. OWL Web Ontology Language could find its applicability in describing both vocabulary and taxonomy of a programming language source code. SPARQL is a query language based on SQL that extracts saved or deducted information from ontologies. Our paper proposes a source code plagiarism detection method, based on ontologies created using Protégé editor, which can be applied in scanning students’ theses’ software source code.
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Image summarisation: human action description from static images

Image summarisation: human action description from static images

Some years later, in 1998, Dirk Voelz, Elisabeth André, Gerd Herzog and Thomas Rist[12] implemented an automatic commentator for the robot soccer games also know as "RoboCup" games. Their system is called Rocco and can generate TV-style live reports for arbitrary matches of the "RoboCup" simulator league. The systems consists of an event recognition subsystem and a report planner. They try to convey emotions and use a discourse planner. This approach like the previous one is not just refined to textual representation but a multimedia generation system. Further work of Gerd Herzog with Karl Rohr[5] in 1995 is the implementation of a system for automatic simultaneous description of human movements in real world image sequences. The system combines VITRA, a natural language access system with a vision system. A model-based approach is used for recognising human movements and it is implemented in two stages, the initialisation stage and the incremental estimation. During initialisation the image regions corresponding to moving persons are detected, the movements states are estimated and the starting values for the Kalman filter are determined. At the incremental estimation stage the Kalman filter is applied to each image, predicting the movement state, then by measuring the actual movement state the Kalman filter is updated. The geometrical scene description is considered necessary in order to translate the low-level vision processes to natural language description and since the process takes place incrementally it is also necessary to identify future intentions in the visual representation, while being able to render it into language. The
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Advances to Semantic Interoperability through CPR Ontology extracting from SOAP framework reports

Advances to Semantic Interoperability through CPR Ontology extracting from SOAP framework reports

we picked what we could spot has low-hanging fruit to develop our systems rendering them sub-optimal but demonstrable of the validity of the concepts and easily extendable/tunable with better ontology support, finer Web Service provisioning but most of all with better

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JISTEM J.Inf.Syst. Technol. Manag.  vol.8 número3

JISTEM J.Inf.Syst. Technol. Manag. vol.8 número3

Kousetti, Millard and Howard (2008) corroborate and add that semantic wikis are the perfect combination of collaboration and semantic expressiveness. Attempting to analyze semantic wikis as a basis for collaborative ontology construction, these authors explain that the creation of ontologies has been in the hands of experts, as well as knowledge management with specialists in this area. In order to realize the semantic web, all participants in the process have the opportunity to contribute and, therefore, to collaborate. The ease of authorship in wiki pages is a great motivator for integration and interoperability of the semantic web. The knowledge users, not technical in ontologies, require a minimal understanding of how to operate in semantic wikis, without the need for deepening the ontological concepts.
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Semantic Web Improved with the Weighted IDF Feature

Semantic Web Improved with the Weighted IDF Feature

Abstract —The development of search engines is taking at a very fast rate. A lot of algorithms have been tried and tested. But, still the people are not getting precise results. Social networking sites are developing at tremendous rate and their growth has given birth to the new interesting problems. The social networking sites use semantic data to enhance the results. This provides us with a new perspective on how to improve the quality of information retrieval. As we are aware, many techniques of text classification are based on TFIDF algorithm. Term weighting has a significant role in classifying a text document. In this paper, firstly, we are extending the queries by “keyword+tags” instead of keywords only. In addition to this, secondly, we have developed a new ranking algorithm (JEKS algorithm) based on semantic tags from user feedback that uses CiteUlike data. The algorithm enhances the already existing semantic web by using the weighted IDF feature of the TFIDF algorithm. The suggested algorithm provides a better ranking than Google and can be viewed as a semantic web service in the domain of academics.
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Semantic Web materialization: a dynamic building model of queries based in ontology mapping

Semantic Web materialization: a dynamic building model of queries based in ontology mapping

Approach the computer language to human language is a challenge in seeking to improve the process of information retrieval. From this perspective, the Semantic Web has allowed a new mode of data processing contained in the Web, in order to allow better understanding of the meaning that these information possess. However, some of the concepts involving the Semantic Web such as ontologies, have highly complex, making it difficult to use. The difficulty in understanding, the structure that they have, and diversity as they are built, make the ontologies manipulation to discover the meaning of the concepts, something not effective, making it impossible to offer a concrete overview of the meaning of the concepts. Thus, this research aims to propose a model that can verify the context and meaning of a term in an ontology. To this end, it performed bibliographic research, model building and implementation of a prototype as proof of concept for the verification of results. The model and implementation have resulted in the generic treatment of an ontology, to obtain the meaning and the context that has a term. Based on the results it is identified that the use of ontologies allows information systems to provide a broader perspective of the context of data, enabling informational user needs can be met more effectively.
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POTENTIAL OF ONTOLOGY FOR INTEROPERABILITY IN E-GOVERNMENT: DISCUSSING INTERNATIONAL INITIATIVES AND THE BRAZILIAN CASE

POTENTIAL OF ONTOLOGY FOR INTEROPERABILITY IN E-GOVERNMENT: DISCUSSING INTERNATIONAL INITIATIVES AND THE BRAZILIAN CASE

the Federal Government, it was decided to develop the work step by step, starting with a basic ontology con- cepts, covering only the most consensual expenditure items (although not unanimous) on the public budget (Araújo et al 2012). Figure 4 (in appendix) reflects the result of this effort considering only the expense items and its budgetary classifiers (Function, Subfunction, program, and so on). In this figure, the identified con- cepts is described as OWL classes and are identified by rectangles in gray and the relationships between clas- ses’ elements by black rectangles. The range of the relationship is represented by a letter r, while the do- main is represented by the letter d. Dashed lines repre- sent subclass relationships. The prefix siop was as- sumed for the object properties while the properties of data types are represented by white rectangles contain- ing its type. As a result of this investment, SOF made available its annual budget data (from 2000) in open format (RDF) with a conceptual agreement assured by the definitions from the built ontology.
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Semantic Web Services and Its Approaches

Semantic Web Services and Its Approaches

OWL-S complements industry efforts such as SOAP, WSDL, UDDI, and BPEL4WS. It builds upon these efforts by adding rich typing and class information that can be used to describe and constrain the range of Web service capabilities much more effectively than XML data types. Further, it integrates such rich class representations with a process model, designed not only to capture the control flow and data flow of Web services, but also their side effects (preconditions and effects) in the world. The use of such a language enables the grouping of like services and like data types into taxonomic hierarchies, together with rich definitions of the relationships and constraints between classes and their instances. The well-defined semantics enables automated manipulation of these structures, with known outcome. In short, OWL-S makes automated interoperation possible [9].
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J. Braz. Comp. Soc.  vol.11 número2

J. Braz. Comp. Soc. vol.11 número2

The exercise of developing an ontology about contemporary art raised several philosophical thoughts we consider important to mention here. Today art deals with the effacing of frontiers regarding classifications. It is impossible to constrict a work of art to the domain of a category like “painting” or “sculpture”. New classifications appear from time to time to work around this language limitation: one may classify a work of art as “performance” because the artist paints a canvas while dancing around it during the exhibition, or “installation” because the spectator is confronted with an environment that includes music, video, sculptures, smells, all making a single work of art 1 .
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J. Braz. Comp. Soc.  vol.15 número2

J. Braz. Comp. Soc. vol.15 número2

Abstract: Geographic Data Warehouses (GDW) are one of the main technologies used in decision-making processes and spatial analysis, and the literature proposes several conceptual and logical data models for GDW. However, little effort has been focused on studying how spatial data redundancy affects SOLAP (Spatial On-Line Analytical Processing) query performance over GDW. In this paper, we investigate this issue. Firstly, we compare redundant and non-redundant GDW schemas and conclude that redundancy is related to high performance losses. We also analyze the issue of indexing, aiming at improving SOLAP query performance on a redundant GDW. Comparisons of the SB-index approach, the star-join aided by R-tree and the star-join aided by GiST indicate that the SB-index significantly improves the elapsed time in query processing from 25% up to 99% with regard to SOLAP queries defined over the spatial predicates of intersection, enclosure and containment and applied to roll-up and drill-down operations. We also investigate the impact of the increase in data volume on the performance. The increase did not impair the performance of the SB-index, which highly improved the elapsed time in query processing. Performance tests also show that the SB-index is far more compact than the star-join, requiring only a small fraction of at most 0.20% of the volume. Moreover, we propose a specific enhancement of the SB-index to deal with spatial data redundancy. This enhancement improved performance from 80 to 91% for redundant GDW schemas.
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Simulation of inter and intra group behaviors using semantic virtual environments

Simulation of inter and intra group behaviors using semantic virtual environments

Lien and collaborators [30] proposed ways for using roadmaps to simulate a type of flocking behavior called shepherding behavior in which outside agents guide or control members of a flock. Data-driven models are quite recent in comparison with other methods, and aim to record motion in a pre-production stage or to use information from real life to calibrate the simulation algorithms. Another method using data-driven technique were proposed by Lee et al. [29]. The authors recorded the movement of a crowd of humans from an aerial view using a camera, extracted the movement trajectories in 2D for each individual, and then a model of agent is learned from the observed trajectories. The model of agent decides each agent’s action based on the environment characteristics and the nearest agents. Once the model of agent is learned, the virtual crowd can be simulated, as shown in Figure 2.6. This figure illustrates the patterns learned form real videos (on top) and the respective simulations (on bottom): figures in left shows a pattern with group formations and individuals, while figures in right shows a line formation pattern.
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