Ontology Based Semantic Information Retrieval

Web contents related to educational institutions as well as their geographic data of a country is an emerging field of data sharing and consolidating with suitable data repositories to extract useful information to improve and maintain the area-based quality of education. The information of these institutions is available in the unstructured or semi-structured format from heterogeneous sources on the web that originate challenges to develop a smarter method in finding specific information using traditional search engines. The integration of these heterogeneous data along with geographic information is a formidable task to explore implicit knowledge of educational institutions that assist people to improve these systems and it is still getting researchers' attention. In this regard, our research demonstrates the feasibility of semantic web technologies for converting and integrating these unstructured or semi-structured information by introducing machine understandable description of resources and instances by consisting of their concepts and relations, called a repository name EduBD. This yields a magnificent knowledge graph with .75 million nodes, i.e. RDF triples to facilitate reasoning meaningful information by a graph based query language SPARQL. Furthermore, we utilized our knowledge-base EduBD with one more accessible spatial data source Geo-Bangladesh using Physical-Cyber-Social computing approach to examine spatial contents and social information of each institution and therefore achieved the semantic interoperability of our Linked Open Data (LOD) application by eliminating natural language polisemy problems.

Quran is the holy book of Muslims that contains the commandment of words of Allah. Quran provides instructions and guidance to humankind in achieving happiness in life in the world and the hereafter. As a holy book, Quran contains rich knowledge and scientific facts. However, humans have difficulty in understanding the Quran content. It is caused by the fact that the meaning of the searched message content depends on the interpretation. Ontology able to store the knowledge representation of Holy Quran. This paper studies recent ontology on Holy Quran research. We investigate the current trends and technology being applied. This investigation cover on several aspects, such as outcomes of previous studies, language which used on ontology development, coverage area of Quran ontology, datasets, tools to perform ontology development ontology population techniques, approaches used to integrate the knowledge of Quran and other resources into ontology, ontology testing techniques, and limitations on previous research. This review has identified four major issues involved in Quran ontology, i.e. availability of Quran ontology in various translation, ontology resources, automated process of Meronymy relationship extraction, and Instances Classification. The review of existing studies will allow future researchers to have a broad and useful background knowledge on primary and essential aspects of this research field.

Automatic classification has become an important research area due to the rapid increase of digital information. Evidently, manual classification of documents is a tough work due to occurrences of vocabulary ambiguities of classification schemes as well as the language used in the text in hand.

In our study, we made an attempt to resolve this matter. This research has developed a computer program that can automatically classify a given text document based on a well developed ontology. Therefore, the user gets correct options of classification just after feeding the document to the new system. The new ontology is a domain ontology which is based on the Dewey Decimal Classification scheme and the Sears List. Data was obtained for classification accuracy for both manual and automatic methods. Moreover, the relationship between the vagueness of language in documents and the inaccuracy of classification were determined against manual classification and manual classification with automatic aid.

The research revealed that the results were more accurate when the newly developed automatic system was used. In addition, it also revealed that the vagueness of documents affects more in manual classification than in manual classification with the automatic aid.

The Web of Data is an open environment consisting of a great number of large inter-linked RDF datasets from various do-mains. In this environment, organizations and companies adopt the Linked Data practices utilizing Semantic Web (SW) tech-nologies, in order to publish their data and offer SPARQL endpoints (i.e., SPARQL-based search services). On the other hand, the dominant standard for information exchange in the Web today is XML. Additionally, many international standards (e.g., Dublin Core, MPEG-7, METS, TEI, IEEE LOM) in several domains (e.g., Digital Libraries, GIS, Multimedia, e-Learning) have been expressed in XML Schema. The aforementioned have led to an increasing emphasis on XML data, accessed using the XQuery query language. The SW and XML worlds and their developed infrastructures are based on different data models, semantics and query languages. Thus, it is crucial to develop interoperability mechanisms that allow the Web of Data users to access XML datasets, using SPARQL, from their own working environments. It is unrealistic to expect that all the existing legacy data (e.g., Relational, XML, etc.) will be transformed into SW data. Therefore, publishing legacy data as Linked Data and providing SPARQL endpoints over them has become a major research challenge. In this direction, we introduce the SPARQL2XQuery Framework which creates an interoperable environment, where SPARQL queries are automatically translated to XQuery queries, in order to access XML data across the Web. The SPARQL2XQuery Framework provides a mapping model for the expression of OWL–RDF/S to XML Schema mappings as well as a method for SPARQL to XQuery translation. To this end, our Framework supports both manual and automatic mapping specification between ontologies and XML Schemas. In the automatic mapping specification scenario, the SPARQL2XQuery exploits the XS2OWL component which transforms XML Sche-mas into OWL ontologies. Finally, extensive experiments have been conducted in order to evaluate the schema transfor-mation, mapping generation, query translation and query evaluation efficiency, using both real and synthetic datasets.

In this paper, we describe the narrative ontological model encompassed in the Labyrinth system. The aim of the system is to allow users to explore a digital archive by following the narrative relations among the resources contained in it. Targeted at cultural heritage applications, the Labyrinth project relies on the notion of “cultural archetype”, i.e., a core representation encompassing archetypical stories and characters, exploited as a conceptual framework for the access to archives of heterogeneous media objects. In particular, we describe how the system leverages various types of ontological reasoning to let narrative relations emerge between artworks, and exemplify how these relations are exploited by the system to provide the user with a narrative conceptual framework she or he is familiar with in the exploration of the archive.

"In the context of the emergent Web of Data, a large number of organizations, institutes and companies (e.g., DBpedia, Geonames, PubMed ACM, IEEE, NASA, BBC) adopt the Linked Data practices and publish their data utilizing Semantic Web (SW) technologies. On the other hand, the dominant standard for information exchange in the Web today is XML. Many international standards (e.g., Dublin Core, MPEG-7, METS, TEI, IEEE LOM) have been expressed in XML Schema resulting to a large number of XML datasets. The SW and XML worlds and their developed infrastructures are based on different data models, semantics and query languages. Thus, it is crucial to provide interoperability and integration mechanisms to bridge the gap between the SW and XML worlds.
In this chapter, we give an overview and a comparison of the technologies and the standards adopted by the XML and SW worlds. In addition, we outline the latest efforts from the W3C groups, including the latest working drafts and recommendations (e.g., OWL 2, SPARQL 1.1, XML Schema 1.1). Moreover, we present a survey of the research approaches which aim to provide interoperability and integration between the XML and SW worlds. Finally, we present the SPARQL2XQuery and XS2OWL Frameworks, which bridge the gap and create an interoperable environment between the two worlds. These Frameworks provide mechanisms for: (a) Query translation (SPARQL to XQuery translation); (b) Mapping specification and generation (Ontology to XML Schema mapping); and (c) Schema transformation (XML Schema to OWL transformation)."

Data exploration and visualization systems are of great importance in the Big Data era, in which the volume and
heterogeneity of available information make it difficult for humans to manually explore and analyse data. Most traditional systems
operate in an offline way, limited to accessing preprocessed (static) sets of data. They also restrict themselves to dealing
with small dataset sizes, which can be easily handled with conventional techniques. However, the Big Data era has realized the
availability of a great amount and variety of big datasets that are dynamic in nature; most of them offer API or query endpoints
for online access, or the data is received in a stream fashion. Therefore, modern systems must address the challenge of on-the-fly
scalable visualizations over large dynamic sets of data, offering efficient exploration techniques, as well as mechanisms for
information abstraction and summarization. Further, they must take into account different user-defined exploration scenarios
and user preferences. In this work, we present a generic model for personalized multilevel exploration and analysis over large
dynamic sets of numeric and temporal data. Our model is built on top of a lightweight tree-based structure which can be efficiently
constructed on-the-fly for a given set of data. This tree structure aggregates input objects into a hierarchical multiscale
model. We define two versions of this structure, that adopt different data organization approaches, well-suited to exploration and
analysis context. In the proposed structure, statistical computations can be efficiently performed on-the-fly. Considering different
exploration scenarios over large datasets, the proposed model enables efficient multilevel exploration, offering incremental construction
and prefetching via user interaction, and dynamic adaptation of the hierarchies based on user preferences. A thorough
theoretical analysis is presented, illustrating the efficiency of the proposed model. The proposed model is realized in a web-based
prototype tool, called SynopsViz that offers multilevel visual exploration and analysis over Linked Data datasets. Finally, we
provide a performance evaluation and a empirical user study employing real datasets.

Cet article se propose de discuter l’idée, aujourd’hui répandue, que les ontologies fondationnelles sont utiles, voire nécessaires, pour assurer l’interopérabilité sémantique entre systèmes de collecte de données faisant usage de terminologies hétérogènes, c’est-à-dire pour résoudre le problème dit de la Tour de Babel. Après un rappel des principales caractéristiques des ontologies, nous proposons une liste des grandes fonctions généralement attribuées, et a minima attendues des ontologies fondationnelles, pour en discuter ensuite le bien-fondé. Nous montrons (i) que la possibilité de réaliser une ontologie intégrant des primitives et définitions formelles suffisamment universelles pour décrire la sémantique des concepts de domaines spécialisés du savoir est loin d’être acquise ; (ii) que même si une telle ontologie s’avérait réalisable, il n’est pas certain qu’elle permettrait d’assurer l’interopérabilité sémantique entre systèmes de données hétérogènes, c’est-à-dire un échange de données préservant le sens que ces données possèdent pour ceux qui les ont encodées.

In the context of the emergent Web of Data, a large number of organizations, institutes and companies (e.g., DBpedia, Geonames, PubMed ACM, IEEE, NASA, BBC) adopt the Linked Data practices and publish their data utilizing Semantic Web (SW) technologies. On the other hand, the dominant standard for information exchange in the Web today is XML. Many international standards (e.g., Dublin Core, MPEG-7, METS, TEI, IEEE LOM) have been expressed in XML Schema resulting to a large number of XML datasets. The SW and XML worlds and their developed infrastructures are based on different data models, semantics and query languages. Thus, it is crucial to provide interoperability and integration mechanisms to bridge the gap between the SW and XML worlds.

This paper describes the main functionalities of an ontology-
based data explorer for Key Performance Indicators (KPI), aimed to support users in the extraction of KPI values from a shared repository. Data produced by partners of a Virtual Enterprise are semantically annotated through a domain ontology in which KPIs are described together with their mathematical formulas. Based on this model and on reasoning capabilities, the tool provides functionalities for dynamic aggregation of data and computation of KPI values through the formula. In this way, besides the usual drill-down, a novel mode of data exploration is enabled,
based on the expansion of a KPI into its components.

Recommending news items is traditionally done by termbased algorithms like TF-IDF. This paper concentrates on the benefits of recommending news items using a domain ontology instead of using a term-based approach. For this purpose, we propose Athena, which is an extension to the existing Hermes framework. Athena employs a user profile to store terms or concepts found in news items browsed by the user. Based on this information, the framework uses a traditional method based on TF-IDF, and several ontologybased methods to recommend new articles to the user. The paper concludes with the evaluation of the different methods, which shows that the new ontology-based method that we propose in this paper performs better (w.r.t. accuracy, precision, and recall) than the traditional method and, with the exception of one measure (recall), also better than the other considered ontology-based approaches.

L'Éternel descendit pour voir la ville et la tour que bâtissaient les fils des hommes. Et l'Éternel dit : Voici, ils forment un seul peuple et ont tous une même langue, et c'est là ce qu'ils ont entrepris ; maintenant rien ne les empêcherait de faire tout ce qu'ils auraient projeté. Allons ! Descendons, et là confondons leur langage, afin qu'ils n'entendent plus la langue, les uns des autres.

When we speak of social memory, it is said that it will always remain a skill of communication in support of communication. Increasing the skill of information processing, on equal terms, is the result of the system's complexity increase. It is not just the observer depending on information, but first of all the information depending on the observer; and this concept makes visible how the issue consists in the circle throughout which the observer builds up autonomously all information from which his decisions depend on. In order to make possible this kind of interaction, we need to conceive memory not just as a simple sum of records: what is inside memory must be relied to a net of self-referential cross references which makes possible to retrieve the information through a machine. Using the theory of society, this text studies the selection of information and its cataloguing in Early Modernity. The keyword to point this matter out is "order".

This paper describes GoNTogle, a framework for document annotation and retrieval, built on top of Semantic Web and IR technologies. GoNTogle supports ontology-based annotation for documents of several formats, in a fully collaborative environment. It provides both manual and automatic annotation mechanisms. Automatic annotation is based on a learning method that exploits user annotation history and textual information to automatically suggest annotations for new documents. GoNTogle also provides search facilities beyond the traditional keyword-based search. A flexible combination of keyword-based and semantic-based search over documents is proposed in conjunction with advanced ontology-based search operations. The proposed methods are implemented in a fully functional tool and their effectiveness is experimentally validated.

Web contents related to educational institutions as well as their geographic data of a country is an emerging field of data sharing and consolidating with suitable data repositories to extract useful information to improve and maintain the area-based quality of education. The information of these institutions is available in the unstructured or semi-structured format from heterogeneous sources on the web that originate challenges to develop a smarter method in finding specific information using traditional search engines. The integration of these heterogeneous data along with geographic information is a formidable task to explore implicit knowledge of educational institutions that assist people to improve these systems and it is still getting researchers' attention. In this regard, our research demonstrates the feasibility of semantic web technologies for converting and integrating these unstructured or semistructured information by introducing machine understandable description of resources and instances by consisting of their concepts and relations, called a repository name EduBD. This yields a magnificent knowledge graph with .75 million nodes, i.e. RDF triples to facilitate reasoning meaningful information by a graph based query language SPARQL. Furthermore, we utilized our knowledge-base EduBD with one more accessible spatial data source Geo-Bangladesh using Physical-Cyber-Social computing approach to examine spatial contents and social information of each institution and therefore achieved the semantic interoperability of our Linked Open Data (LOD) application by eliminating natural language polisemy problems.

Natural Language Processing is a programmed approach to analyze text that is based on both a set of theories and a set of technologies. This forum aims to bring together researchers who have designed and build software that will analyze, understand, and generate languages that humans use naturally to address computers.

The need for Emergency Management continually grows as the population and exposure to catastrophic failures increase. The ability to offer appropriate services at these emergency situations can be tackled through group communication mechanisms. The entities involved in the group communication include people, organizations, events, locations and essential services. Cloud computing is a “as a service” style of computing that enables on-demand network access to a shared pool of resources. So this work focuses on proposing a social cloud constituting group communication entities using an open source platform, Eucalyptus. The services are exposed as semantic web services, since the availability of machine-readable metadata (Ontology) will enable the access of these services more intelligently. The objective of this paper
is to propose an Ontology-based Emergency Management System in a social cloud and demonstrate the same using emergency healthcare domain.

This paper presents GoNTogle, a tool which provides advanced document annotation and search facilities. GoNTogle allows users to annotate several document formats, using ontology concepts. It also produces automatic annotation suggestions based on textual similarity and previous document annotations. Finally, GoNTogle combines keyword and ontology-based search, offering advanced ontology query facilities.

Cet article se propose de discuter l’idée, aujourd’hui répandue, que les ontologies fondationnelles sont utiles, voire nécessaires, pour assurer l’interopérabilité sémantique entre systèmes de collecte de données faisant usage de terminologies hétérogènes, c’est-à-dire pour résoudre le problème dit de la Tour de Babel. Après un rappel des principales caractéristiques des ontologies, nous proposons une liste des grandes fonctions généralement attribuées, et a minima attendues des ontologies fondationnelles, pour en discuter ensuite le bien-fondé. Nous montrons (i) que la possibilité de réaliser une ontologie intégrant des primitives et définitions formelles suffisamment universelles pour décrire la sémantique des concepts de domaines spécialisés du savoir est loin d’être acquise ; (ii) que même si une telle ontologie s’avérait réalisable, il n’est pas certain qu’elle permettrait d’assurer l’interopérabilité sémantique entre systèmes de données hétérogènes, c’est-à-dire un échange de ...

May 29~30, 2021, Vancouver, Canada https://ccsit2021.org/soen/index.html Scope & Topics 6 th International Conference on Software Engineering (SOEN 2021) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Software Engineering and Applications. The goal of this Conference is to bring together researchers and practitioners from academia and industry to focus on understanding Modern software engineering concepts and establishing new collaborations in these areas. Authors are solicited to contribute to the Conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Computer Science and Information Technology. Topics of interest include, but are not limited to, the following Topics of Interest  Advanced Topics in Software Engineering  Knowledge-based Systems and Formal Methods  Languages and Formal Methods  Managing Software Projects  Multimedia and Visual Software Engineering  Quality Management  Search Engines and Information Retrieval  Software Engineering Decision Making  Software Engineering Practice  Software Maintenance and Testing  The Software Process  Web Engineering

The multimedia information retrieval from World Wide Web is a challenging issue. Describing multimedia object in general, images in particular with low-level features increases the semantic gap. From WWW, information present in a HTML document as textual keywords can be extracted for capturing semantic information with the view to narrow the semantic gap. The high-level textual information of images can be extracted and associated with the textual keywords, which narrow down the search space and improve the precision of retrieval. In this paper, a strength matrix is being proposed, which is based on the frequency of occurrence of keywords and the textual information pertaining to image URLs. The strength of these textual keywords are estimated and used for associating these keywords with the images present in the documents. The high-level semantics of the image is described in the HTML documents in the form of image name, ALT tag, optional description, etc., is used for estimating the strength. In addition, word position and weighting mechanism is also used for further improving the association textual keywords with the image related text. The effectiveness of information retrieval of the proposed technique is found to be comparatively better than many of the recently proposed retrieval techniques. The experimental results of the proposed method endorse the fact that image retrieval using image information and textual keywords is better than those of the text based and the content-based approaches.

With the advancement of the web it is very difficult to keep up with the amplifying requirements of learning
on web, to satisfy user’s expectation. Users demand with the updated and accurate results. To solve the
queries Search Engines use different techniques. Google the most famous search engine uses Page Ranking
Algorithm. Ranking Algorithms arrange the results according to the user’s needs. This paper deals with
“Page Rank Algorithm”. Our proposed algorithm is an extension of page rank algorithm which refines the
results so that user gets what he/she expects. We have used a measure Average Precision to compare Page
Rank algorithm and the proposed algorithm, and proved that our algorithm provides better results.

Today, automated reasoning is a real need for intelligent systems. Information Retrieval systems in general and specifically a question answering system require a reasoning mechanism as well. In this paper, a heuristic reasoning mechanism, implemented in the online phase of TeLQAS is proposed. TeLQAS is an ontology-based natural language question answering system for the domain of Telecommunications. In an online process, TeLQAS accepts the users' questions in English. Then, as a first try, the system attempts to obtain reasonable answers based upon the ontology through reasoning. However, the next step is a summarization-based answer extraction, which is not the focus of this paper. The reasoning mechanism utilizes a number of rules defined based on the existing relation types in the ontology and also some linguistics and structural clues of the English Language. The results of experiments prove a considerable improvement in the system performance in comparison with the case where there is no reasoning according to the ontology. In addition to the accuracy of answers, a great reduction in turn-around time is also gained.

Lexical-semantic resources (LSRs) have an important role in many information retrieval and extraction tasks. However, in order to be effective, LSRs need to cover a broad spectrum of knowledge facets about terms, e.g., encyclopedic, linguistic , and common sense. These different knowledge facets are usually found in different and heterogeneous knowledge sources spread over the Web, turning the task of integrating them into an unified LSR a hard one. In this work, we propose a new approach to automatically build LSRs that are tailored to semantic search engines, i.e., our approach builds LSRs that favor disambiguation and faceted search. Moreover, while most of the related work is limited to using encyclopedic sources only, such as Wikipedia, we instantiate our approach using additional and heterogeneous knowledge sources, such as dictionaries (WordNet), common sense (Open Mind Common Sense), and semantic networks (Con-ceptnet5). For evaluation, we compare our approach with UBY, a open source and state-of-the-art LSR, in terms of the number of terms covered; the degree of semantic connections established for each term; and the strength of the extracted semantic relations between terms. We conducted experiments in several test sets and show that our approach is superior under the aforementioned aspects.

Classification is an important technique used in information retrieval. Supervised classification suffers from certain limitations concerning the collection and labeling of the training dataset. When facing Multi-Domain classification, multiple training datasets and classifiers are needed which is relatively difficult. In this paper an unsupervised classification system is proposed that can manage the Multi-Domain classification problem as well. It is a multi-domain system where each domain represented by an ontology. A document is mapped on each ontology based on the weights of the mutual tokens between them with the help of fuzzy sets, resulting in a mapping degree of the document with each domain. An experiment carried out showing satisfying classification results with an improvement in the evaluation results of the proposed system compared to Apache Lucene.