Search results for: ontology matching

Authors: Swapnil Gupta, C. Pandu Rangan

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A uniquely restricted matching is defined to be a matching M whose matched vertices induces a sub-graph which has only one perfect matching. In this paper, we make progress on the open question of the status of this problem on interval graphs (graphs obtained as the intersection graph of intervals on a line). We give an algorithm to compute maximum cardinality uniquely restricted matchings on certain sub-classes of interval graphs. We consider two sub-classes of interval graphs, the former contained in the latter, and give O(|E|^2) time algorithms for both of them. It is to be noted that both sub-classes are incomparable to proper interval graphs (graphs obtained as the intersection graph of intervals in which no interval completely contains another interval), on which the problem can be solved in polynomial time.

Keywords: uniquely restricted matching, interval graph, matching, induced matching, witness counting

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Authors: Zhangquan Zhou, Guilin Qi

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Materialization is an important reasoning service for applications built on the Web Ontology Language (OWL). To make materialization efficient in practice, current research focuses on deciding tractability of an ontology language and designing parallel reasoning algorithms. However, some well-known large-scale ontologies, such as YAGO, have been shown to have good performance for parallel reasoning, but they are expressed in ontology languages that are not parallelly tractable, i.e., the reasoning is inherently sequential in the worst case. This motivates us to study the problem of parallel tractability of ontology materialization from a theoretical perspective. That is we aim to identify the ontologies for which materialization is parallelly tractable, i.e., in the NC complexity. Since the NC complexity is defined based on Boolean circuit that is widely used to investigate parallel computing problems, we first transform the problem of materialization to evaluation of Boolean circuits, and then study the problem of parallel tractability based on circuits. In this work, we focus on datalog rewritable ontology languages. We use Boolean circuits to identify two classes of datalog rewritable ontologies (called parallelly tractable classes) such that materialization over them is parallelly tractable. We further investigate the parallel tractability of materialization of a datalog rewritable OWL fragment DHL (Description Horn Logic). Based on the above results, we analyze real-world datasets and show that many ontologies expressed in DHL belong to the parallelly tractable classes.

Keywords: ontology materialization, parallel reasoning, datalog, Boolean circuit

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Authors: André Smith, Amr Abdel-Dayem

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This paper presents an overview of the history and development of stereo matching algorithms. Details from its inception, up to relatively recent techniques are described, noting challenges that have been surmounted across these past decades. Different components of these are explored, though focus is directed towards the local matching techniques. While global approaches have existed for some time, and demonstrated greater accuracy than their counterparts, they are generally quite slow. Many strides have been made more recently, allowing local methods to catch up in terms of accuracy, without sacrificing the overall performance.

Keywords: developmental survey, local stereo matching, rectification, stereo correspondence

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Authors: Hyun-Koo Kim, Yonghun Kim, Yong-Hoon Kim, Ju Hee Lee, Myungho Song

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In this paper, we have described an efficient stereo matching method and pedestrian detection method using stereo types LWIR camera. We compared with three types stereo camera algorithm as block matching, ELAS, and SGM. For pedestrian detection using stereo LWIR camera, we used that SGM stereo matching method, free space detection method using u/v-disparity, and HOG feature based pedestrian detection. According to testing result, SGM method has better performance than block matching and ELAS algorithm. Combination of SGM, free space detection, and pedestrian detection using HOG features and SVM classification can detect pedestrian of 30m distance and has a distance error about 30 cm.

Keywords: advanced driver assistance system, pedestrian detection, stereo matching method, stereo long-wave IR camera

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Authors: Masao Okabe

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Nowadays, in Japan, variety of students get into a university and one of the main roles of introductory courses for freshmen is to make such students well prepared for subsequent intermediate courses. For that purpose, the flipped-mastery model is not enough because videos usually used in a flipped classroom is not adaptive and does not fit all freshmen with different academic performances. This paper proposes an ontology-navigated tutoring system called EduGraph. Using EduGraph, students can prepare for and review a class, in a more flexibly personalizable way than by videos. Structuralizing learning materials by its ontology, EduGraph also helps students integrate what they learn as knowledge, and makes learning materials sharable. EduGraph was used for an introductory course for freshmen. This application suggests that EduGraph is effective.

Keywords: adaptive e-learning, flipped classroom, mastery learning, ontology

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Authors: Karine B. de Oliveira, Carina F. Dorneles

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The structure level matching is the problem of combining elements of a structure, which can be represented as entities, classes, XML elements, web forms, and so on. This is a challenge due to large number of distinct representations of semantically similar structures. This paper describes a structure-based matching method applied to search for different representations in data sources, considering the similarity between elements of two structures and the data source context. Using real data sources, we have conducted an experimental study comparing our approach with our baseline implementation and with another important schema matching approach. We demonstrate that our proposal reaches higher precision than the baseline.

Keywords: context, data source, index, matching, search, similarity, structure

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Authors: Ashish Shrivastava, C. Pandu Rangan

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In this paper, we consider The Hospitals Residents problem with Social Stability (HRSS), where hospitals and residents can communicate only through the underlying social network. Those residents and hospitals which don not have any social connection between them can not communicate and hence they cannot be a social blocking pair with respect to a socially stable matching in an instance of hospitals residents problem with social stability. In large scale matching like NRMP or Scottish medical matching scheme etc. where set of agents, as well as length of preference lists, are very large, social stability is a useful notion in which members of a blocking pair could block a matching if and only if they know the existence of each other. Thus the notion of social stability in hospitals residents problem allows us to increase the cardinality of the matching without taking care of those blocking pairs which are not socially connected to each other. We know that finding a maximum cardinality socially stable matching, in an instance, of HRSS is NP-hard. This motivates us to solve this problem with bounded length preference lists on one side. In this paper, we have presented a polynomial time algorithm to compute maximum cardinality socially stable matching in a HRSS instance where residents can give at most two length and hospitals can give unbounded length preference list. Preference lists of residents and hospitals will be strict in nature.

Keywords: matching under preference, socially stable matching, the hospital residents problem, the stable marriage problem

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Authors: Neli P. Zlatareva

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Ontology validation is an important part of web applications’ development, where knowledge integration and ontological reasoning play a fundamental role. It aims to ensure the consistency and correctness of ontological knowledge and to guarantee that ontological reasoning is carried out in a meaningful way. Existing approaches to ontology validation address more or less specific validation issues, but the overall process of validating web ontologies has not been formally established yet. As the size and the number of web ontologies continue to grow, the necessity to validate and ensure their consistency and interoperability is becoming increasingly important. This paper presents a validation technique intended to test the consistency of independent ontologies utilized by a common application.

Keywords: knowledge engineering, ontological reasoning, ontology validation, semantic web

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Authors: Marc Sango, Olimpia Hoinaru, Christophe Gransart, Laurence Duchien

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Ontologies offer a means for representing and sharing information in many domains, particularly in complex domains. For example, it can be used for representing and sharing information of System Requirement Specification (SRS) of complex systems like the SRS of ERTMS/ETCS written in natural language. Since this system is a real-time and critical system, generic ontologies, such as OWL and generic ERTMS ontologies provide minimal support for modeling temporal information omnipresent in these SRS documents. To support the modeling of temporal information, one of the challenges is to enable representation of dynamic features evolving in time within a generic ontology with a minimal redesign of it. The separation of temporal information from other information can help to predict system runtime operation and to properly design and implement them. In addition, it is helpful to provide a reasoning and querying techniques to reason and query temporal information represented in the ontology in order to detect potential temporal inconsistencies. Indeed, a user operation, such as adding a new constraint on existing planning constraints can cause temporal inconsistencies, which can lead to system failures. To address this challenge, we propose a lightweight 3-layer temporal Quality of Service (QoS) ontology for representing, reasoning and querying over temporal and non-temporal information in a complex domain ontology. Representing QoS entities in separated layers can clarify the distinction between the non QoS entities and the QoS entities in an ontology. The upper generic layer of the proposed ontology provides an intuitive knowledge of domain components, specially ERTMS/ETCS components. The separation of the intermediate QoS layer from the lower QoS layer allows us to focus on specific QoS Characteristics, such as temporal or integrity characteristics. In this paper, we focus on temporal information that can be used to predict system runtime operation. To evaluate our approach, an example of the proposed domain ontology for handover operation, as well as a reasoning rule over temporal relations in this domain-specific ontology, are given.

Keywords: system requirement specification, ERTMS/ETCS, temporal ontologies, domain ontologies

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Authors: Onyekachi Onwudike, Russell Lock, Iain Phillips

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The variability that exists across governments, her departments and the provisioning of services has been areas of concern in the E-Government domain. There is a need for reuse and integration across government departments which are accompanied by varying degrees of risks and threats. There is also the need for assessment, prevention, preparation, response and recovery when dealing with these risks or threats. The ability of a government to cope with the emerging changes that occur within it is known as resilience. In order to forge ahead with concerted efforts to manage reuse and integration induced risks or threats to governments, the ambiguities contained within resilience must be addressed. Enhancing resilience in the E-Government domain is synonymous with reducing risks governments face with provisioning of services as well as reuse of components across departments. Therefore, it can be said that resilience is responsible for the reduction in government’s vulnerability to changes. In this paper, we present the use of the ontology to measure the resilience of governments. This ontology is made up of a well-defined construct for the taxonomy of resilience. A specific class known as ‘Resilience Requirements’ is added to the ontology. This class embraces the concept of resilience into the E-Government domain ontology. Considering that the E-Government domain is a highly complex one made up of different departments offering different services, the reliability and resilience of the E-Government domain have become more complex and critical to understand. We present questions that can help a government access how prepared they are in the face of risks and what steps can be taken to recover from them. These questions can be asked with the use of queries. The ontology focuses on developing a case study section that is used to explore ways in which government departments can become resilient to the different kinds of risks and threats they may face. A collection of resilience tools and resources have been developed in our ontology to encourage governments to take steps to prepare for emergencies and risks that a government may face with the integration of departments and reuse of components across government departments. To achieve this, the ontology has been extended by rules. We present two tools for understanding resilience in the E-Government domain as a risk analysis target and the output of these tools when applied to resilience in the E-Government domain. We introduce the classification of resilience using the defined taxonomy and modelling of existent relationships based on the defined taxonomy. The ontology is constructed on formal theory and it provides a semantic reference framework for the concept of resilience. Key terms which fall under the purview of resilience with respect to E-Governments are defined. Terms are made explicit and the relationships that exist between risks and resilience are made explicit. The overall aim of the ontology is to use it within standards that would be followed by all governments for government-based resilience measures.

Keywords: E-Government, Ontology, Relationships, Resilience, Risks, Threats

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Authors: Sahar Maâlej Dammak, Anis Jedidi, Rafik Bouaziz

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With the great mass of pages managed through the world, and especially with the advent of the Web, their manual annotation is impossible. We focus, in this paper, on the semiautomatic annotation of the web pages. We propose an approach and a framework for semantic annotation of web pages entitled “Querying Web”. Our solution is an enhancement of the first result of annotation done by the “Semantic Radar” Plug-in on the web resources, by annotations using an enriched domain ontology. The concepts of the result of Semantic Radar may be connected to several terms of the ontology, but connections may be uncertain. We represent annotations as possibility distributions. We use the hierarchy defined in the ontology to compute degrees of possibilities. We want to achieve an automation of the fuzzy semantic annotation of web resources.

Keywords: fuzzy semantic annotation, semantic web, domain ontologies, querying web

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Authors: Fateh Adhnouss, Husam El-Asfour, Kenneth McIsaac, AbdulMutalib Wahaishi, Idris El-Feghia

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Conceptualization is an essential component of semantic ontology-based approaches. There have been several approaches that rely on extensional structure and extensional reduction structure in order to construct conceptualization. In this paper, several limitations are highlighted relating to their applicability to the construction of conceptualizations in dynamic and open environments. These limitations arise from a number of strong assumptions that do not apply to such environments. An intensional structure is strongly argued to be a natural and adequate modeling approach. This paper presents a conceptualization structure based on property relations and propositions theory (PRP) to the model ontology that is suitable for open environments. The model extends the First-Order Logic (FOL) notation and defines the formal representation that enables interoperability between software systems and supports semantic integration for software systems in open, dynamic environments.

Keywords: conceptualization, ontology, extensional structure, intensional structure

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Authors: Chaimaa Messaoudi, Rachida Fissoune, Hassan Badir

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Technological advances of computer science and data analysis are helping to provide continuously huge volumes of biological data, which are available on the web. Such advances involve and require powerful techniques for data integration to extract pertinent knowledge and information for a specific question. Biomedical exploration of these big data often requires the use of complex queries across multiple autonomous, heterogeneous and distributed data sources. Semantic integration is an active area of research in several disciplines, such as databases, information-integration, and ontology. We provide a survey of some approaches and techniques for integrating biological data, we focus on those developed in the ontology community.

Keywords: biological ontology, linked data, semantic data integration, semantic web

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Authors: Sabrina Azzi, Michal Iglewski, Véronique Nabelsi

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Diagnosis error problem is frequent and one of the most important safety problems today. One of the main objectives of our work is to propose an ontological representation that takes into account the diagnostic criteria in order to improve the diagnostic. We choose pneumonia disease since it is one of the frequent diseases affected by diagnosis errors and have harmful effects on patients. To achieve our aim, we use a semi-automated method to integrate diverse knowledge sources that include publically available pneumonia disease guidelines from international repositories, biomedical ontologies and electronic health records. We follow the principles of the Open Biomedical Ontologies (OBO) Foundry. The resulting ontology covers symptoms and signs, all the types of pneumonia, antecedents, pathogens, and diagnostic testing. The first evaluation results show that most of the terms are covered by the ontology. This work is still in progress and represents a first and major step toward a development of a diagnosis decision support system for pneumonia.

Keywords: Clinical decision support system, Diagnostic errors, Ontology, Pneumonia

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Authors: Bo Wang

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As the first high-resolution civil optical satellite, ZY-3 satellite is able to obtain high-resolution multi-view images with three linear array sensors. The images can be used to generate Digital Elevation Models (DEM) through dense matching of stereo images. However, due to the clouds, forest, water and buildings covered on the images, there are some problems in the dense matching results such as outliers and areas failed to be matched (matching holes). This paper introduced an algorithm to verify the accuracy of DEM that generated by ZY-3 satellite with Shuttle Radar Topography Mission (SRTM). Since the accuracy of SRTM (Internal accuracy: 5 m; External accuracy: 15 m) is relatively uniform in the worldwide, it may be used to improve the accuracy of ZY-3 DEM. Based on the analysis of mass DEM and SRTM data, the processing can be divided into two aspects. The registration of ZY-3 DEM and SRTM can be firstly performed using the conjugate line features and area features matched between these two datasets. Then the ZY-3 DEM can be refined by eliminating the matching outliers and filling the matching holes. The matching outliers can be eliminated based on the statistics on Local Vector Binning (LVB). The matching holes can be filled by the elevation interpolated from SRTM. Some works are also conducted for the accuracy statistics of the ZY-3 DEM.

Keywords: ZY-3 satellite imagery, DEM, SRTM, refinement

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Authors: Emmanuel Chibuogu Asogwa, Tochukwu Sunday Belonwu

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An urgent demand for creative solutions has been created by the rapid expansion of medical knowledge, the complexity of patient care, and the requirement for more precise decision-making. As a solution to this problem, the creation of a Medical Intelligent Process Model (MIPM) utilizing ontology-based appears as a promising way to overcome this obstacle and unleash the full potential of healthcare systems. The development of a Medical Intelligent Process Model (MIPM) using ontology-based techniques is motivated by a lack of quick access to relevant medical information and advanced tools for treatment planning and clinical decision-making, which ontology-based techniques can provide. The aim of this work is to develop a structured and knowledge-driven framework that leverages ontology, a formal representation of domain knowledge, to enhance various aspects of healthcare. Object-Oriented Analysis and Design Methodology (OOADM) were adopted in the design of the system as we desired to build a usable and evolvable application. For effective implementation of this work, we used the following materials/methods/tools: the medical dataset for the test of our model in this work was obtained from Kaggle. The ontology-based technique was used with Confusion Matrix, MySQL, Python, Hypertext Markup Language (HTML), Hypertext Preprocessor (PHP), Cascaded Style Sheet (CSS), JavaScript, Dreamweaver, and Fireworks. According to test results on the new system using Confusion Matrix, both the accuracy and overall effectiveness of the medical intelligent process significantly improved by 20% compared to the previous system. Therefore, using the model is recommended for healthcare professionals.

Keywords: ontology-based, model, database, OOADM, healthcare

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Authors: A. Elsehemy, M. Abdeen , T. Nazmy

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Since the appearance of the Semantic web, many semantic search techniques and models were proposed to exploit the information in ontology to enhance the traditional keyword-based search. Many advances were made in languages such as English, German, French and Spanish. However, other languages such as Arabic are not fully supported yet. In this paper we present a framework for ontology based information retrieval for Arabic language. Our system consists of four main modules, namely query parser, indexer, search and a ranking module. Our approach includes building a semantic index by linking ontology concepts to documents, including an annotation weight for each link, to be used in ranking the results. We also augmented the framework with an automatic document categorizer, which enhances the overall document ranking. We have built three Arabic domain ontologies: Sports, Economic and Politics as example for the Arabic language. We built a knowledge base that consists of 79 classes and more than 1456 instances. The system is evaluated using the precision and recall metrics. We have done many retrieval operations on a sample of 40,316 documents with a size 320 MB of pure text. The results show that the semantic search enhanced with text classification gives better performance results than the system without classification.

Keywords: Arabic text classification, ontology based retrieval, Arabic semantic web, information retrieval, Arabic ontology

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Authors: Pranjali Avinash Yadav-Deshmukh

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Remarkable development of the Internet along with the new technological innovation, such as high-speed systems and affordable large storage space have led to a tremendous increase in the amount and accessibility to digital records. For any person, studying of all these data is tremendously time intensive, so there is a great need to access effective multi-document summarization (MDS) systems, which can successfully reduce details found in several records into a short, understandable summary or conclusion. For semantic representation of textual details in ontology area, as a theoretical design, our system provides a significant structure. The stability of using the ontology in fixing multi-document summarization problems in the sector of catastrophe control is finding its recommended design. Saliency ranking is usually allocated to each phrase and phrases are rated according to the ranking, then the top rated phrases are chosen as the conclusion. With regards to the conclusion quality, wide tests on a selection of media announcements are appropriate for “Jammu Kashmir Overflow in 2014” records. Ontology centered multi-document summarization methods using “NLP centered extraction” outshine other baselines. Our participation in recommended component is to implement the details removal methods (NLP) to enhance the results.

Keywords: disaster management, extraction technique, k-means, multi-document summarization, NLP, ontology, sentence extraction

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Authors: Yanhua Ma, Lu Zhai, Xinchen Wang, Hongyu Liang

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In this paper, by understanding the development status of the human-machine interface in today's automobile cab, a subjective and objective evaluation system for evaluating the optimization of human-machine interface matching in automobile cab was established. The man-machine interface of the car cab was divided into a software interface and a hard interface. Objective evaluation method of software human factor analysis is used to evaluate the hard interface matching; The analytic hierarchy process is used to establish the evaluation index system for the software interface matching optimization, and the multi-level fuzzy comprehensive evaluation method is used to evaluate hard interface machine. This article takes Dongfeng Sokon (DFSK) C37 model automobile as an example. The evaluation method given in the paper is used to carry out relevant analysis and evaluation, and corresponding optimization suggestions are given, which have certain reference value for designers.

Keywords: analytic hierarchy process, fuzzy comprehension evaluation method, human-machine interface, matching optimization, software human factor analysis

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Authors: Montaceur Zaghdoud, Mohamed Moussaoui, Jalel Akaichi

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Frequent subgraph mining refers usually to graph matching and it is widely used in when analyzing big data with large graphs. A lot of research works dealt with structural exact or inexact graph matching but a little attention is paid to semantic matching when graph vertices and/or edges are attributed and typed. Therefore, it seems very interesting to integrate background knowledge into the analysis and that extracted frequent subgraphs should become more pruned by applying a new semantic filter instead of using only structural similarity in graph matching process. Consequently, this paper focuses on developing a new hybrid approximate structuralsemantic graph matching to discover a set of frequent subgraphs. It uses simultaneously an approximate structural similarity function based on graph edit distance function and a possibilistic vertices similarity function based on affinity function. Both structural and semantic filters contribute together to prune extracted frequent set. Indeed, new hybrid structural-semantic frequent subgraph mining approach searches will be suitable to be applied to several application such as community detection in social networks.

Keywords: approximate graph matching, hybrid frequent subgraph mining, graph mining, possibility theory

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Authors: Yuan Zheng

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3D model-based vehicle matching provides a new way for vehicle recognition, localization and tracking. Its key is to construct an evaluation function, also called fitness function, to measure the degree of vehicle matching. The existing fitness functions often poorly perform when the clutter and occlusion exist in traffic scenarios. In this paper, we present a practical and efficient fitness function. Unlike the existing evaluation functions, the proposed fitness function is to study the vehicle matching problem from both local and global perspectives, which exploits the pixel gradient information as well as the silhouette information. In view of the discrepancy between 3D vehicle model and real vehicle, a weighting strategy is introduced to differently treat the fitting of the model’s wireframes. Additionally, a normalization operation for the model’s projection is performed to improve the accuracy of the matching. Experimental results on real traffic videos reveal that the proposed fitness function is efficient and robust to the cluttered background and partial occlusion.

Keywords: 3D-2D matching, fitness function, 3D vehicle model, local image gradient, silhouette information

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Authors: Hao-Hsiang Ku, Ching-Ho Chi

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Big data applications have become an imperative for many fields. Many researchers have been devoted into increasing correct rates and reducing time complexities. Hence, the study designs and proposes an Ontology-based backpropagation neural network classification and reasoning strategy for NoSQL big data applications, which is called ON4NoSQL. ON4NoSQL is responsible for enhancing the performances of classifications in NoSQL and SQL databases to build up mass behavior models. Mass behavior models are made by MapReduce techniques and Hadoop distributed file system based on Hadoop service platform. The reference engine of ON4NoSQL is the ontology-based backpropagation neural network classification and reasoning strategy. Simulation results indicate that ON4NoSQL can efficiently achieve to construct a high performance environment for data storing, searching, and retrieving.

Keywords: Hadoop, NoSQL, ontology, back propagation neural network, high distributed file system

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Authors: Said Boularouk, Didier Josselin, Eitan Altman

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In this paper, we present a vocal ontology of OpenStreetMap data for the apprehension of space by visually impaired people. Indeed, the platform based on produsage gives a freedom to data producers to choose the descriptors of geocoded locations. Unfortunately, this freedom, called also folksonomy leads to complicate subsequent searches of data. We try to solve this issue in a simple but usable method to extract data from OSM databases in order to send them to visually impaired people using Text To Speech technology. We focus on how to help people suffering from visual disability to plan their itinerary, to comprehend a map by querying computer and getting information about surrounding environment in a mono-modal human-computer dialogue.

Keywords: TTS, ontology, open street map, visually impaired

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Authors: András Rövid, Takeshi Hashimoto

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The paper focuses on the problem of the point correspondence matching in stereo images. The proposed matching algorithm is based on the combination of simpler methods such as normalized sum of squared differences (NSSD) and a more complex phase correlation based approach, by considering the noise and other factors, as well. The speed of NSSD and the preciseness of the phase correlation together yield an efficient approach to find the best candidate point with sub-pixel accuracy in stereo image pairs. The task of the NSSD in this case is to approach the candidate pixel roughly. Afterwards the location of the candidate is refined by an enhanced phase correlation based method which in contrast to the NSSD has to run only once for each selected pixel.

Keywords: stereo matching, sub-pixel accuracy, phase correlation, SVD, NSSD

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Authors: Estela Mayra de Moura Vianna, Thiago José Tavares Ávila, Bruno Morais Silva, Diego Henrique Bezerra, Paulo Henrique Gomes Silva, Alan Pedro da Silva

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The Brazilian Federal Constitution defines the access to information as a crucial right of the citizen and the Law on Access to Public Information, which regulates this right. Accordingly, the Fiscal Responsibility Act, 2000, amended in 2009 by the “Law of Transparency”, began demanding a wider disclosure of public accounts for the society, including electronic media for public access. Thus, public entities began to create "Transparency Portals," which aim to gather a diversity of data and information. However, this information, in general, is still published in formats that do not simplify understanding of the data by citizens and that could be better especially available for audit purposes. In this context, a proposal of ontology about Brazilian Transparency Portal can play a key role in how these data will be better available. This study aims to identify and implement in ontology, the data model about Transparency Portal ecosystem, with emphasis in activities that use these data for some applications, like audits, press activities, social government control, and others.

Keywords: audit, government transparency, ontology, public sector

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Authors: Mohd Ridzwan Yaakub, Muhammad Iqbal Abu Latiffi

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Online business has become popular recently due to the massive amount of information and medium available on the Internet. This has resulted in the huge number of reviews where the consumers share their opinion, criticisms, and satisfaction on the products they have purchased on the websites or the social media such as Facebook and Twitter. However, to analyze customer’s behavior has become very important for organizations to find new market trends and insights. The reviews from the websites or the social media are in structured and unstructured data that need a sentiment analysis approach in analyzing customer’s review. In this article, techniques used in will be defined. Definition of the ontology and description of its possible usage in sentiment analysis will be defined. It will lead to empirical research that related to mobile phones used in research and the ontology used in the experiment. The researcher also will explore the role of preprocessing data and feature selection methodology. As the result, ontology-based approach in sentiment analysis can help in achieving high accuracy for the classification task.

Keywords: feature selection, ontology, opinion, preprocessing data, sentiment analysis

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Authors: Weitao Lin

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To quickly extract adequate information from large-scale unstructured text data, this paper studies the representation of events in Chinese scenarios and performs the regularized abstraction. It proposes a Chinese event detection technique based on dependency parsing and rule matching. The method first performs dependency parsing on the original utterance, then performs pattern matching at the word or phrase granularity based on the results of dependent syntactic analysis, filters out the utterances with prominent non-event characteristics, and obtains the final results. The experimental results show the effectiveness of the method.

Keywords: natural language processing, Chinese event detection, rules matching, dependency parsing

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Authors: C. -L. Huang, J. -H. Ho

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Social media has become more and more important in our life. Many enterprises promote their services and products to fans via the social media. The positive or negative sentiment of feedbacks from fans is very important for enterprises to improve their products, services, and promotion activities. The purpose of this paper is to understand the sentiment of the fan’s responses by analyzing the responses posted by fans on Facebook. The entity and aspect of fan’s responses were analyzed based on a predefined ontology. The ontology for cell phone sentiment analysis consists of aspect categories on the top level as follows: overall, shape, hardware, brand, price, and service. Each category consists of several sub-categories. All aspects for a fan’s response were found based on the ontology, and their corresponding sentimental terms were found using lexicon-based approach. The sentimental scores for aspects of fan responses were obtained by summarizing the sentimental terms in responses. The frequency of 'like' was also weighted in the sentimental score calculation. Three famous cell phone fan pages on Facebook were selected as demonstration cases to evaluate performances of the proposed methodology. Human judgment by several domain experts was also built for performance comparison. The performances of proposed approach were as good as those of human judgment on precision, recall and F1-measure.

Keywords: opinion mining, ontology, sentiment analysis, text mining

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Authors: Bum-Soo Kim, Jin-Uk Kim

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In this paper, we design and implement a partial denoising boundary image matching system using indexing techniques. Converting boundary images to time-series makes it feasible to perform fast search using indexes even on a very large image database. Thus, using this converting method we develop a client-server system based on the previous partial denoising research in the GUI (graphical user interface) environment. The client first converts a query image given by a user to a time-series and sends denoising parameters and the tolerance with this time-series to the server. The server identifies similar images from the index by evaluating a range query, which is constructed using inputs given from the client, and sends the resulting images to the client. Experimental results show that our system provides much intuitive and accurate matching result.

Keywords: boundary image matching, indexing, partial denoising, time-series matching

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Authors: F. H. Abanda, B. Kamsu-Foguem, J. H. M. Tah

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Construction cost estimation is one of the most important aspects of construction project design. For generations, the process of cost estimating has been manual, time-consuming and error-prone. This has partly led to most cost estimates to be unclear and riddled with inaccuracies that at times lead to over- or under-estimation of construction cost. The development of standard set of measurement rules that are understandable by all those involved in a construction project, have not totally solved the challenges. Emerging Building Information Modelling (BIM) technologies can exploit standard measurement methods to automate cost estimation process and improves accuracies. This requires standard measurement methods to be structured in ontologically and machine readable format; so that BIM software packages can easily read them. Most standard measurement methods are still text-based in textbooks and require manual editing into tables or Spreadsheet during cost estimation. The aim of this study is to explore the development of an ontology based on New Rules of Measurement (NRM) commonly used in the UK for cost estimation. The methodology adopted is Methontology, one of the most widely used ontology engineering methodologies. The challenges in this exploratory study are also reported and recommendations for future studies proposed.

Keywords: BIM, construction projects, cost estimation, NRM, ontology

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