Semantic Web Information Retrieval Based on the Wordnet

[[abstract]]Most of the existing textual information retrieval approaches depend on a lexical match between words in user’s requests and words in target objects. Typically only objects that contain one or more common words with those in the user’s query are returned as relevant. This lexical based retrieval model is far from ideal. In this research an approach to semantic based information retrieval of semantically annotated documents is presented. The approach operates based on: (i).natural language understanding, (ii).the Wordnet ontology, and (iii).the Semantic web standards. Not only the information is annotated and searched on a semantic basis, but also the retrieval process can be enhanced by the use of rich vocabulary knowledge in the ontology.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]EI[[ispeerreviewed]]Y[[booktype]]紙本[[countrycodes]]KO

Hybrid Ontology for Semantic Information Retrieval Model Using Keyword Matching Indexing System

Ontology is the process of growth and elucidation of concepts of an information domain being common for a group of users. Establishing ontology into information retrieval is a normal method to develop searching effects of relevant information users require. Keywords matching process with historical or information domain is significant in recent calculations for assisting the best match for specific input queries. This research presents a better querying mechanism for information retrieval which integrates the ontology queries with keyword search. The ontology-based query is changed into a primary order to predicate logic uncertainty which is used for routing the query to the appropriate servers. Matching algorithms characterize warm area of researches in computer science and artificial intelligence. In text matching, it is more dependable to study semantics model and query for conditions of semantic matching. This research develops the semantic matching results between input queries and information in ontology field. The contributed algorithm is a hybrid method that is based on matching extracted instances from the queries and information field. The queries and information domain is focused on semantic matching, to discover the best match and to progress the executive process. In conclusion, the hybrid ontology in semantic web is sufficient to retrieve the documents when compared to standard ontology

Investigating ontology based query expansion using a probabilistic retrieval model

This research briefly outlines the problems of traditional information retrieval systems and discusses the different approaches to inferring context in document retrieval. By context we mean word disambiguation which is achieved by exploring the generalisation-specialisation hierarchies within a given ontology. Specifically, we examine the use of ontology based query expansion for defining query context. Query expansion can be done in many ways and in this work we consider the use of relevance feedback and pseudo-relevance feedback for query expansion. We examine relevance feedback and pseudo-relevance to ascertain the existence of performance differences between relevance feedback and pseudo-relevance feedback. The information retrieval system used is based on the probabilistic retrieval model and the query expansion method is extended using information from a news domain ontology. The aim of this project is to assess the impact of the use of the ontology on the query expansion results. Our results show that ontology based query expansion has resulted in a higher number of relevant documents being retrieved compared to the standard relevance feedback process. Overall, ontology based query expansion improves recall but does not produce any significant improvements for the precision results. Pseudo-relevance feedback has achieved better results than relevance feedback. We also found that reducing or increasing the relevance feedback parameters (number of terms or number of documents) does not correlate with the results. When comparing the effect of varying the number of terms parameter with the number of documents parameter, the former benefits the pseudo-relevance feedback results but the latter has an additional effect on the relevance feedback results. There are many factors which influence the success of ontology based query expansion. The thesis discusses these factors and gives some guidelines on using ontologies for the purpose of query expansion

Effectiveness of simple terminological ontology to support document retrieval in a specialized domain / Seyed Abolfazle Moosavifar

The research investigated the proposition that a simple terminological ontology supported by general purpose lexical resources and aided by information retrieval and natural language processing techniques can effectively annotate and retrieve documents in a specialised knowledge domain. This is addressing the evidence from a recent survey, which reported that low satisfaction in the retrieval of documents in a personal collection. A common, but robust approach in this area is keyword-based retrieval. The weakness of keyword-based retrieval is its inability to ‘understand’ the meaning of the keywords (semantic). Ontology approach is introduced as a way to support semantic retrieval. However, there is a problem with the construction of the ontology by laymen, especially ontologies for specialised domain areas. Therefore, the use of simple terminological ontology (constructed based on intuitive understanding of the domain) is proposed in this research. The research objectives are structured to introduce new algorithms for ontology-based automatic annotation, retrieval and ranking of documents and to check on the reliability of WordNet to provide lexical support for the (simple terminological) ontology-based document retrieval. To achieve these objectives, the Boolean IR model was extended by incorporating four coefficients to adjust the term weights, namely to deal with the word significance and word coherence in multi-word terms, to consider the matching type (exact or synonym) and to factor the category weight when calculating the term weights. To find the retrieval effectiveness, the results of ontology-based retrieval was evaluated against the conventional retrieval, and validated against expert retrieval. The results of the ontology-based automatic annotation were evaluated against expert annotation. In addition, the reliability of using WordNet to provide lexical support was tested during the process of the annotation and retrieval. The research found synonyms from WordNet selected with the correct senses can help to improve the (simple terminological) ontology-based annotation and retrieval of documents in a specialised domain area. The research also found that (simple terminological) ontology-based retrieval that is support by selected synonyms from WordNet can recall all documents that are retrieved using keyword-based retrieval with reasonable precision. The evaluations of the retrieval by get help from expert domain also emphasized this result. The research result also indicated there are few common tags between the automatic and expert annotation. There were issues with the expert annotations; nonetheless, if we regard the expert annotation is paramount, then we suggest semiautomatic annotation of the documents in order to improve the result of ontologybased retrieval. Future researchers can use our research ideas (e.g. annotation and retrieval algorithms; assignment of weights to ontology terms) to make further progress in the field of semantic information retrieval. System designers can base our research findings (e.g. type of lexical support) to decide on methods for improving the retrieval in personal collection

TREE-D-SEEK: A Framework for Retrieving Three-Dimensional Scenes

In this dissertation, a strategy and framework for retrieving 3D scenes is proposed. The strategy is to retrieve 3D scenes based on a unified approach for indexing content from disparate information sources and information levels. The TREE-D-SEEK framework implements the proposed strategy for retrieving 3D scenes and is capable of indexing content from a variety of corpora at distinct information levels. A semantic annotation model for indexing 3D scenes in the TREE-D-SEEK framework is also proposed. The semantic annotation model is based on an ontology for rapid prototyping of 3D virtual worlds. With ongoing improvements in computer hardware and 3D technology, the cost associated with the acquisition, production and deployment of 3D scenes is decreasing. As a consequence, there is a need for efficient 3D retrieval systems for the increasing number of 3D scenes in corpora. An efficient 3D retrieval system provides several benefits such as enhanced sharing and reuse of 3D scenes and 3D content. Existing 3D retrieval systems are closed systems and provide search solutions based on a predefined set of indexing and matching algorithms Existing 3D search systems and search solutions cannot be customized for specific requirements, type of information source and information level. In this research, TREE-D-SEEK—an open, extensible framework for retrieving 3D scenes—is proposed. The TREE-D-SEEK framework is capable of retrieving 3D scenes based on indexing low level content to high-level semantic metadata. The TREE-D-SEEK framework is discussed from a software architecture perspective. The architecture is based on a common process flow derived from indexing disparate information sources. Several indexing and matching algorithms are implemented. Experiments are conducted to evaluate the usability and performance of the framework. Retrieval performance of the framework is evaluated using benchmarks and manually collected corpora. A generic, semantic annotation model is proposed for indexing a 3D scene. The primary objective of using the semantic annotation model in the TREE-D-SEEK framework is to improve retrieval relevance and to support richer queries within a 3D scene. The semantic annotation model is driven by an ontology. The ontology is derived from a 3D rapid prototyping framework. The TREE-D-SEEK framework supports querying by example, keyword based and semantic annotation based query types for retrieving 3D scenes

Keyword based profile creation using latent dirichlet allocation, domain dictionary and domain ontology / Nor Adzlan Jamaludin

Expert Finding is a field in information retrieval that focuses on finding an expert based on several criteria. Some of the methods that have been applied for expert finding include statistical, machine learning and ontology-based methods. Profile creation is one of the steps or tasks that are required in expert finding, which is the process of capturing and representing the details of experts and users which later can be used for retrieval. An issue that is faced for profile creation in expert finding is that the profiles being created are focused on the details of the experts but not on the users who are searching for these experts. This research explores a profile creation model that creates domain specific keyword-based profiles of users using Latent Dirichlet Allocation, domain dictionary and domain ontology from bookmarks. The domain of agriculture is selected as the case study for this research. The model is implemented in a form of a prototype and is evaluated by comparing how similar the prototype created profiles with manually built ones. From the results and analysis of the research, it is concluded that the method can successfully create domain specific profiles. The significances and contributions of the research include the application of LDA in user profiling, the proposed model, model prototype and the results and findings of the experiments conducted throughout the research

The design and implementation of Malaysian indigenous herbs knowledge management system based on ontology model

This paper introduces the design and implementation of an Ontology-Based Malaysian Herbal Knowledge Based System (MiHerbs).The ontology model used in this research is based on a previous research with title, ’Malaysia Indigenous Herbs Knowledge Representation’.The research proposed an Ontology based knowledge representation model of Malaysian indigenous herbs using Web Ontology Language (OWL).The model can be used to encode and store knowledge in a “Knowledge Base” such as database, repository or library. The model also can enhance search formulation in information retrieval of herbal knowledge with ease, fast and accurate.However, the back end databases which is based on the OWL language needs to be transformed to relational database format. The transformation from OWL to Relational database is based on the OWL2DB algorithm guideline will be further discussed in this research.Assisted by the System Development Life Cycle (SDLC) methodology, MiHerbs is expected to help herbal research agencies, private sector and government to store and share their herbal related information via the system to provide an ease information access to public or even people around the world

Transformation of Extracted Knowledge in Malay Unstructured Documents Into an Interrogative Structured Form

The availability of knowledge discovery operation helps to extract valuable information and knowledge in large volumes of data in structured databases. However, a large portion of the available information is not in structured form but rather collections of text documents in unstructured format, which also implies to Malay unstructured documents. Therefore, structuring characteristics must be imposed to unstructured documents in order to transform information available in unstructured documents into knowledge. A new approach has been established to transform extracted knowledge in Malay unstructured document by identifying, organizing, and structuring them into interrogative structured form. Its architecture is developed based on the implementation of (i) interrogative knowledge identification; (ii) interrogative contextual information; and (iii) interrogative knowledge organization and structuring with Malay knowledge representation by concepts. It utilizes the Malay language corpus; interrogative theory; as well as object-oriented, ontology, and database model. The research involves system development based on architecture of the MalaylK-Ontology, which is being measured by quantitative retrieval performance using the recall and precision metrics. The development of the Retrieval lnterrogative Ontology Analysis Application is used to verify fitness of task for the functionalities and usefulness on the utilization of interrogative contextual information with color coding supplement, additional information annotation, and Malay knowledge representation by concepts. A number of experiments are carried out to quantify the accuracy of knowledge extracted. The MalaylK-Ontology is tested by using stratified random sampling drawn from various sources of Malay unstructured documents such as news, e-mails, articles, magazines, and texts from children story books. The results of the experiments have proved that the approach of MalaylK-Ontology performed well as compared to knowledge extracted manually done by an expert. The results of questionnaires evaluation on the Retrieval lnterrogative Ontology Analysis Application have shown good achievement in understanding the main point of the unstructured document easily and clearly. This is to improve better understanding the process of making sense of information into knowledge, maintaining the meaning of the information and gaining the interpretation of the identical knowledge in unstructured document which facilitate identical knowledge perceived by different people

Finding the best visualization of an ontology

An ontology is a classification model for a given domain. In information retrieval ontologies are used to perform broad searches. An ontology can be visualized as nodes and edges. Each node represents an element and each edge a relation between a parent and a child element. Working with an ontology becomes easier with a visual representation. An idea is to use the expressive power that a 3D representation to provide visualization for the user. In this paper we propose a new method for positioning the elements of the visualized concept lattice in the 3D world based on Operations Research (OR) methods. One method uses a discrete location model to create an initial solution and we propose heuristic methods to further improve the visual result. We evaluate the visual results according to our success criteria and the feedback from users. Running times of the heuristic indicate that an improved version should be feasible for on-line processing and what-if analysis of ontologies.

Multi-modality ontology semantic image retrieval with user interaction model / Mohd Suffian Sulaiman

Interest in the production and potential of digital images has increased greatly in the past decade. The extensive use of digital technologies produces millions of digital images daily. However, the capabilities of technologies equipment manifest the difficulty and challenge for the user to retrieve or search the visual information especially in a large and varieties of a collection. The issues of time consuming for tagging the image, often subject to individual interpretation and lack of ability for a computer to understand the semantic high-level human understanding of image become the former approaches unable to provide an effective solution to this problem. In addressing this problem, this research explores the techniques developed to combine textual description with visual features to form as multi-modality ontology. This semantic technology is chosen due to the ability to mine, interpret and organise the knowledge. Ontology can be seen as a knowledge base that can be used to improve the image retrieval process with the aim of reducing the semantic gap between visual features and high-level semantics. To achieve this aim, multi-modality ontology semantic image retrieval model is proposed. Four main components comprising resource identification, information extraction, knowledge-based construction and image retrieval mechanism are the main tasks need to be implemented in this model. In order to enhance the retrieval performance, the ontology is combined with user interaction by exploiting the ontology relationship. This approach is proposed based on an adaptation from a part of relevance feedback concept. To realise this approach, the semantic image retrieval prototype is developed based on the existing foundation algorithm and customised to provide the ability for user engagement in order to enhance the retrieval performance. To measure the retrieval performance, the ontology evaluation needs to be done first. The correctness of ontology content between the referred corpus and the notation of the ontology is important to make sure the reliability of the proposed approach. Twenty samples of natural language queries are used to test the retrieval performance through the generating of the SPARQL query automatically to access the metadata in the ontology. The graphical user interface is designed to display the image retrieval results. Based on the results, the retrieval performance is measured quantitatively by using precision, recall, accuracy and F-measure techniques. An experiment shows that the proposed model has an average accuracy 0.977, precision 0.797, recall 1.000 and F-measure 0.887 compared to text-based image retrieval, 0.666 (accuracy), 0.160 (precision), 0.950 (recall) and 0.275 (F-measure); textual ontology, 0.937 (accuracy), 0.395 (precision), 0.900 (recall) and 0.549 (F-measure); visual ontology, 0.984 (accuracy), 0.229 (precision), 0.300 (recall) and 0.260 (F-measure); multi-modality ontology, 0.920 (accuracy), 0.398 (precision), 1.000 (recall) and 0.569 (F-measure). In conclusion, results of the proposed model demonstrated better performance in order to reduce the semantic gap, enhance the semantic image retrieval performance and provide the easy way for the user to retrieve the herbal medicinal plant images