Ontology-based Semantic Retrieval Research Articles

Ontology-based semantic retrieval of documents using Word2vec model

Semantic retrieval of engineering knowledge is crucial in various engineering activities, such as process development and product model planning. To make a solution for this issue, previously, a few word-based semantic enabling existing approaches such as Lexical Retrieval Model with Semantic Residual Embedding’s (LR-SRE), Document Retrieval Model through Semantic Linking (DR-MTS), Semantic Term Matching in Axiomatic Approaches to Information Retrieval (STM-IR) and Hybrid Ontology for Semantic Information Retrieval Model using Keyword Matching Indexing System (HOS-IR) were utilized. But, they all have shown less query-based accuracy results than the required value. In our proposed Information Retrieval (IR) design, the semantic knowledge-based retrieval scheme has been implemented. For query, entered by a user and processed for finding the dominated word. Word is then compared for its similarity equations, and similarity values are then computed to give output. Highly similar values are obtained as the class value. From class, the respective clusters are selected. Then, documents in that cluster are retrieved and ranked according to the relevance of the user. To support the accuracy level performance of this IR system, a word2vec model has been employed with the benefits of Horse Herd Optimization (HHO), which helps to extract vectors as features for classification. These results are stored as a .csv file for further retrieval. By implementing the proposed IR-word2vec model, the results showed that it outperforms other existing techniques by improved similarity index and accuracy for query results in an execution time of 1.7 s.

Ontology-Based Semantic Retrieval for Durian Pests and Diseases Control System

In Southeast Asia, durian is affectionately called the king of fruit. Durian is the most popular crop planted in eastern and southern of Thailand. The total crop is around 600,000 tons per year; among this, 500,000 tons of the total production were exported worldwide. In Thailand, the knowledge of durian production is based on experience from generation to generation, especially the knowledge of durian pests and diseases control. This paper presents the ontology knowledge based for durian pests and diseases retrieval system. The major contributions of the system consist of 1) the stored knowledge of durian pests and diseases and 2) the diagnosis of durian diseases and the suggestions for the treatments. The ontology knowledge consists of 8 main classes: 1) diseases, 2) pests, 3) cultivars, 4) symptoms of bunch, 5) leaf area symptoms, 6) symptoms of the branches and trunk, 7) symptoms of fruit, and 8) symptoms of root and growth. The experimental results yielded 100% of precision, 88.33% of recall, and 93.8% of overall performance.

An ontology-based semantic retrieval approach for heterogeneous 3D CAD models

Many types of heterogeneous CAD models created by previous product development activities are stored in enterprise databases. Searching reusable CAD models for engineer design requirements is still very difficult. In this paper, we propose an ontology-based semantic retrieval approach for heterogeneous 3D CAD models by using semantic web theory and information retrieval theory. By exploiting layered feature ontology and ontology mapping, a uniform description for heterogeneous CAD models is generated as semantic descriptor. Meanwhile, to improve the representative ability, several heuristic rules are constructed with the aid of SWRL language and ontology reasoning is acted on the semantic descriptors, which makes our retrieval system achieve better performance. Furthermore, multi-mode similarity measurement is carried out to meet engineers’ diversified requirements. We demonstrate the feasibility and effectiveness of our approach through experiments on heterogeneous 3D CAD model library.

A Semantic Retrieval Model Based on Domain Ontology of Orchard Disease and Pests

Ontology-based semantic retrieval can im- prove the efficiency of information retrieval. This paper proposes a semantic retrieval model based on domain on- tology of orchard disease and pests. According to Forestry Thesaurus, we semi-automatically construct a domain on- tology and repair ontology inconsistency to ensure the ac- curacy and uniqueness of the domain knowledge. A concept similarity algorithm is proposed and applied to calculate sentence similarity. We present a synthetic sentence simi- larity algorithm, which is a combination of the traditional sentence similarity algorithm and the weighted sentence similarity algorithm. Compared with other related meth- ods through experiments, our retrieval model has higher accuracy in semantic retrieval.

Ontology-Based Semantic Retrieval for Education Management Systems

The traditional information retrieval technologies are based on keywords, and therefore provide limited capabilities to capture the conceptualizations associated with user needs and contents. As a new technology of information retrieval, semantic retrieval can retrieve information resource fully and precisely based on the knowledge understanding and knowledge reasoning. Ontology, which can well represent and reason about the domain knowledge, is proved to be very useful in the semantic retrieval. On this basis, in this paper, we propose a complete ontology-based semantic retrieval approach and framework for education management system. Firstly, we present some rules for constructing domain ontology from the education management system; Then, a semantic annotation method of the constructed ontology is given; Further, the ontologybased semantic retrieval algorithmis proposed; Finally, a complete framework is developed and some experiments are done. Conducted experiments show that our semantic retrieval model obtained comparable and better performance results than the traditional information retrieval technology for education management system.

Research of Conceptual Relation Extraction Based on Improved Hierarchical Clustering Method

The main task of Ontology learning is concept extraction and conceptual relation extraction. This paper mainly studies the latter. Conceptual relation consists of taxonomic relation and non-taxonomic relation. It introduces hierarchy clustering method, and uses concept hierarchy clustering method which chooses different clustering standards in each hierarchy to obtain the taxonomic relation. It improves the accuracy of the relationship extraction. For extracting the nontaxonomic relation, this paper uses a extended association rule, this method can get concrete names of relation, and confirms the domain and range. In the end, the paper uses the introduced method of Ontology Learning to constructing a domain ontology in the law. And it completes the implementation of an Ontology-based semantic retrieval system. The final effect of this system application demonstrates that this Ontology learning method is efficient.

Ontology-based semantic retrieval for mechanical design knowledge

The semantic retrieval of mechanical domain knowledge is critical in product design process. To address the problems with existing keyword-based and semantic-enabled methods, an ontology-based search system (OBSS) for knowledge search and retrieval from knowledge base is proposed. In our scheme, the mechanical design knowledge ontology is first constructed, and query semantic extension and retrieval are then adopted for knowledge retrieval. For the query semantic extension, the semantic similarity analysis is adopted to discover the semantic distance between the semantic key in query and its extended semantic keys. Then, the query boosts of extended semantic keys are modified by the semantic similarity. In the precision and recall experiments, the results demonstrate that our method outperforms the keyword-based search technique. Further, two query cases ensure that the semantic retrieval algorithm based on domain ontology could understand the latent search intents. This research contributes to the query intent capture and the application of engineering ontology for design knowledge retrieval.

Ontology-based Semantic Retrieval for Risk Management of Construction Project

Risk management is crucial for successful construction project management. Effective risk assessment can improve construction enterprises’ competitiveness and profitability to a great extent. And the information of previous projects is a very precious resource and reference for construction project risk management. In order to facilitate project participants to query required information of previous projects among numerous project documents efficiently, this paper introduces ontology as the modeling approach for information exchange and puts forward a semantic retrieval method based on this ontology for construction project risk management. At first, this paper introduces the procedure of constructing risk domain ontology by means of seven footwork modeling method, then the methods of construction projects’ document processing and querying sentences’ expansion are presented. Finally, this paper put forward a semantic retrieval system based on construction project risk domain ontology which can correlate the ontology conceptual knowledge and the documents’ content. Experimental results suggest that the proposed ontology-based semantic retrieval method can improve information’s semantic expression ability and realize the intellectualization of construction project risk management.

Ontology-based semantic retrieval for engineering domain knowledge

The semantic retrieval of the engineering domain knowledge is critical in many engineering activities, e.g., product design and process planning. To address the problems with existing keyword-based and semantic-enable methods, we propose an ontology-based semantic retrieval scheme for knowledge search and retrieval from domain documents. In our scheme, domain ontology is first constructed using the graph-based approach to automating construction of domain ontology GRAONTO proposed by our group, and query semantic extension and retrieval are then adopted for semantic-based knowledge retrieval. For query semantic extension, latent semantic analysis is adopted to discover the latent semantic relationships between queries and ontology semantic features, and ontology semantic graph is used to represent the query. For semantic retrieval, a graph-based k-means method is proposed to partition the domain documents into several clusters, and a hierarchical searching strategy is employed for document retrieval. Finally, experimental results on the fixture design corpus verify the benefits of the proposed scheme.

Research on Emergency Plan Ontology Model

Emergency plan is a project document to guide and deal with public emergency events, and also is the core of emergency management. Nowadays, most emergency plans in China exist in non-semantic form and are difficult to play roles effectively. Ontology is an important method to describe semantic model. Therefore, in this paper, emergency plan ontology model is constructed by ontology technology of semantic Web and Web Ontology Language OWL, which formally describes the conceptions of emergency plan and the relations between them. Finally, the model is used in ontology-based semantic retrieval system, and improves the retrieval recall and precision.

Survey of Ontology-based Semantic Retrieval in Folksonomy

Survey of Ontology-based Semantic Retrieval in Folksonomy

Semantic Retrieval Based on SPARQL and Fuzzy Ontology for Electronic Commerce

Information retrieval is the important work for Electronic Commerce. Ontology-based semantic retrieval is a hotspot of current research. In order to achieve fuzzy semantic retrieval, this paper proposes an approach using Resource Description Framework (RDF) and fuzzy ontology. First, we apply RDF/RDFS data model to represent e-commerce information on the Semantic Web. Then, introducing new data type referred as fuzzy linguistic variables to RDF data model, the semantic query expansion in SPARQL query language is constructed by order relation, equivalence relation and inclusion relation between fuzzy concepts defined in linguistic variable ontologies. Examples show that this research facilitates the semantic retrieval through fuzzy concepts for Electronic Commerce on the Semantic Web.

Agricultural Policy-Oriented Ontology-Based Semantic Information Retrieval

Research on architecture of ontology-based information semantic representation and Retrieval is done. As a case study, a prototype for agricultural policy-oriented ontology-based semantic information retrieval system (APOSIRS) is established. Ontology plays a role that providing a shared terminology and supporting for the retrieval process. The architecture allows APOSIRS-based applications to perform automatic semantic information Retrieval of agricultural policy text at more length: automatic and dynamic semantic annotation of unstructured and semi-structured content, semantically-enabled information extraction, indexing, retrieval, as well as ontology management, such as querying and modifying the underlying ontology and knowledge bases. Main components of this architecture have been implemented and their results are reported.

Personalized ontology-based semantic annotation and retrieval of image model

In order to solve the problem that it is difficult to implement semantic retrieval of image,a novel ontology-cored semantic annotation and retrieval model of image was proposed,which constructed a personalized ontology to describe image,extracted the image semantic features based on the concept set and used inter-relationship "Is-A" of ontology to measure the similarity between the images and query.The difficulty lies in the personalized evolution of the top ontology,as well as the semantic similarity measure based on concept set and inter-relationship " Is-A ".This model was implemented and some related experiments were carried out.The experimental results validate the retrieval accuracy as high as 88.6%,significantly higher than the traditional keyword-based and generic ontology-based image retrieval models,which realized intelligent retrieval of image.

Ontology-based Enterprise Content Retrieval Method

As the traditional way of information retrieval is based on keywords that the computer cannot understand users’ potential semantic and personalized query requirements, an ontology-based method of the enterprise content retrieval is studied in this paper. The architecture of the ontology-based enterprise content retrieval model is proposed and the retrieval process is discussed. Take the electronic products distributing company as an example, the enterprise ontology is set up and an ontology-based semantic retrieval algorithm is designed, which provides a reference for content retrieval in the enterprises.

Semantic Information Retrieval Based on Fuzzy Ontology for Electronic Commerce

Information retrieval is the important work for Electronic Commerce. Ontology-based semantic retrieval is a hotspot of current research. In order to achieve fuzzy semantic retrieval, this paper applies a fuzzy ontology framework to information retrieval system in E-Commerce. The framework includes three parts: concepts, properties of concepts and values of properties, in which property’s value can be either standard data types or linguistic values of fuzzy concepts. The semantic query expansions are constructed by order relation, equivalence relation, inclusion relation, reversion relation and complement relation between fuzzy concepts defined in linguistic variable ontologies with Resource Description Framework (RDF). The application to retrieve customer, product and supplier information shows that the framework can overcome the localization of other fuzzy ontology models, and this research facilitates the semantic retrieval of information through fuzzy concepts on the Semantic Web.

Ontology-Based Semantic Indexing for MPEG-7 and TV-Anytime Audiovisual Content

In this paper, we describe a framework that we have developed for the support of ontology-based semantic indexing and retrieval of audiovisual content following the MPEG-7 and TV-Anytime standard specifications for metadata descriptions. Our work aims to provide a methodology to enhance the retrieval effectiveness of audiovisual content, while maintaining compatibility with the international multimedia standards. In our framework, domain-specific ontologies guide the definition of both the application-specific metadata and the instance-description metadata that describe the contents of audiovisual programs and/or their segments. The ontologies are also used in order to provide compatible descriptions in both audiovisual content standards (MPEG-7 and TV-Anytime) for the same content. This approach allows indexing compatibility and interoperability of TV-Anytime and digital library applications. We describe the design and implementation of a system supporting this framework. The components of the system include a segmentation tool for segmenting audiovisual information, which also provides ontology-based semantic indexing capabilities, and an appropriate API for semantic query support. An application testbed for the domain of soccer games has been developed on top of this system. An ontology for soccer games has been defined and used for indexing and retrieval of soccer games that have been stored in the system database. The methodology we developed opens up a wide opportunity for the creation of MPEG-7 and TV-Anytime services offering structured domain-specific ontologies that can be integrated to these standards for enhancing audiovisual content retrieval performance.