Papers by Keyword: Domain Ontology

Abstract: Keyword-based online book retrieval can not fully understand the user's query intent. Query expansion is a typical solution, but the rate of recall and precision is still very low in existing methods. In response to these problems, this paper presents a semantic query expansion method based on domain ontology and local co-occurrence probability model. First, ontology reasoning and concepts related calculation are used to obtain the initial expansion terms. Furthermore, the local co-occurrence probability model is used to filter the candidate expansion terms and the filtering function is used for secondary selection. Experiment results show that this method can effectively improve retrieval efficiency.

Abstract: Vessel survey is a kind of technical method to ensure the health and safety of the vessel. The main purpose of the paper is to find a way to extract workflow of vessel survey field operation (VSFO) directly from rule-based document (RBD) of the field, introduces the relevant researches on the vessel survey field, and analyzes the novel way to improve the quality of vessel survey via intelligentized aided technologies. The establishment of VSFO workflow is treated as the major concern in the work, and relative studies in other fields are introduced. After the comparison, three characteristics of VSFO workflow are discussed. On the basis of previous studies, a semantic approach of the establishment is proposed, and the whole process consists of several steps. Firstly, the structure of RBD in vessel survey is discussed; meanwhile, vessel survey domain ontology (VSDO) is built with rules in RBD. Secondly, the classification algorithm is designed with the original framework provided by domain experts. Finally, the distance calculation of clustering is divided into three parts: reachable distance, structural complexity and survey content complexity.

Abstract: For the parts information describing in decision-making process of CAPP system, with the concept of domain ontology, part information model, which includes four information layer(parts layer, feature layer, topology layer and process layer), has been established, a hierarchical description of process information has been achieved while build a domain ontology model of shaft parts by using the protégé, a ontology editing software. using a stepped shaft part as example illustrate the information indicating of the part information model in order to verify the feasibility of the model has been created, it has laid a good foundation to build process knowledge database for the CAPP system.

Abstract: In recent years, the prevailing topic crawler algorithms are concentrated on the contents of topical words. These existing approaches neglect the sematic relationship among textual concepts, which lead to low correlation between crawled webpages. To address the issue, this paper presents a deep analysis of Shark Search algorithm, and makes an optimization in terms of incorporating the characteristics associated with semi-structured webpages. Furthermore, we enhance the performance of vector space model utilized in Shark Search algorithm by virtue of domain ontology, and propose a standardized method based on the vector space of ontology model to improve the evaluation metric of TF-IDF. The experimental results demonstrate the effectiveness of our algorithm that outperforms the state-of-the-art significantly in precision and recall.

Abstract: In order to solve the problems that design documents have large amounts of knowledge but distributed in disorder and that designers can’t express the true retrieval intention, which lead to a lot of useless retrieval results, this paper studied a retrieval algorithm of design knowledge based on domain ontology, and a design knowledge retrieval model based on domain ontology was established. On this basis, considering from word frequency, interval feature and semantic extension, the weight of which were taken into account for establishing retrieved concept set and indexing feature set of design documents; And according to vector space model, an improved similarity algorithm was used to match these set .Ultimately, the design knowledge matching with the retrieval intention was obtained, and the recall and precision of knowledge retrieval was improved greatly, thus achieving the purpose of knowledge reuse.

Abstract: One key step in text mining is the categorization of texts, i.e., to put texts of the same or similar contents into one group so as to distinguish texts of different contents. However, traditional word-frequency-based statistical approaches, such as VSM model, failed to reflect the complicated meaning in texts. This paper ushers in domain ontology and constructs new conceptual vector space model in the pre-processing stage of text clustering, substituting the initial matrix (lexicon-text matrix) in the latent semantic analysis with concept-text matrix. In the clustering analysis stage, this model adopts semantic similarity, partially overcoming the difficulty in accurately and effectively evaluating the degree of similarity of text due to simply taking into account the frequency of words and/or phrases in the text. Experimental results indicate that this method is helpful in improving the result of text clustering.

Abstract: Nowadays, automatic scoring is an important way of teaching and examinations. However, there is no existing research on automatic scoring for subjective item of database domain both at home and abroad. According to the characteristics of database domain, we construct database domain synonyms ontology and proposed a text similarity calculation algorithm based on Hamming distance. Then we implement the automatic scoring for subjective item of database domain on the basis of ontology. In addition, in order to verify the accuracy and rationality of the algorithm, we take a specific subject as an example. The experiment results further illustrate the accuracy and efficiency of the proposed automatic scoring algorithm.

Abstract: The express delivery industry of China is relatively backward in the automation degree of critical business processes. The basic reason is that the business-related supporting data, which is scattered in the multidimensional space, is difficult to utilize and process. This paper proposes an automatic data acquisition framework to resolve such difficulty, which synthetically utilize intelligent inernet of things (IoT), semantic web and complext event processing (CEP) technology. We also implement a SCEP prototype system with the capability of real-time detecting complex business events on the goods sorting line, which adopts a detection method consisting of four stages. The simulation results show that the system has good performance and feasible enough to deal with the complex business which need data support from multidimensional space.

Abstract: In today's network environment, the semantic gap between machine language and human language is the most important challenge of information management. Processing of text plays an important role in information management and knowledge management. In this paper, a proposed method shows how a text is related to its background knowledge. By background knowledge, People mean the parts of domain ontology which are not expressed in the text, but are shared by the creator and potential readers. Given the text-ontology mapping, people may discover the semantic domain of a text and how the text covers the domain knowledge. The semantic relatedness between the concepts mentioned in a text, as a whole unit,and the other concepts of the domain should be measured. This measure is based on the semantic relations defined by the ontology among its concepts. The experimental results prove that proposed method presents better overall performance and is natural way to improve retrieval results of users needed.

Abstract: Considering weakness of the traditional retrieval method based on keyword matching, the paper introduced semantic into information retrieval, and proposed a semantic retrieval model based on ontology. The paper offered a construction method of domain ontology and implemented semantic reasoning using Jena and improved a semantic similarity calculation method.