Ontology Search Engine Research Articles

A personalised ontology ranking model based on analytic hierarchy process

Ontology ranking is one of the important functions of ontology search engines, which ranks searched ontologies based on the ranking model applied. A good ranking method can help users acquire the exactly required ontology from a considerable amount of search results, efficiently. Existing approaches to rank ontologies take only a single aspect into consideration and ignore users' personalised demands, hence produce unsatisfactory result. It is believed that, the factors that influence ontology importance and the users' demands both need to be considered comprehensively in ontology ranking. A personalised ontology ranking model based on the hierarchical analysis approach is proposed in this paper. We build a hierarchically analytical model and apply analytic hierarchy process to quantify ranking indexes and assign weights to them. The experimental results show that the proposed method can rank ontologies effectively and meet users' personalised demands.

A personalised ontology ranking model based on analytic hierarchy process

Ontology ranking is one of the important functions of ontology search engines, which ranks searched ontologies based on the ranking model applied. A good ranking method can help users acquire the exactly required ontology from a considerable amount of search results, efficiently. Existing approaches to rank ontologies take only a single aspect into consideration and ignore users' personalised demands, hence produce unsatisfactory result. It is believed that, the factors that influence ontology importance and the users' demands both need to be considered comprehensively in ontology ranking. A personalised ontology ranking model based on the hierarchical analysis approach is proposed in this paper. We build a hierarchically analytical model and apply analytic hierarchy process to quantify ranking indexes and assign weights to them. The experimental results show that the proposed method can rank ontologies effectively and meet users' personalised demands.

Review On Toward SWSs Discovery: Mapping from WSDL to OWL-S Based on Ontology Search and Standardization Engine

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

A new ontology ranking method with OntoDUIA for ontology retrieval system

Ontology is being widely used for data integration and knowledge discovery in the field of data engineering. Ontology ranking is one of the important functions of ontology search engine, which ranks the searched ontologies in a reasonable order. A good ranking method can help a user acquiring the satisfied ontology efficiently from a considerable amount of search results. However, existing methods in literatures are unable to rank ontologies to well meet user's ranking demand due to their inherent shortcomings. In this paper, a novel ontology ranking method OntoDUIA is proposed and presented. It evaluates user-query related ontologies and ranks them based on a set of metrics of query relevance, usability, instance distribution and authority of ontology. To evaluate OntoDUIA, a series of experiments are conducted to compare the performance among OntoDUIA and some existing ontology ranking methods as well as human experts. Experimental results show that OntoDUIA can effectively meet user's ontology ranking demand, and it achieves stable and reliable ranking results. Finally, OntoDUIA can also be applied to ontology retrieval system.

Fast Mapping Algorithm from WSDL to OWL-S

Recently semantic web services represent the most technology developed for machine to machine interaction. The problem of discovering and selecting the most suitable web service represents a challenge for semantic web services. In this paper performance evaluation of mapping algorithm from web services annotations (WSDL) to semantic annotations (OWL-S) based on ontology search engine is presented. During mapping process primitive type remains without change. The complex type are converted to OWL ontology by extracted them and passing to ontology search and standardization process without need of conversion into temporary ontology. The keywords extracted in the linguistic search phase and are extended using word net. The mapping algorithm and its modification are implemented in Java and evaluated by 310 files WSDL. The output results of two algorithms are identical. But the proposed modified algorithm is faster than mapping algorithm.

A Novel and Hybrid Ontology Ranking Framework using Semantic Closeness Measure

Semantic Web is a Web that adds more meaning to the Web documents in order to access knowledge instead of unstructured material and also allowing knowledge to be processed automatically. One of the methods to achieve this is of using Ontology. The Ontology defines the terms and the relations among the terms on a domain. There are number of Ontology repositories present. When this increases day by day, the need for getting relevant ontology for the search keyword also increases. Even though there are number of semantic web search engines, Swoogle is placed first, which ranks the ontologies using an adaptation of Google’s Page Rank scoring method. A major drawback with this system is that many ontologies are poorly inter-referenced, which does not reflect the quality of the ontologies. This paper reviews the methodologies used in Swoogle for computing rank score and proposes Semantic Closeness Measure (SCM) which has not been employed in any other ontology ranking algorithms. This work develops a hybrid ranking system to rank the ontologies better than Swoogle and other ontology search engines. The results confirm that the proposed system places the highly relevant and quality ontologies on the top list by reranking the Swoogle’s results. This ranking framework enables the searcher to get relevant results quickly and reduces time in searching the long list of results.

Ranking Ontologies Based on Formal Concept Analysis

Ontology ranking is one of the important functions of ontology search engine and plays an important role for ontology reuse. It facilitates effectively user to choose the reusable ontologies from the search results returned by ontology search engine. The current ontology ranking methods can not satisfy user because of various defects. On the basis of analyzing the existing ontology ranking methods, we think both the correlation among the ontologies and the matches between the query and ontologies should fully be considered on ranking ontologies. An ontology ranking method is proposed based on Formal Concept Analysis in this paper. The experimental results show that the proposed method is better than that of hyperlink analysis and content analysis. Ontology search engines will get benefit from our method because if can also help user to choose efficiently suitable ontologies to reuse.

Toward SWSs Discovery: Mapping from WSDL to OWL-S Based on Ontology Search and Standardization Engine

Semantic Web Services (SWSs) represent the most recent and revolutionary technology developed for machine-to-machine interaction on the web 3.0. As for the conventional web services, the problem of discovering and selecting the most suitable web service represents a challenge for SWSs to be widely used. In this paper, we propose a mapping algorithm that facilitates the redefinition of the conventional web services annotations (i.e., WSDL) using semantic annotations (i.e., OWL-S). This algorithm will be a part of a new discovery mechanism that relies on the semantic annotations of the web services to perform its task. The “local ontology repository” and “ontology search and standardization engine” are the backbone of this algorithm. Both of them target to define any data type in the system using a standard ontology-based concept. The originality of the proposed mapping algorithm is its applicability and consideration of the standardization problem. The proposed algorithm is implemented and its components are validated using some test collections and real examples. An experimental test of the proposed techniques is reported, showing the impact of the proposed algorithm in decreasing the time and the effort of the mapping process. Moreover, the experimental results promises that the proposed algorithm will have a positive impact on the discovery process as a whole.

SOSEM: A Self-recommending Ontology Search Extension Mechanism Based on User Behavior

Ontology search mechanism plays an important role in the discovery and usage of ontology. The current ontology search engines all adopt keyword-based search mechanism. It is difficult to clearly represent users’ search requirements because the number of the keywords input by a user is usually few. Extending the input keyword(s) by using WordNet will increase the recall rates and decrease the accuracy rates, because it enlarges the search scope imprecisely. To improve the performance of ontology searching, a self-recommending ontology search extension mechanism is proposed based on the study of the ontology search requirements and user search behaviors in this paper. The experimental results show that the proposed approach can effectively improve the matched coverage between query and target ontologies. Our method can also help user to conveniently search desired ontologies with high precision rates. Ontology search engines will get benefit from our method, because it will obviously improve the customer’s experience during ontology searching.

Incorporating compactness to generate term-association view snippets for ontology search

A query-relevant snippet for ontology search is useful for deciding if an ontology fits users’ needs. In this paper, we illustrate a good snippet in a keyword-based ontology search engine should be with term-association view and compact, and propose an approach to generate it. To obtain term-association view snippets, a model of term association graph for ontology is proposed, and a concept of maximal r-radius subgraph is introduced to decompose the term association graph into connected subgraphs, which preserve close relations between terms. To achieve compactness, in a query-relevant maximal r-radius subgraph, a connected subgraph thereof with a small graph weight is extracted as a sub-snippet. Finally, a greedy method is used to select sub-snippets to form a snippet in consideration of query relevance and compactness without violating the length constraint. An empirical study on our implementation shows that our approach is feasible. An evaluation on effectiveness shows that the term-association view snippet is favored by users, and the compactness helps reading and judgment.

Agricultural Market Name Geo-Locating System Based on an Administrative Ontology and Web Search Engine

The problem of associating the agricultural market names on web sites with their locations is essential for geographical analysis of the agricultural products. In this paper, an algorithm which employs the administrative ontology and the statistics from the search results were proposed. The experiments with 100 market names collected from web sites were conducted. The experimental results demonstrate that the algorithm proposed obtains satisfactory performance in resolving the problem above, thus the effectiveness of the method is verified.

Falcons Concept Search: A Practical Search Engine for Web Ontologies

Web ontologies provide shared concepts for describing domain entities and thus enable semantic interoperability between applications. To facilitate concept sharing and ontology reusing, we developed Falcons Concept Search, a novel keyword-based ontology search engine. In this paper, we illustrate how the proposed mode of interaction helps users quickly find ontologies that satisfy their needs and present several supportive techniques including a new method of constructing virtual documents of concepts for keyword search, a popularity-based scheme to rank concepts and ontologies, and a way to generate query-relevant structured snippets. We also report the results of a usability evaluation as well as user feedback.