Ontology Ranking Research Articles

An Enhanced ELECTRE II Method for Multi-Attribute Ontology Ranking with Z-Numbers and Probabilistic Linguistic Term Set

The high number of ontologies available on the web to date makes it increasingly difficult to select appropriate ontologies for reuse. Many studies have attempted to provide support for ontology selection and ranking; however, the existing studies provide support for ontology ranking from an objective perspective as opposed to a subjective perspective. They do not take into account the qualitative aspects of ontologies. Furthermore, the existing methods have a limited focus on group environments. In this paper, a multi-criteria decision-making approach is presented for ontology ranking with the development of an enhanced model combining the ELECTRE II model with the Z-Probabilistic Linguistic Term Set (ZPLTS). The ZPLTS-ELECTRE II model enables decision-makers to model ontology ranking problems using both numerical and linguistic data. Furthermore, the newly proposed model provides support for ontology ranking in group settings, with an emphasis on modeling the differing levels of credibility of decision-makers using the ZPLTS, which allows decision-makers to not only specify their opinion but also specify their level of credibility. The model was applied to rank a set of mental health ontologies obtained from the BioPortal repository. The results showed that the method was able to rank the ontologies successfully. The results were further compared with the traditional ELECTRE II and the PLTS ELECTRE II methods, displaying superior modeling capabilities. This paper demonstrated the effectiveness of the newly proposed ZPLTS-ELECTRE II model for ontology ranking in a real-world context, but the method is not constrained to the ontology ranking domain; rather, it may be applied to other real-world decision problems as well.

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.

Study on Ontology Ranking Models Based on the Ensemble Learning

This article describes how more knowledge appears on the Internet than in an ontological form. Displaying results to users precisely when searching is the key issue of the research on ontology retrieval. The considered factors of ontology ranking are not only limited to internal character-matching, but analysis of metadata, including the entities, structures and the relations in ontologies. Currently, existing single feature ranking algorithms focus on the structures, elements and the contents of a certain aspect in ontology, thus, the results are not satisfactory. Combining multiple single-featured models seems to achieve better results, but the objectivity and versatility of models' weights are debatable. Machine learning effectively solves the problem and putting advantages of ranking learning algorithms together is the pressing issue. So we propose ensemble learning strategies to combine different algorithms in ontology ranking. And the ranking result is more satisfied compared to Swoogle and base algorithms.

Assessing the quality of domain ontologies: Metrics and an automated ranking system

The ability of a user to select an appropriate, high-quality domain ontology from a set of available options would be most useful in knowledge engineering and other intelligent applications. This capability, however, requires good quality assessment metrics as well as automated support when there is a large number of ontologies from which to make a selection. This research analyzes existing metrics for domain ontology evaluation and extends them to derive a Layered Ontology Metrics Suite based on semiotic theory. The metrics are implemented in a Domain Ontology Ranking System (DoORS) prototype, the purpose of which is to search an ontology library for specific terms to retrieve candidate domain ontologies and then assess their quality and suitability based upon the suite of metrics. The prototype system is compared to existing approaches to automated ontology quality ranking to illustrate the usefulness of the 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.

A new ontology ranking method with OntoDUIA for ontology retrieval system

A new ontology ranking method with OntoDUIA for ontology retrieval system

DWRank: Learning concept ranking for ontology search

With the recent growth of Linked Data on the Web there is an increased need for knowledge engineers to find ontologies to describe their data. Only limited work exists that addresses the problem of searching and ranking ontologies based on a given query term. In this paper we introduce DWRank, a tw o-staged bi-directional graph walk ranking algorithm for concepts in ontologies. DWRank characterises two features of a concept in an ontology to determine its rank in a corpus, the centrality of the concept to the ontology within which it is defined (HubScore) and the authoritativeness of the ontology in which it is defined (AuthorityScore). DWRank then uses a Learning to Rank approach to learn the feature weights for the two aforementioned ranking strategies. We compare DWRank with state-of-the-art ontology ranking models and traditional information retrieval algorithms. This evaluation shows that DWRank significantly outperforms the best ranking models on a benchmark ontology collection for the majority of the sample queries defined in the benchmark. In addition, we compare the effectiveness of the HubScore part of our algorithm with the state-of-the-art ranking model to determine a concept centrality and show the improved performance of DWRank in this aspect. Finally, we evaluate the effectiveness of the design decisions made for the AuthorityScore method in DWRank to find missing inter-ontology links and present a graph-based analysis of the ontology corpus that shows the increased connectivity of the ontology corpus after extraction of the implicit inter-ontology links.

Information content-based ranking of ontologies

Ontologies form the backbone of the Semantic Web. Owing to multidisciplinary applications, a large number of ontologies are cropping up in each domain. Choosing the best ontology according to the user's requirements becomes a challenging task. Most of the existing approaches on ranking ontologies are either based on the internal structure of the ontologies or by analysing the Semantic Web link structures. However, these approaches have not exploited the inherent features of OWL which is used extensively in constructing ontologies. In this paper, a novel ontology ranking approach is proposed by considering two factors: 1 the OWL characterising features and 2 the nearness of the ontology to a certain concept. We define information content weight and develop a methodology for ranking ontologies. We demonstrate the viability of the proposed methodology through experimental results on the 'drug' concept.

Epigenetic changes in obesity (383.6)

The aim of this study was to find epigenetic differences between obese sensitive and obese resistant phenotypes in an obesity sensitive mouse model. 88 C57BL/6J females were fed either a high‐fat diet (HF, 60% kilocalories from fat) or a control diet (CT, 10% kilocalories from fat) for 10 weeks. 15 CT mice, 15 mice from the low (obesity resistant ‐ HFR), and 15 high (obesity sensitive ‐ HFS) weight extremes, respectively, were selected based on the heterogeneous weight gain of the group exposed to HF. Promoter DNA methylation (liver) was assessed between the 3 groups using methylated CpG islands amplification combined with microarrays (MCAM). Following statistical analysis for microarrays and gene ontology (GO) ranking we found that the HFR group is epigenetically similar to the CT, with 8 hyper‐methylated and 73 hypo‐methylated genes. The HFS group had 7,721 differentially methylated genes compared to the CT, with the majority being hyper‐methylated. Also, we found higher methylation in the imprinted control region of the H19/Igf2 imprinted region, as determined by pyrosequencing, in the HFS group versus CT. In conclusion, the epigenomes of the obesity‐resistant and control mice are similar, while the epigenome of the obesity‐prone mice presents specific alterations. GO analysis revealed that such epigenetic alterations are grouped within functional classes related to key pathways involved in liver metabolism.Grant Funding Source: Nutrition Research Institute Research Funds

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.

Comparative Analysis of Ontology Ranking Algorithms

Ontologies are the backbone of knowledge representation on Semantic web. Challenges involved in building ontologies are in terms of time, efforts, skill, and domain specific knowledge. In order to minimize these challenges, one of the major advantages of ontologies is its potential of “reuse,” currently supported by various search engines like Swoogle, Ontokhoj. As the number of ontologies that such search engines like Swoogle, OntoKhoj Falcon can find increases, so will the need increase for a proper ranking method to order the returned lists of ontologies in terms of their relevancy to the query which can save a lot of time and effort. This paper deals with analysis of various ontology ranking algorithms. Based on the analysis of different ontology ranking algorithms, a comparative study is done to find out their relative strengths and limitations based on various parameters which provide a significant research direction in ranking of ontologies in semantic web.

Applying multi-criteria approaches to ontology ranking: a comparison with AKTiveRank

Recent years have seen a progressive spread of ontologies. This indicates the need for instruments for their evaluation and ranking. However, most of the developed algorithms are based on a single metric, thus focusing on a unique feature and neglecting other potentially meaningful aspects. Others perform a weighted aggregation of different metrics, thus resulting in poor tunability and sensitivity to measurement errors. In this context, we propose the adoption of general purpose and flexible multi-criteria decision analysis techniques which represent a total novelty for ontologies. Two methods belonging to the Electre family are presented: Electre I and Electre III. Both techniques are compared to the well-known AKTiveRank procedure, demonstrating their suitability for ontology ranking. The former best identifies the most promising candidates amongst a set of alternatives. The latter provides a set of tuning parameters for making thin ranking procedures.

Semantic metrics

In the context of the Semantic Web, many ontology-related operations, e.g., ontology ranking, segmentation, alignment, articulation, reuse, evaluation, etc., can be boiled down to one fundamental operation: computing the similarity and/or dissimilarity among ontological entities, and in some cases among ontologies. In this paper, we review conventional metrics for computing distance and we propose a series of metrics conceived purposely for semantics rich entities. We give a formal account of semantic metrics drawn from a variety of research disciplines and enrich them with formalisation based on Description Logics. We argue that concept-based metrics can be aggregated to produce numeric distances at ontology-level. We speculate on the usability of our ideas in potential ontology engineering tasks.