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International audience; Ontology search and ranking are key building blocks to establishand reuse shared conceptualizations of domain knowledge on the Web. However, the effectiveness of proposed ontology ranking models is difficult to compare since these are often evaluated on diverse datasets that are limited by their static nature and scale. In this paper, we first introduce the LOVBench dataset as a benchmark for ontology term ranking. With inferred relevance judgments for morethan 7000 queries, LOVBench is large enough to perform a comparison study using learning to rank (LTR) with complex ontologyranking models. Instead of relying on relevance judgments from a few experts, we consider implicit feedback from many actual users collected from the Linked Open Vocabularies (LOV) platform. Our approach further enables continuous updates of the benchmark, capturing the evolution of ontologies’ relevance in an ever-changing data community. Second, we compare the performance of several feature configurations from the literature using LOVBench in LTR settings and discuss the results in the context of the observed real-world user behavior. Our experimental results show that feature configurations which are (i) well-suited to the user behavior, (ii) cover all features types, and (iii) consider decomposition of features can significantly improve the ranking performance.

Major retrieval issues on conventional online database search systems faced by novice researchers are identified and described. An ontology-based search framework is proposed. The framework consists of five elements: (1) user, (2) query, (3) query interface, (4) search process, and (5) result. The query interface is designed based on the ontology hierarchical structure in the ontology datastore and is represented as keywords/sub-keywords structures in the query interface. Ontology enables relationships between keywords and terms to be defined. Ontology allows desired information to be retrieved by sharing common vocabularies with an understanding of meaning of terms in the domain. A prototype system is developed based on the design science methodology selected to guide the system development. The system will process the search query once the query is sent. The search process will look for the matched documents and return results to the user. The results that are returned to the users will be reviewed and users have the option to repeat the process if the results do not fulfil the query made. The system development takes into account problems identification in the database search environment and motivation in improving and extending current limitations.

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.

In this world of fast computing, automation plays an important role. In image retrieval technique automation is a great quest. Giving an image as a query and retrieving relevant images is a challenging research area. In this paper we are proposing a research of using Multi-Modality Ontology integration for image retrieval concept. The core strategy in multimodal information retrieval is the combination or fusion of different data modalities to expand and complement information. Here we use both visual and textual ontology contents to provide search functionalities. Both images and texts are complimentary information units as the human perspective will be different. So, the computational linguistic of images will lead to disambiguate text meaning when it is not quite clear in right sense of several words. That’s why the Multi-Modal information retrieval may lead to an improved operation of information retrieval system. If we go for automation we are in need of a fuzzy technique to predicate the result. So in this paper we using Support Vector Machine classifier to classify the image automatically by using the general feature such as color, texture and texton of an image , then by using this result we can create both feature and domain ontology for an particular image. Using this Multi-Modality Ontology we can refine our image searching system.

This paper introduces efforts and achievements of Agriculture Ontology Service Research Group of Agricultural Information Institute of Chinese Academy of Agriculture Sciences in last 10 years. It summarizes the research on ontology construction methodology, ontology management system, ontology application and etc.

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.

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 two-staged bi-directional graph walk ranking algorithm for concepts in ontologies. We apply this algorithm on the task of searching and ranking concepts in ontologies and compare it with state-of-the-art ontology ranking models and traditional information retrieval algorithms such as PageRank and tf-idf. Our 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.

The National Institute of Diabetes and Digestive Disease (NIDDK) Central Data Repository (CDR) is a web-enabled resource available to researchers and the general public. The CDR warehouses clinical data and study documentation from NIDDK funded research, including such landmark studies as The Diabetes Control and Complications Trial (DCCT, 1983-93) and the Epidemiology of Diabetes Interventions and Complications (EDIC, 1994-present) follow-up study which has been ongoing for more than 20 years. The CDR also houses data from over 7 million biospecimens representing 2 million subjects. To help users explore the vast amount of data stored in the NIDDK CDR, we developed a suite of search mechanisms called the public query tools (PQTs). Five individual tools are available to search data from multiple perspectives: study search, basic search, ontology search, variable summary and sample by condition. PQT enables users to search for information across studies. Users can search for data such as number of subjects, types of biospecimens and disease outcome variables without prior knowledge of the individual studies. This suite of tools will increase the use and maximize the value of the NIDDK data and biospecimen repositories as important resources for the research community. Database URL: https://www.niddkrepository.org/niddk/home.do.

Proceedings of the Eight International Semantic Web Conference, The Semantic Web was designed to unambiguously define and use ontologies to encode data and knowledge on the Web. Many people find it difficult, however, to write complex RDF statements and queries because it requires familiarity with the appropriate ontologies and the terms they define. We describe a framework that eases the experiences in authoring and querying RDF data, in which we focus on automatically finding a set of appropriate Semantic Web ontology terms from a set of words used as the labels of nodes and edges in an incoming semantic graph.

The talk will address several issues in the application and development of ontologies: the selection of appropriate ontologies for a task; the population of a selected ontology through information extraction from text; the semi-automatic development or extension of an ontology; the lexicalisation of ontologies for the purpose of ontology-based information extraction from text. Each of these issues will be addressed through a particular application: the OntoSelect ontology library and search engine (http://olp.dfki.de/ontoselect/); the OntoLT Protege PlugIn for ontology learning from text (http://olp.dfki.de/OntoLT/OntoLT.htm); the SOBA system for ontology-based information extraction from text; the LingInfo lexicon model for the integration of lexical/linguistic information in ontologies (http://olp.dfki.de/LingInfo/).

The most important property of the Gene Ontology is the terms. These control vocabularies are defined to provide consistent descriptions of gene products that are shareable and computationally accessible by humans, software agent, or other machine-readable meta-data. Each term is associated with information such as definition, synonyms, database references, amino acid sequences, and relationships to other terms. This information has made the Gene Ontology broadly applied in microarray and proteomic analysis. However, the process of searching the terms is still carried out using traditional approach which is based on keyword matching. The weaknesses of this approach are: ignoring semantic relationships between terms, and highly depending on a specialist to find similar terms. Therefore, this study combines semantic similarity measure and genetic algorithm to perform a better retrieval process for searching semantically similar terms. 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In this paper we describe a novel methodology for retrieving and combining information from multiple ontologies for the medical domain. In the last decades the number and diversity of available ontologies for the medical domain has grown considerably. The variety and number of such resources available makes the cost to integrate them into an application incremental, often prohibitive for exploratory prototyping, and discouraging for larger-scale integration. Cross-ontology localized merging is proposed as a way to allow for a flexible and scalable solution. This approach also indicates a low maintenance cost and high reusability for different application types within the medical domain