Ontology Expansion Research Articles

Ontology expansion based on UWN reusability

Ontology expansion based on UWN reusability

Ontology expansion based on UWN reusability

In the present era of big data the demand for developing efficient knowledge representation techniques for different applications is expanding steadily. Knowledge representation is largely based on ontology in the Semantic Web. However, one of the major difficulties to construct a new ontology require users the high cost and long time. Collecting complete knowledge about the domain of opening is time consuming and cannot guarantee expected results too. Therefore, reuse of existing resources can offer a cost and time benefit than building a new one from scratch. Hence, an approach is proposed to reuse the existing ontologies available on the web or online libraries then enrich it. This paper proposed Uyghur domain ontology constructing method based on UWN reusability, which can extract domain ontology factors from UWN and get the initial ontology in which the concepts and relations are already relative standard, then expand it automatically via Word2Vec models, at last construct the final higher quality version after revising and improving. The method is simple, but effective on ontology enrichment. After evaluating the accuracy, the experimental results show that our method is feasible and effective.

An ontology constructing technology oriented on massive social security policy documents

To solve the problem brought from enormous policy documents and complex management in the social security domain, the article uses ontology as the way of representing and storing knowledge. The article first constructs the framework of ontology through manual work so that it can ensure the relative accuracy of the ontology structure. Then it achieves the automatic ontology expansion based on the inclusion relationship of property sets or operational object sets. The article uses a semi-automatic method that extracts hierarchical concepts and non-hierarchical concepts from domain thesaurus by using the method combining statistics with rules to construct the ontology. Besides constructing the ontology, the article proposes the concepts of concept phrase vector model and high frequency characteristics phrase vector model. The experiment result indicates that ontology semi-automatic construction process can help experts to construct the social security ontology effectively oriented on massive policy documents and is a considerable reference for the construction of ontology in other domains.

An Ontology-Based Model for Treatment Guidelines of Internet and Games Addiction

Internet and games addiction will become a difficult problem for the parents, because the internet and games easier to access and has more contents. Thus, the number of internet and games addiction will be increasing in the future. This study recommends ontology expansion for treatment guidelines of internet and games addiction that will use as the component of recommendation system in web technology. This study’s methodology can be condensed into three states; data collection ontology development, and evaluation. This ontology included seven main classes, there are profile, characteristics, risk factors, devices, treatment, and GAST. The evaluation result that conducted by domain experts included a highly-superior concentration of 88.34%, which confirms that this ontology may be employed for developing a recommendation system.

Predicate enrichment of aligned XPaths for wrapper induction

Extracting data from various semi-structured sources is a topic that has received a lot of attention. Wrapper induction specifically has been studied extensively, where users annotate a couple of data sources with examples of the data they want, after which a procedure (wrapper) is constructed that can optimally extract similar data as well. In this paper a novel wrapper induction approach is proposed, exploiting the premise of the general applicability of the XPath query language, studied specifically within the context of web pages. After a user annotates a limited set of web pages with the required data, a generalised XPath is constructed that is capable of extracting the examples and, optimally, similar data as well. This generalised baseline XPath is then enriched with predicates, based on context and structure of the data sources, to optimise the precision/recall balance of the data extraction capability of the wrapper. Six variations of such limiting predicates are introduced and investigated. In this paper, it is shown that the baseline approach often generalises the samples too much, leading to a decreased precision. Enriching the baseline wrapper by the addition of predicates limits the generalisation power of the queries in an intelligent manner. Experimental results show that there is a significant improvement in the overall precision of the generalised query, without an excessive loss in recall. Documented tests and real world experience with a large amount of data show that the technique is flexible, easily understood and applicable in a broad range of applications. It is not only of interest in the fields of web information retrieval, but can also be used in the contexts of, e.g., reverse engineering of databases, ontology expansion and deep web data mining, as both simple lists of data and complex structures can be extracted.

Ontorat: automatic generation of new ontology terms, annotations, and axioms based on ontology design patterns.

BackgroundIt is time-consuming to build an ontology with many terms and axioms. Thus it is desired to automate the process of ontology development. Ontology Design Patterns (ODPs) provide a reusable solution to solve a recurrent modeling problem in the context of ontology engineering. Because ontology terms often follow specific ODPs, the Ontology for Biomedical Investigations (OBI) developers proposed a Quick Term Templates (QTTs) process targeted at generating new ontology classes following the same pattern, using term templates in a spreadsheet format.ResultsInspired by the ODPs and QTTs, the Ontorat web application is developed to automatically generate new ontology terms, annotations of terms, and logical axioms based on a specific ODP(s). The inputs of an Ontorat execution include axiom expression settings, an input data file, ID generation settings, and a target ontology (optional). The axiom expression settings can be saved as a predesigned Ontorat setting format text file for reuse. The input data file is generated based on a template file created by a specific ODP (text or Excel format). Ontorat is an efficient tool for ontology expansion. Different use cases are described. For example, Ontorat was applied to automatically generate over 1,000 Japan RIKEN cell line cell terms with both logical axioms and rich annotation axioms in the Cell Line Ontology (CLO). Approximately 800 licensed animal vaccines were represented and annotated in the Vaccine Ontology (VO) by Ontorat. The OBI team used Ontorat to add assay and device terms required by ENCODE project. Ontorat was also used to add missing annotations to all existing Biobank specific terms in the Biobank Ontology. A collection of ODPs and templates with examples are provided on the Ontorat website and can be reused to facilitate ontology development.ConclusionsWith ever increasing ontology development and applications, Ontorat provides a timely platform for generating and annotating a large number of ontology terms by following design patterns.Availability: http://ontorat.hegroup.org/

Ontology expansion: appending with extracted sub-ontology

Ontology expansion: appending with extracted sub-ontology

Retrieval strategy based on semantic expansion of keywords

Traditional retrieval strategy based on-matching is facing unavoidable bottleneck because of mechanical characters matching and polysemy problems.A retrieval strategy based on domain ontology and semantic expansion of keywords was proposed.On the one hand,the domain ontology concept structure was used to expand the retrieval area;on the other hand,a method was presented to filter the irrelevant results.In this way,both the recall ratio and the precision ratio could be improved simultaneously.After that,an algorithm of sorting the retrieval results by the similarity between ontology and result was given.