CROSS DOMAIN KNOWLEDGE VERIFICATION - Verifying Knowledge In Foundation Ontology Based Domain Ontologies

Author(s):  
Victor Saquicela ◽  
Luis M. Vilches-Blázquez ◽  
Oscar Corcho

In this paper the authors present an approach for the semantic annotation of RESTful services in the geospatial domain. Their approach automates some stages of the annotation process, by using a combination of resources and services: a cross-domain knowledge base like DBpedia, two domain ontologies like GeoNames and the WGS84 vocabulary, and suggestion and synonym services. The authors’ approach has been successfully evaluated with a set of geospatial RESTful services obtained from ProgrammableWeb.com, where geospatial services account for a third of the total amount of services available in this registry.


2021 ◽  
Vol 13 (5) ◽  
pp. 124
Author(s):  
Jiseong Son ◽  
Chul-Su Lim ◽  
Hyoung-Seop Shim ◽  
Ji-Sun Kang

Despite the development of various technologies and systems using artificial intelligence (AI) to solve problems related to disasters, difficult challenges are still being encountered. Data are the foundation to solving diverse disaster problems using AI, big data analysis, and so on. Therefore, we must focus on these various data. Disaster data depend on the domain by disaster type and include heterogeneous data and lack interoperability. In particular, in the case of open data related to disasters, there are several issues, where the source and format of data are different because various data are collected by different organizations. Moreover, the vocabularies used for each domain are inconsistent. This study proposes a knowledge graph to resolve the heterogeneity among various disaster data and provide interoperability among domains. Among disaster domains, we describe the knowledge graph for flooding disasters using Korean open datasets and cross-domain knowledge graphs. Furthermore, the proposed knowledge graph is used to assist, solve, and manage disaster problems.


Author(s):  
Déliar Rogozan ◽  
Gilbert Paquette

Evolution is a fundamental requirement for useful ontologies. Knowledge evolves continuously in all fields of knowledge due to the progress in research and applications. Because they are theories of knowledge in a precise domain, Ontologies need to evolve because the domain has changed, the viewpoint of the domain has changed or because problems in the original domain conceptualization have to be resolved or have been resolved (Noy & Klein, 2003). Moreover, in open and dynamic environments such as the Semantic Web, the ontologies need to evolve because domain knowledge evolves continually (Heflin & Hendler, 2000) or because ontology-oriented software-agents must respond to changes in users’ needs (Stojanovic, Maedche, Stojanovic, & Studer, 2003).


Author(s):  
Chrisa Tsinaraki

Several consumer electronic devices that allow capturing digital multimedia content (like mp3 recorders, digital cameras, DVD camcorders, smart phones etc.) are available today. These devices have allowed both the amateur and the professional users to produce large volumes of digital multimedia material, which, together with the traditional media objects digitized recently (using scanners, audio and video digitization devices) form a huge distributed multimedia information source. The multimedia material that is available today is usually organized in independent multimedia information sources, developed on top of different software platforms. The Internet, the emergence of advanced network infrastructures that allow for the fast, efficient and reliable transmission of multimedia content and the development of digital multimedia content services on top of them form an open multimedia consumption environment. In this environment, the users access the multimedia material either through computers or through cheap consumer electronic devices that allow the consumption and management of multimedia content. The users of such an open environment need to be able to access the services offered by the different vendors in a transparent way and to be able to compose the different atomic services (like, for example, multimedia content filtering) into new, composite ones. In order to fulfill this requirement, interoperability between the multimedia content services offered is necessary. Interoperability is achieved, at the syntactic level, through the adoption of standards. At the semantic level, interoperability is achieved through the integration of domain knowledge expressed in the form of domain ontologies. An ontology is a logical theory accounting for the intended meaning of a formal vocabulary, i.e. its ontological commitment to a particular conceptualization of the world (Guarino, 1998). The standard that dominates in multimedia content description is the MPEG-7 (Salembier, 2001), formally known as Multimedia Content Description Interface. It supports multimedia content description from several points of view, including media information, creation information, structure, usage information, textual annotations, media semantics, and low-level visual and audio features. Since the MPEG-7 allows the structured description of the multimedia content semantics, rich and accurate semantic descriptions can be created and powerful semantic retrieval and filtering services can be built on top of them. It has been shown, in our previous research (Tsinaraki, Fatourou and Christodoulakis, 2003), that domain ontologies capturing domain knowledge can be expressed using pure MPEG-7 constructs. This way, domain knowledge can be integrated in the MPEG-7 semantic descriptions. The domain knowledge is subsequently utilized for supporting semantic personalization, retrieval and filtering and has been shown to enhance the retrieval precision (Tsinaraki, Polydoros and Christodoulakis, 2007). Although multimedia content description is now standardized through the adoption of the MPEG-7 and semantic multimedia content annotation is possible, multimedia content retrieval and filtering (especially semantic multimedia content retrieval and filtering), which form the basis of the multimedia content services, are far from being successfully standardized.


2014 ◽  
Vol 66 (5) ◽  
pp. 553-580 ◽  
Author(s):  
Tung Thanh Nguyen ◽  
Tho Thanh Quan ◽  
Tuoi Thi Phan

Purpose – The purpose of this paper is to discuss sentiment search, which not only retrieves data related to submitted keywords but also identifies sentiment opinion implied in the retrieved data and the subject targeted by this opinion. Design/methodology/approach – The authors propose a retrieval framework known as Cross-Domain Sentiment Search (CSS), which combines the usage of domain ontologies with specific linguistic rules to handle sentiment terms in textual data. The CSS framework also supports incrementally enriching domain ontologies when applied in new domains. Findings – The authors found that domain ontologies are extremely helpful when CSS is applied in specific domains. In the meantime, the embedded linguistic rules make CSS achieve better performance as compared to data mining techniques. Research limitations/implications – The approach has been initially applied in a real social monitoring system of a professional IT company. Thus, it is proved to be able to handle real data acquired from social media channels such as electronic newspapers or social networks. Originality/value – The authors have placed aspect-based sentiment analysis in the context of semantic search and introduced the CSS framework for the whole sentiment search process. The formal definitions of Sentiment Ontology and aspect-based sentiment analysis are also presented. This distinguishes the work from other related works.


2017 ◽  
Vol 3 (3) ◽  
pp. 58 ◽  
Author(s):  
Bojan Cestnik ◽  
Elsa Fabbretti ◽  
Donatella Gubiani ◽  
Tanja Urbančič ◽  
Nada Lavrač

Literature-based discovery tools have been often used to overcome the problem of fragmentation of science and to assist researchers in their process of cross-domain knowledge discovery. In this paper we propose a methodology for cross-domain literature-based discovery that focuses on outlier documents to reduce the search space of potential cross-domain links and to improve search efficiency. In a previous study, literature mining tools OntoGen for document clustering and CrossBee for cross-domain bridging term exploration were combined to search for hidden relations in scientific papers from two different domains of interest, where the utility of the approach was demonstrated in a study involving PubMed papers about Alzheimer’s disease and gut microbiome. This paper extends the approach by proposing a methodology, implemented as a repeatable workflow in a web-based text mining platform TextFlows, which enables easy access and execution of the methodology for the interested researcher.


Author(s):  
Aatif Ahmad Khan ◽  
Sanjay Kumar Malik

Semantic Search refers to set of approaches dealing with usage of Semantic Web technologies for information retrieval in order to make the process machine understandable and fetch precise results. Knowledge Bases (KB) act as the backbone for semantic search approaches to provide machine interpretable information for query processing and retrieval of results. These KB include Resource Description Framework (RDF) datasets and populated ontologies. In this paper, an assessment of the largest cross-domain KB is presented that are exploited in large scale semantic search and are freely available on Linked Open Data Cloud. Analysis of these datasets is a prerequisite for modeling effective semantic search approaches because of their suitability for particular applications. Only the large scale, cross-domain datasets are considered, which are having sizes more than 10 million RDF triples. Survey of sizes of the datasets in triples count has been depicted along with triples data format(s) supported by them, which is quite significant to develop effective semantic search models.


Author(s):  
Chao Shang ◽  
Sarthak Dash ◽  
Md. Faisal Mahbub Chowdhury ◽  
Nandana Mihindukulasooriya ◽  
Alfio Gliozzo

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