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The ubiquitous presence of surveillance systems generates massive amounts of video data. Storage and analysis of this data in real-time is a substantial challenge. There is huge potential in representing data in machine-readable and machine-interpretable format due to the presence of hidden semantics in images and videos. However, such representation requires ontology, which calls for expert domain knowledge. In this paper, a novel NLP-guided approach to generate an ontology for multimedia representation and information retrieval is proposed. A semi-automatic NLP-guided framework, which extracts all possible relations among objects is presented. This framework leverages the textual data of the domain to generate possible descriptions and actions within the domain. Relations among objects get embedded as object properties, whereas the category of an object as a class. Features and attributes of objects encode the data properties of the ontology. The proposed ontology is compared with existing multimedia ontologies and evaluated with regard to its capability to represent relations occurring in benchmark datasets, demonstrating the completeness and thorough coverage of the domain concepts. Spatial reasoning rules are established using Semantic Web Rule Language (SWRL) rules, and information retrieval is demonstrated using Description Logic (DL) and SPARQL queries. The proposed NLP-guided ontology generation approach is general enough to help in the development of ontologies for other domains as well, by providing video and textual data of the domain of interest, with limited human involvement. • Object properties extraction with textbase and NLP-guided approach. • Frequency-based semantic refinement to enhance domain-specific relations. • A novel data-driven video surveillance ontology "VizITS" for the ITS domain. • Semantic information retrieval as events, metadata, and question-answering. [ABSTRACT FROM AUTHOR]

The ubiquitous presence of surveillance systems generates massive amounts of video data. Storage and analysis of this data in real-time is a substantial challenge. There is huge potential in representing data in machine-readable and machine-interpretable format due to the presence of hidden semantics in images and videos. However, such representation requires ontology, which calls for expert domain knowledge. In this paper, a novel NLP-guided approach to generate an ontology for multimedia representation and information retrieval is proposed. A semi-automatic NLP-guided framework, which extracts all possible relations among objects is presented. This framework leverages the textual data of the domain to generate possible descriptions and actions within the domain. Relations among objects get embedded as object properties, whereas the category of an object as a class. Features and attributes of objects encode the data properties of the ontology. The proposed ontology is compared with existing multimedia ontologies and evaluated with regard to its capability to represent relations occurring in benchmark datasets, demonstrating the completeness and thorough coverage of the domain concepts. Spatial reasoning rules are established using Semantic Web Rule Language (SWRL) rules, and information retrieval is demonstrated using Description Logic (DL) and SPARQL queries. The proposed NLP-guided ontology generation approach is general enough to help in the development of ontologies for other domains as well, by providing video and textual data of the domain of interest, with limited human involvement. • Object properties extraction with textbase and NLP-guided approach. • Frequency-based semantic refinement to enhance domain-specific relations. • A novel data-driven video surveillance ontology "VizITS" for the ITS domain. • Semantic information retrieval as events, metadata, and question-answering. [ABSTRACT FROM AUTHOR]

In visual domains, such as Medicine, Meteorology and Geology, the tasks are accomplished through intensive use of visual knowledge. In this work, we focus in an essential kind of task that is performed in many visual domains: the visual interpretation task. We call visual interpretation the expert reasoning process that starts with the perception of visual features of domain objects and results in the understanding of the scene. We propose a top-down approach for solving visual interpretation tasks, which is based on symbolic pattern matching supported by well-founded ontologies. Our approach combines well-founded domain ontologies; a meta-model for representing inferential knowledge, which is based on the notion of perceptual chunks; and a reasoning model for visual interpretation. The proposed model was applied for developing an expert system for automating the task of visual interpretation of depositional processes, which a crucial task for petroleum exploration. [ABSTRACT FROM AUTHOR]

• This study develops a formal ontology of over 400 consumer financial goals. • The ontology is designed for wide range of financially situated users. • This research extends the Consumer Financial Protection Bureau's Financial Capability Model. • The study provides specific financial goals for consumers to improve their financial capability. • It focuses discussion on the objectives required to achieve financial capability. In this research, we communicate the design and evaluation of a consumer financial goals ontology to be utilized as a knowledgebase within recommender systems applications designed to provide decision support in the domain of financial planning. The goal of this research is to provide a domain conceptualization and knowledge classification of a comprehensive set of financial capability enhancing objectives contextually appropriate for a wide range of socio-economically situated consumers. Currently, to the best of our knowledge, within the domain of consumer financial planning no formal conceptual model or knowledge classification of financial goals exists which might be utilized as a knowledgebase for applications such as a FinTech recommender system. Achieving financial goals is a key behavior associated with consumer financial capability, a topic of national economic importance. A holistic representation of domain concepts is critical for advancement of solutions to problems pertinent to a domain. One primary reason for the dearth of applications designed to assist consumers with financial goal setting is the absence of a common domain ontology of financial goals. This study addresses a gap in the literature by contributing to the research knowledgebase an ontology for a domain of consumer financial goals. In doing so, it advances scholarly research through novel domain knowledge classification while providing researchers and practitioners with an ontological knowledgebase for indexing and retrieval within applications designed to improve consumer financial capability through identification and recommendation of specific, context-aware financial goals. The ontology could be used, for example, as a knowledgebase for a Personal Financial Recommender System (PFRS), or other financial technology (FinTech) application, designed to assist users with identification, setting, and tracking of financial goals. [ABSTRACT FROM AUTHOR]