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Main path analysis is a popular method for extracting the scientific backbone from the citation network of a research domain. Existing approaches ignored the semantic relationships between the citing and cited publications, resulting in several adverse issues, in terms of coherence of main paths and coverage of significant studies. This paper advocated the semantic main path network analysis approach to alleviate these issues based on citation function analysis. A wide variety of SciBERT‐based deep learning models were designed for identifying citation functions. Semantic citation networks were built by either including important citations, for example, extension, motivation, usage and similarity, or excluding incidental citations like background and future work. Semantic main path network was built by merging the top‐K main paths extracted from various time slices of semantic citation network. In addition, a three‐way framework was proposed for the quantitative evaluation of main path analysis results. Both qualitative and quantitative analysis on three research areas of computational linguistics demonstrated that, compared to semantics‐agnostic counterparts, different types of semantic main path networks provide complementary views of scientific knowledge flows. Combining them together, we obtained a more precise and comprehensive picture of domain evolution and uncover more coherent development pathways between scientific ideas. [ABSTRACT FROM AUTHOR]

This study aims to explore research collaborations and authorship patterns in the field of semantic digital libraries(SDL). The data is extracted (N=2075) from the Scopus database using keywords related to semantic digital libraries by considering all types of publications during 1983-2019. The analysis of each document is based on the following scientometrics indicators: author productivity, degree of collaboration, collaboration index, collaboration coefficient and modified collaboration coefficient. Correlation matrices were also calculated and inferences drawn in terms of authors and publications. A network visualisation tool VOSviewer was used to present authorship correlation network strength and keyword mapping for a better insight into the emerging areas in the field of SDL. The resulting average degree of collaboration of 0.898 indicates that a large number of publications are multi-authored and that there is a higher level of collaborative research in the field of semantic digital libraries. Meghini C from the Institute of Information Science and Technologies, Italy has produced the highest number of research paper(n=18) whereas Egenhofer MJ found to be a profoundly impacted author with 851 citations on in the studied domain. Results also reveal that the focus areas of research related to SDL include digital libraries, semantic web, ontology, metadata and information retrieval. However, keywords such as natural language processing system, computational linguistics, linked data are also repeated frequently in the published literature, thus revealing the emerging areas of the future research in the domain of SDL. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive survey on knowledge retrieval of historical concepts using semantic web technologies. The scope of this discussion encompasses research material in the field of computing and humanities including but not limited to Cultural Heritage, Museum and Digital Library research. Semantic web techniques used to describe, store, search, query and retrieve historic concepts such as RDF, RDFs, OWL, and Ontologies are discussed along with other extensively researched methods from domains like Natural Language Processing and Computer Vision. The discussion elaborates on Ontological-enabled information retrieval and the concept of new knowledge discovery. This work presents a comprehensive set of problems identified in the field of information retrieval and their solutions through the semantic web along with an insight in the future directions and the open-ended questions in the domain of retrieval of historic concept. [ABSTRACT FROM AUTHOR]

Abstract Today most of the users on the move require contextualized local and georeferenced information. Several solutions aim to meet these trends, thus assisting users and satisfying their needs and preferences, such as virtual assistants and Location-Aware Recommender Systems (LARS), both in commercial and research literature. However, general purpose virtual assistants usually have to manage large domains, dealing with big amounts of data and online resources, losingfocus on more specific requirements and local information. On the other hand, traditional recommender systems are based on filtering techniques and contextual knowledge, and they usually do not rely on Natural Language Processing (NLP) features on users' queries, which are useful to understand and contextualize users' necessities on the spot. Therefore, comprehending the actual users' information needs and other key information that can be included in the user query, such as geographical references, is a challenging task which is not yet fully accomplished by current state-of-the-art solutions. In this paper, we propose Paval (Location-Aware Virtual Personal Assistant 2 2 The name Paval is chosen as a permutation of the initials of "Location-aware virtual personal assistant".), a semantic assisting engine for suggesting local points of interest (POIs) and services by analyzing users' natural language queries, in order to estimate the information need and potential geographic references expressed by the users. The system exploits NLP and semantic techniques providing as output recommendations on local geolocated POIs and services which best match the users' requests, retrieved by querying our semantic Km4City Knowledge Base. The proposed system is validated against the most popular virtual assistants, such as Google Assistant, Apple Siri and Microsoft Cortana, focusing the assessment on the request of geolocated POIs and services, showing very promising capabilities in successfully estimating the users' information needs and multiple geographic references. [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]

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 Semantic Web has emerged as an extension of the current Web, in which Web content has well-defined meaning through the addition of logic-based metadata. However, current mechanisms for information retrieval from semantic knowledge bases restrict their use to only experienced users. To address this gap, the natural language processing (NLP) is deemed to be very intuitive from a use point of view, due to it hides the formality of a knowledge base as well as the executable query language. This paper presents a novel ontology-based information retrieval system for DBpedia called ONLI (Ontology-based Natural Language Interface). ONLI proposes the use of an ontology model in order to represent both the syntactic question’s structure and the question’s context. This model allows inferring the answer type expected by the user through an established question’s classification. These features allow reducing the search space thus increasing the probability of providing the correct answer. From this perspective, ONLI was evaluated in terms of their ability to find the correct answer into DBpedia’s content, achieving promising results and proving to be very useful to non-experienced users. [ABSTRACT FROM AUTHOR]

Given the huge amount of heterogeneous data stored in different locations, it needs to be federated and semantically interconnected for further use. This paper introduces WINFRA, a comprehensive open-access platform for semantic web data and advanced analytics based on natural language processing (NLP) and data mining techniques (e.g., association rules, clustering, classification based on associations). The system is designed to facilitate federated data analysis, knowledge discovery, information retrieval, and new techniques to deal with semantic web and knowledge graph representation. The processing step integrates data from multiple sources virtually by creating virtual databases. Afterwards, the developed RDF Generator is built to generate RDF files for different data sources, together with SPARQL queries, to support semantic data search and knowledge graph representation. Furthermore, some application cases are provided to demonstrate how it facilitates advanced data analytics over semantic data and showcase our proposed approach toward semantic association rules. [ABSTRACT FROM AUTHOR]