Search

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 paper presents a benchmarking survey on query expansion techniques for social media information retrieval, with a focus on comparing the performance of methods using semantic web technologies. The study evaluated query expansion techniques such as generative AI models and semantic matching algorithms and how they are integrated in a semantic framework. The evaluation was based on cosine similarity metrics, including the Discounted Cumulative Gain (DCG), Ideal Discounted Cumulative Gain (IDCG), and normalized Discounted Cumulative Gain (nDCG), as well as the Mean Average Precision (MAP). Additionally, the paper discusses the use of semantic web technologies as a component in a pipeline for building thematic knowledge graphs from retrieved social media data with extended ontologies integrated for the refugee crisis. The paper begins by introducing the importance of query expansion in information retrieval and the potential benefits of incorporating semantic web technologies. The study then presents the methodologies and outlines the specific procedures for each query expansion technique. The results of the evaluation are presented, as well as the rest semantic framework, and the best-performing technique was identified, which was the curie-001 generative AI model. Finally, the paper summarizes the main findings and suggests future research directions. [ABSTRACT FROM AUTHOR]

t: The drastic growth of coastal observation sensors results in copious data that provide weather information. The intricacies in sensor-generated big data are heterogeneity and interpretation, driving high-end Information Retrieval (IR) systems. The Semantic Web (SW) can solve this issue by integrating data into a single platform for information exchange and knowledge retrieval. This paper focuses on exploiting the SW base system to provide interoperability through ontologies by combining the data concepts with ontology classes. This paper presents a 4-phase weather data model: data processing, ontology creation, SW processing, and query engine. The developed Oceanographic Weather Ontology helps to enhance data analysis, discovery, IR, and decision making. In addition to that, it also evaluates the developed ontology with other state-of-the-art ontologies. The proposed ontology’s quality has improved by 39.28% in terms of completeness, and structural complexity has decreased by 45.29%, 11% and 37.7% in Precision and Accuracy. Indian Meteorological Satellite INSAT-3D’s ocean data is a typical example of testing the proposed model. The experimental result shows the effectiveness of the proposed data model and its advantages in machine understanding and IR. [ABSTRACT FROM AUTHOR]

People are getting more and more used to consume a digital, online content. Many outlets switched to online publication or at least increased their online presence. Besides, online publication is not the only domain of publishing houses. Many different organisations and companies - including those from area of agriculture and rural development - provide online content in form of articles. Importance of semantic web is growing constantly. Together with metadata descriptions, it is necessary for all the current search engines, smart assistants and AI technologies. Public standards and open source software can significantly speed up development and reduce costs when it comes to the Internet and World Wide Web. The paper provides overview of an updated methodology for creation, storage and presentation of online information content in World Wide Web environment. The latest research was focused mainly on presentation and semantics. The whole research process is established as well as the final f ormulation of the methodology. [ABSTRACT FROM AUTHOR]

The central argument of this paper is that the design, implementation and use of technologies that underpin general semantic search have implications for what we know and the way in which knowledge is understood. Semantic search is an assemblage of technologies that most Internet users would use regularly without necessarily realising. Users of search engines implementing semantic search can obtain answers to questions rather than just retrieve pages that include their search query. This paper critically examines the design of the Semantic Web, upon which semantic search is based. It demonstrates that implicit in the design of the Semantic Web are particular assumptions about the nature of classification and the nature of knowledge. The Semantic Web was intended for interoperability within specific domains. It is here argued that the extension to general semantic search, for use by the general public, has implications for what type of knowledge is visible and what counts as legitimate knowledge. The provision of a definitive answer to a query, via the reduction of discursive knowledge into machine-processable data, provides the illusion of objectivity and authority in a way that is increasingly impenetrable to critical scrutiny. [ABSTRACT FROM AUTHOR]

Many Q&A sites initially run only in English, and then gradually release their multi-lingual variants to serve users who speak other languages. The launch of such multi-lingual sites always lead to an intense dispute about the pros and cons of multi-lingual sites. Although all arguments and concerns sound reasonable, people can rarely provide solid evidence to convince each other. In this paper, from users' comments about the launch of several non-English Stack Overflow sites, we first identify three major concerns including community split, knowledge needs and interests in other languages, and knowledge fragmentation and duplication. To validate these three concerns, we conduct an evidence-based data analysis and comparison of user characteristics, tag usage and cross-site links between the Russian Stack Overflow and the English Stack Overflow on these three concerns. Our study sheds light on the existence value and risks of multi-lingual Q&A sites. [ABSTRACT FROM AUTHOR]

Microblogging services such as Twitter are important, up-to-date, and live sources of information on a multitude of topics and events. An increasing number of systems use such services to detect and analyze events in real-time as they unfold. In this context, we recently proposed ArmaTweet—a system developed in collaboration among armasuisse and the Universities of Oxford and Fribourg to support semantic event detection on Twitter streams. Our experiments have shown that ArmaTweet is successful at detecting many complex events that cannot be detected by simple keyword-based search methods alone. Building up on this work, we explore in this paper several approaches for event detection on microposts. In particular, we describe and compare four different approaches based on keyword search (Plain-Seed-Query), information retrieval (Temporal Query Expansion), Word2Vec word embeddings (Embedding), and semantic retrieval (ArmaTweet). We provide an extensive empirical evaluation of these techniques using a benchmark dataset of about 200 million tweets on six event categories that we collected. While the performance of individual systems varies depending on the event category, our results show that ArmaTweet outperforms the other approaches on five out of six categories, and that a combined approach offers highest recall without adversely affecting precision of event detection. [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]

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]

Temporal features, such as an explicit date and time or a time-specific event, employ concise semantics for any kind of information retrieval. Therefore, temporal features should be suitable for linked data information retrieval. However, we have found that most linked data information retrieval techniques pay little attention to the power of temporal feature inclusion. We propose a keyword-based linked data information retrieval framework ‘ that can incorporate temporal features and give more concise results. The evaluation of our system performance indicates that it is promising. [ABSTRACT FROM AUTHOR]

Developing scalable, semi-automatic approaches to derive insights from a domain-specific Web corpus is a longstanding research problem in the knowledge discovery community. The problem is particularly challenging in illicit fields, such as human trafficking, where traditional assumptions concerning information representation are frequently violated. In this article, we describe an end-to-end investigative knowledge discovery system for illicit Web domains. We built and evaluated a prototype, involving separate components for information extraction, semantic modeling and query execution, on a real-world human trafficking Web corpus containing 1.3 million pages, with promising results. [ABSTRACT FROM PUBLISHER]

Purpose -- The purpose of this explorative research study is to focus on the implementation of semantic Web technology on agriculture domain of e-governance data. The study contributes to an understanding of problems and difficulties in implantations of unstructured and unformatted unique datasets of multilingual local language-based electronic dictionary (IndoWordnet). Design/methodology/approach -- An approach to an implementation in the perspective of conceptual logical concept to realization of agriculture-based terms and terminology extracted from linked multilingual IndoWordNet while maintaining the support and specification of the World Wide Web Consortium (W3C) standard of semantic Web technology to generate ontology and uniform unicode structured datasets. Findings -- The findings reveal the fact about partial support of extraction of terms, relations and concepts while linking to IndoWordNet, resulting in the form of SynSets, lexical relations of Words and relations between themselves. This helped in generation of ontology, hierarchical modeling and creation of structured metadata datasets. Research limitations/implications -- IndoWordNet has limitations, as it is not fully revised version due to diversified cultural base in India, and the new version is yet to be released in due time span. As mentioned in Section 5, implications of these ideas and experiments will have good impact in doing more exploration and better applications using such wordnet. Practical implications -- Language developer tools and frameworks have been used to get tagged annotated raw data processed and get intermediate results, which provides as a source for the generation of ontology and dynamic metadata. Social implications -- The results are expected to be applied for other e-governance applications. Better use of applications in social and government departments. Originality/value -- The authors have worked out experimental facts and raw information source datasets, revealing satisfactory results such as SynSets, sensecount, semantic and lexical relations, class concepts hierarchy and other related output, which helped in developing ontology of domain interest and, hence, creation of a dynamic metadata which can be globally used to facilitate various applications support. [ABSTRACT FROM AUTHOR]