Breaking Boundaries

Traditional citation analyses often fail to capture how research reshapes intellectual landscapes. This study applies Structural Variation Analysis (SVA) to assess the interdisciplinary impact of a highly cited paper on the Cross-Entropy Method in Operations Research. Using co-citation network analysis, the authors examine structural shifts through key metrics, including modularity change (∆M = 52.57), cluster linkage (CL = 135.2), centrality divergence (CKL = 0.47), and entropy (E = 0.98). The findings reveal that this methodological paper plays a pivotal role in bridging previously unconnected research domains. Beyond accumulating citations, the Cross-Entropy Method has fundamentally altered research connectivity. SVA offers an early indicator of transformative research before conventional citation metrics can capture their full impact.

India is known for its traditional medicinal system such as Ayurveda, Yoga, Unani, Siddha and Homeopathy. Ayurveda plays a significant role in curing the diseases without any side effects. Medicinal plants or herbs are considered as a major resource in meeting the need of people health care. Information about this medicinal knowledge must be preserved and digitized. There have been a massive number of publications and large number of articles on ayurvedic research in the form of unstructured textual data. Text mining approach is used to provide the solution to handle such voluminous of unstructured data. With the exponential growth of text based data, navigating the relevant information needed is the challenging task. Semantic understanding of document content forms the vital requirement for ensuring the quality of content retrieval. However, the current approaches are finding variation in textual classification in bringing the classification accuracy which may fail to understand the data during classification. Hence, an efficient model is required to search, classify and retrieve the most relevant data. The main objective of this research is to develop an effective and efficient framework and algorithm to search and retrieve the most relevant facts by including the application of ontology-based text mining approach. The current status of research is analyzed and reviewed in the area of semantic web retrieval, ontology-based approaches and various classification technique for building the framework. Text mining with the special emphasis on understanding the semantic meaning of content is achieved by using domain ontology called medicinal plant ontology construction. The challenges in finding the semantically related content for the given query are achieved through semantic web and ontology which enriched the data on web for structured representation thereby providing the strong semantics in knowledge representation. The methodology of information extraction is implemented by using medicinal plant ontology with semantic knowledge representation, an algorithm called OCEC (Ontology based Concept Extraction and Classification) was developed where each term is described semantically by mapping the terms and its related terms in the medicinal plant ontology. The web language called Web Ontology Language (OWL) is used for knowledge representation and is considered as richer semantic description language for describing unstructured and semi-structured content on the web thereby extracting the exact and relevant data and to offer a strong semantic search. To evaluate the performance of the proposed method, less relevant and most relevant documents were collected from online sources and digital libraries. Comparative study has been performed with various classification techniques. The experimental results show that the proposed method out performed. To further prove the efficiency of the model, experiments were conducted by giving different queries and the results are compared with other existing methods. The results show that the content retrieved by the proposed model improves precision and recall results. Keyword : Traditional Medicine, Ayurveda, Ontology, Semantic Web, Web Ontology Language

In this article, I describe the basic technologies for Semantic Web and relationship between Semantic Web and Knowledge Representation in Artificial Intelligence. Semantic Web is planned as an extension of the current web in order to help cooperation between computers and humans, i.e., computers and humans are expected to understand each other in the knowledge level. I first describe the vision of the Semantic Web, then introduce the current Semantic Web technologies, i.e., RDF, RDPS, and OWL. I describe relationship between the trend of Semantic Web and Knowledge Representation, and clarify challenges and difficulties of Semantic Web from the point of view of Knowledge Representation.

Temporal knowledge representation and reasoning is a major research field in Artificial Intelligence, in Database Systems, and in Web and Semantic Web research. The ability to model and process time and calendar data is essential for many applications like appointment scheduling, planning, Web services, temporal and active database systems, adaptive Web applications, and mobile computing applications. This article aims at three complementary goals. First, to provide with a general background in temporal data modeling and reasoning approaches. Second, to serve as an orientation guide for further specific reading. Third, to point to new application fields and research perspectives on temporal knowledge representation and reasoning in the Web and Semantic Web.

Artificial Intelligence and Semantic Web technologies are redefining digital storytelling by creating personal, interactive narratives that adapt to the user's input in real time. The Semantic Web and Artificial Intelligence have revolutionized web storytelling, turning it into a dynamic, interactive, and personalised experience. Artificial Intelligence models and structured web technologies reinforce narratives based on user input and real-time interaction. Our paper examines the transformative impact of emerging technologies, notably the Semantic Web and Artificial Intelligence on contemporary digital storytelling. Based on an interdisciplinary approach that spans the fields of digital humanities and computer science, our research examines how narrative structures are redefined, improved and democratized by semantic enrichment and algorithmic narrative. By examining recent theoretical frameworks and empirical studies, our work has identified new paradigms for narrative construction and delivery, and has highlighted the dynamic interaction between human creativity and machine intelligence. Our study uses a mixed methodology, combining qualitative content analysis with quantitative assessments of digital storytelling platforms. The findings suggest that AI-based tools and semantic web technologies will allow for greater contextual accuracy, personalisation and interactivity in narrative, thus redefining traditional narrative boundaries. The implications for future research and practice in the academic and professional digital media communities are discussed.

Artificial Intelligence and Semantic Web technologies are redefining digital storytelling by creating personal, interactive narratives that adapt to the user's input in real time. The Semantic Web and Artificial Intelligence have revolutionized web storytelling, turning it into a dynamic, interactive, and personalised experience. Artificial Intelligence models and structured web technologies reinforce narratives based on user input and real-time interaction. Our paper examines the transformative impact of emerging technologies, notably the Semantic Web and Artificial Intelligence on contemporary digital storytelling. Based on an interdisciplinary approach that spans the fields of digital humanities and computer science, our research examines how narrative structures are redefined, improved and democratized by semantic enrichment and algorithmic narrative. By examining recent theoretical frameworks and empirical studies, our work has identified new paradigms for narrative construction and delivery, and has highlighted the dynamic interaction between human creativity and machine intelligence. Our study uses a mixed methodology, combining qualitative content analysis with quantitative assessments of digital storytelling platforms. The findings suggest that AI-based tools and semantic web technologies will allow for greater contextual accuracy, personalisation and interactivity in narrative, thus redefining traditional narrative boundaries. The implications for future research and practice in the academic and professional digital media communities are discussed.

Getting AI Right: Introductory Notes on AI & Society

A major focus in the design of Semantic Web ontology languages used to be on finding a suitable balance between the expressivity of the language and the tractability of reasoning services defined over this language. This focus mirrors the original vision of a Web composed of machine readable and understandable data. Similarly to the classical Web a few years ago, the attention is recently shifting towards a user-centric vision of the Semantic Web. Essentially, the information stored on the Web is from and for humans. This new focus is not only reflected in the fast growing Linked Data Web but also in the increasing influence of research from cognitive science, human computer interaction, and machine-learning. Cognitive aspects emerge as an essential ingredient for future work on knowledge acquisition, representation, reasoning, and interactions on the Semantic Web. Visual interfaces have to support semantic-based retrieval and at the same time hide the complexity of the underlying reasoning machinery from the user. Analogical and similarity-based reasoning should assist users in browsing and navigating through the rapidly increasing amount of information. Instead of pre-defined conceptualizations of the world, the selection and conceptualization of relevant information has to be tailored to the user's context on-the-fly. This involves work on ontology modularization and context-awareness, but also approaches from ecological psychology such as affordance theory which also plays an increasing role in robotics and AI. During the Dagstuhl Seminar 12221 we discussed the most promising ways to move forward on the vision of bringing findings from cognitive science to the Semantic Web, and to create synergies between the different areas of research. While the seminar focused on the use of cognitive engineering for a user-centric Semantic Web, it also discussed the reverse direction, i.e., how can the Semantic Web work on knowledge representation and reasoning feed back to the cognitive science community.

Work on the Semantic Web is all too often phrased as a technological challenge: how to improve the precision of search engines, how to personalise web-sites, how to integrate weakly-structured data-sources, etc. This suggests that we will be able to realise the Semantic Web by merely applying (and at most refining) the results that are already available from many branches of Computer Science. I will argue in this talk that instead of (just) a technological challenge, the Semantic Web forces us to rethink the foundations of many subfields of Computer Science. This is certainly true for my own field (Knowledge Representation), where the challenge of the Semantic Web continues to break many often silently held and shared assumptions underlying decades of research. With some caution, I claim that this is also true for other fields, such as Machine Learning, Natural Language Processing, Databases, and others. For each of these fields, I will try to identify silently held assumptions which are no longer true on the Semantic Web, prompting a radical rethink of many past results from these fields.

The vision of the Semantic Web is a collection of documents in the World Wide Web whose content is meaningful to computers. In this vision computers can directly and effectively process information stored in the Web without having to depend on human guidance. This vision also underlies Knowledge Representation, a subdiscipline of Artificial Intelligence. Research in Knowledge Representation over the last four decades has produced some surprising results but has also identified some very difficult problems that need to be overcome if this vision is to be realized in its entirety. The Semantic Web vision provides several opportunities for Knowledge Representation that have not been exploited in the past as well as some new difficulties that could hinder application of techniques from Knowledge Representation to the Semantic Web. Achieving the vision of the Semantic Web will require exploiting the successes of Knowledge Representation in a challenging new environment.

The paper discusses what kind of entity the proposed Semantic Web (SW) is, in terms of the relationship of natural language structure to knowledge representation (KR). It argues that there are three distinct views on the issue: first, that the SW is basically a renaming of the traditional AI knowledge representation task, with all the problems and challenges of that task. If that is the case, as many believe, then there is no particular reason to expect progress in this new form of presentation, as all the traditional problems of logic and representation reappear and it will be no more successful outside the narrow scientific domains where KR seems to work even though the formal ontology movement has brought some benefits. The paper contains some discussion of the relationship of current SW doctrine to representation issues covered by traditional AI, and also discusses issues of how far SW proposals are able to deal with difficult relationships in parts of concrete science. Secondly, there is a view that the SW will be the WorldWideWeb with its constituent documents annotated so as to yield their content or meaning structure more directly. This view of the SW makes natural language processing central as the procedural bridge from texts to KR, usually via a form of automated Information Extraction. This view is discussed in some detail and it is argued that this is in fact the only way of justifying the structures used as KR for the SW. There is a third view, possibly Berners-Lee's own, that the SW is about trusted databases as the foundation of a system of web processes and services, but it is argued that this ignores the whole history of the web as a textual system, and gives no better guarantee of agreed meanings for terms than the other two approaches.

We live in the age of big data, wherein production and analysis of the massive amounts of data in relation to the various interactions of humans, objects and technologies have become a new everyday common. It comes with no surprise that knowledge organization community has also embraced the data-driven inquiry to advance representation, organization and discovery of the knowledge. In particular, semantic technologies allowed to connect knowledge across institutions, platforms and cultures, bringing a new dimension to representation and organization of knowledge. This paper presents analysis of the knowledge organization research that employed a large-scale or big data analysis techniques to find what are methodologies, research questions, and implications of big data approaches are. Analysis of over 500 scholarly works indexed in Library and Information Science Full text and Google Scholar databases suggests advantages of a large-scale data integration approaches. For instance, Baca and Gill (2015) paper presents how semantic technologies have allowed multilingual and cross-cultural representation of Getty Art & Architecture Thesaurus (AAT), the Getty Thesaurus of Geographic Names (TGN) and the Union List of Artist Names (ULAN). Mayr and Zeng (2017) argue that the semantic web standards, such as SKOS, OWL, RDFS, and SPARQL allowes to publish knowledge organization systems (KOS) as Linked Open Data (LOD). Mayr and Zeng proposes the following outcomes of LOD application: transformation of KOS vocabulary into the lightweight OWL ontologies or SKOS vocabulary datasets, and accessibility of the data by means of SPARQL endpoints. However, the data-driven knowledge organization initiatives raise significant questions on whether data-driven access to the knowledge would facilitate and/or transform the use and accessibility of the knowledge organization systems, whether it would help us to understand humans’ knowledge representation, organization and discovery behavior, or whether it would usher new forms of biases, limitations and privacy incursions. A large corpus of knowledge representation and organization research have discussed various biases of knowledge organization systems, such as representation of marginalized and indigenous populations. For instance, indigenous scholars have demonstrated lack of understanding of indigenous epistemologies in representation of indigenous cultures that resulted in limited and partial representation of indigenous knowledge (Doyle, 2006; Metoyer & Doyle, 2015) . Moreover, algorithmic biases that are built-in in platforms and systems, such as Google search engine, are another major concern when it comes to such issues as utilization of user-generated content to complement traditional representation of resources. The data-driven approach also raises ethical issues related to incorporation of user-generated content without users’ consent. In this regard, Ibekwe-SanJuan and Bowker (2017) confront the relevance of universal bibliographic classification and thesaurus, arguing that big data will not remove the need for human constructed systems. Authors also suggest a shift from purely universalist and top-down approach to more descriptive bottom-up approaches that could potentially include diverse viewpoints. Taking into consideration the complexity of the process of representation of knowledge, we argue that data-drive approach would have little to no effect on eliminating limitations and biases of existing knowledge organization and discovery systems. This study suggests that it is necessary to critically interrogate the advantages of big data approach to knowledge representation and organization to spark conversations about the cultural, technological, scholarly, societal and ethical implications of data driven approach to the knowledge representation, organization and discovery. This study argues that while a data-driven approach would certainly be valuable in provision of a large-scale representation of knowledge, only human- and community- centered approaches to knowledge representation and organization would enhance and ensure multifaceted and rich representation of the knowledge. References Baca, M., & Gill, M. (2015). Encoding multilingual knowledge systems in the digital age: The Getty vocabularies. The fifth North American Symposium on Knowledge Organization ( NASKO 2015), June 18-19, 2015, Los Angeles, California. Retrieved from http://www.iskocus.org/NASKO2015proceedings/Gill%20.pdf Doyle, A. M. (2006). Naming and reclaiming indigenous knowledge: Intersections of landscape and experience. In G. Budin, C. Swertz & K.Mitgutsch (Eds.) Advances in knowledge organization (10), Knowledge Organization for a Global Learning Society: Proceedings of the Ninth International ISKO Conference in Vienna, Austria, 2006, Ergon Verlag, Wurzburg, pp. 435-442. Ibekwe-SanJuan, F., & Bowker, G.C. (2017). Implications of big data for knowledge organization. Knowledge Organization, 44 (3) , 187-198. Mayr, P., & Zeng, M. (2017). Knowledge organization systems in the semantic web. International Society for Knowledge Organization (ISKO), UK Conference 2017, September 11-12. 2017, London, UK. Retrieved from http://www.iskouk.org/content/knowledge-organization-systems-kos-semantic-web Metoyer, C. A., & Doyle, A.M. (2015). Introduction. Cataloging & Classification Quarterly,53 (5-6), 475-478.

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2004 Spring Symposium Series, Monday through Wednesday, March 22-24, at Stanford University. The titles of the eight symposia were (1) Accessible Hands-on Artificial Intelligence and Robotics Education; (2) Architectures for Modeling Emotion: Cross-Disciplinary Foundations; (3) Bridging the Multiagent and Multirobotic Research Gap; (4) Exploring Attitude and Affect in Text: Theories and Applications; (5) Interaction between Humans and Autonomous Systems over Extended Operation; (6) Knowledge Representation and Ontologies for Autonomous Systems; (7) Language Learning: An Interdisciplinary Perspective; and (8) Semantic Web Services. Each symposium had limited attendance. Most symposia chairs elected to create AAAI technical reports of their symposium, which are available as paperbound reports or (for AAAI members) are downloadable on the AAAI members-only Web site. This report includes summaries of the eight symposia, written by the symposia chairs.

Artificial intelligence has a long history of learning from domain problems ranging from chess to jeopardy. In this work, we look at a problem stemming from social science, namely, how do social relationships influence communication content and vice versa. The tools used to study communication content (content analysis) have rarely been combined with those used to study social relationships (social network analysis). Furthermore, there is even less work addressing the longitudinal characteristics of such a combination. This paper presents a general framework for measuring the dynamic bi-directional influence between communication content and social networks. The framework leverages the idea that knowledge about both kinds of networks can be represented using the same knowledge representation. In particular, through the use of Semantic Web standards, the extraction of networks is made easier. The framework is applied to two use-cases: online forum discussions and conference publications. The results provide a new perspective over the dynamics involving both social networks and communication content.

Pigmentation has long served as a powerful system for exploring gene–trait relationships, yet much of the field has focused on a relatively narrow group of well-established genes involved in melanin production and pigment cell differentiation. Recent advances, however, have allowed pigmentation to be studied through a more comprehensive framework. By combining artificial intelligence (AI)–driven phenotyping with genomic mapping approaches such as genome-wide association studies, QTL mapping, and structural variant analysis, a broader range of pigmentation regulators has been identified across diverse animal taxa. This review highlights studies where AI methods, including deep learning, self-supervised modeling, and pattern recognition, have been used to quantify complex pigmentation traits in animals. These approaches have enabled the discovery of non-classical pigmentation genes involved in membrane trafficking, intracellular signaling, structural organization, and non-coding regulation. Rather than displacing the classical pigmentation paradigm, these findings extend it, revealing a wider set of genetic contributors to coloration and pattern diversity. We introduce the term AI-pigmentomics to describe the integration of AI-driven phenotyping with genomic mapping, as part of the broader emergence of AI-omics. Together, AI and genomic mapping are reshaping our understanding of pigmentation by uncovering unexpected biological mechanisms and providing a framework for investigating pigmentation in both model and non-model species.