Semantic Computing

In this paper we present a framework for unified, personalized access to heterogeneous multimedia content in distributed repositories. Focusing on semantic analysis of multimedia documents, metadata, user queries and user profiles, it contributes to the bridging of the gap between the semantic nature of user queries and raw multimedia documents. The proposed approach utilizes as input visual content analysis results, as well as analyzes and exploits associated textual annotation, in order to extract the underlying semantics, construct a semantic index and classify documents to topics, based on a unified knowledge and semantics representation model. It may then accept user queries, and, carrying out semantic interpretation and expansion, retrieve documents from the index and rank them according to user preferences, similarly to text retrieval. All processes are based on a novel semantic processing methodology, employing fuzzy algebra and principles of taxonomic knowledge representation. The first part of this work presented in this paper deals with data and knowledge models, manipulation of multimedia content annotations and semantic indexing, while the second part will continue on the use of the extracted semantic information for personalized retrieval.

FindSampo fosters collecting, sharing, publishing and studying archaeological finds discovered by the public. The framework includes the following: a mobile find-reporting system; a semantic portal for researchers, the public and collection managers to use; and a Linked Open Data service for creating custom data analyses and for application developers.

This paper presents the FindSampo system for analyzing and disseminating archaeological object finds made by the public. The system is based on Linked Open Data (LOD), and consists of a web portal and an open data service. The underlying knowledge graph contains data of some 3000 archaeological object finds catalogued in the archaeological collection of the Finnish Heritage Agency (FHA) from 2015 to 2020. The portal and LOD service have been open to public use since May 2021.

This paper outlines the ongoing work of the DigiNUMA project for creating solutions in data harmonisation, analysis, and dissemination of pan-European archaeological and numismatic Cultural Heritage, using linked data and semantic web technologies. The project focuses on Viking Age (800-1150 AD) Finnish and English numismatic data as a case study. A broader context is gained by research into harmonizing collection data of the National Museum of Finland, the British Museum, and the Fitzwilliam Museum in Cambridge for compatibility with the international Nomisma.org ontology, and by creating tools that can be used to work with other Nomisma.org datasets.

https://2022.eswc-conferences.org/wp-content/uploads/2022/05/pd_Rantala_et_al_paper_238.pdf

This paper discusses current challenges in archaeological cultural heritage data management and presents the interdisciplinary research project DigiNUMA. The project investigates solutions in data harmonisation and dissemination of pan-European cultural heritage through an interdisciplinary and cross-sectoral project in Digital Humanities, semantic computing, participatory heritage, museum collections management and archaeological/numismatic studies. Using Finnish and English numismatic data as a test case, DigiNUMA creates ontological infrastructure and a proof-of-concept data model for finely-grained Linked Open Data (LOD) harmonisation across national and international databases for cultural heritage data, and tests it through a broad suite of Digital Humanities analyses.
https://ceur-ws.org/Vol-3232/paper34.pdf

Museums, heritage agencies and other institutions responsible for managing archaeological cultural heritage across Europe are engaged in developing digital platforms to better open their collections to the public as a common resource for the purposes of discovering, learning about, and sharing our common past. This paper explores the potential of new semantic computing technologies in democratizing not only public access to digital cultural heritage records, but also to computational and Linked Open Data-assisted data analysis and knowledge discovery. As a case study, we consider archaeological and numismatic Open Data services in Finland, and discuss the research results obtained during the ongoing development work for the CoinSampo framework for opening Finnish and international numismatic data. Existing digital cultural heritage services are often built with the needs of professional collections management in mind. The presentation of the records is typically structured after the familiar format established for the printed catalogues of yesteryear, with few analytical tools that would take advantage of the potential of digital data to probe and visualize internal relationships and patterns within the full body of the opened material. CoinSampo, however, will provide scientific tools to new audiences among the non-professional public who have not enjoyed such a level of access to numismatic data. The broad range of target audiences we envisage includes collections managers, who will benefit from enhanced access to their own data for updating records and for error detection and correction, as well as academic researchers interested in using the material in scientific analysis. Importantly, it also includes non-professional groups such as coin collectors, educators, local historians, and the archaeological hobby metal-detectorists who produce most of the new coin finds entering the national collections in Finland and in other European countries where detecting is legal. By adopting a citizen science and participatory heritage approach in the development of Open Data services, we aim to promote a technological model for cultural heritage dissemination that addresses the needs of a wide spectrum of different user audiences inside and outside the professional sphere. CCS Concepts: • Information systems → Web searching and information discovery; • Applied computing → Arts and humanities;

Good data in, good data out' is a well-known adage, often used to consider the 'precision' of data entry rather than other human activity when inputting information into a database. These could be omissions or errors, but are just as likely to be inconsistencies in data recording. This is especially relevant when thinking about the efficiencies that recorders might make (i.e., to make their work easier) and what this could mean for end users (e.g., researchers). For example, a recorder of an archaeological object might do things differently based on their knowledge or understanding of the value of recording certain types of data (e.g., the various characteristics of the object) and how this information is likely to be used by others. With this in mind, this article will consider the research question of how can advancing Findable, Accessible, Interoperable, Reusable (FAIR) principles be used to contextualise the impact of human decision-making on the research value of archaeological data, taking (as a test case) the Portable Antiquities Scheme (PAS) database of archaeological finds from England and Wales. The methodology used here examines data recorded within the PAS database directly and through a separate web application demonstrator (PASampo) that is being developed. The latter, in particular, enables ease-to-use visualisation of the data to provide an analytical evaluation of how data is inputted into the PAS database and, more significantly, how the researcher might extract this data. Through a series of case studies exploring aspects of material culture, we will highlight how data

This paper presents the idea and preliminary results of the ARCH-ON (Archaeological Ontologies) pilot project. This project aims to lay the groundwork for the semantic description and classification of archaeological artefacts, enabling new approaches to the recording, study, and public accessibility of information regarding material culture from the past.

Structural ambiguity in a sentence cannot be resolved without semantic help. We present a process for structural disambiguation that uses verb expectations, presupposition satisfaction, and plausibility, and an algorithm for making the final choice when these cues give conflicting information. The process, called the Semantic Enquiry Desk, is part of a semantic interpreter that makes sure all its partial results are wellformed semantic objects; it is from this that it gains much of its power.

Brand erasure (Figueira, 2026a, 2026b) named the failure mode of brand theory in AI-mediated markets-the systematic suppression of brand identity inside synthesized answers and completed agentic actions. This paper develops the corresponding positive construct: Brand Legibility, the degree to which a brand is recognizable, parseable, and attributable by the AI systems that mediate consumer experience. We position Brand Legibility as the latent-substrate counterpart of brand salience (Romaniuk & Sharp, 2004) and the operational property whose absence produces erasure. We introduce the Semantic Equity Index (SEI) as the quantitative measure of legibility and the Brand Legibility Framework as the operating model that integrates Answer Engine Optimization, Generative Engine Optimization, and Agentic Optimization as three coordinated layers. We close with the Brand Erasure Audit as the measurement protocol that operationalizes the framework, and with implications for brand-equity theory, marketing organizations, and the governance of attribution.

Precision farming technologies have been increasingly recognized for their potential ability for improving agricultural productivity, reducing production cost, and minimizing damage to the environment. In this context, the main goals of this paper are the following: First, we present a methodology that can be applied to extract semantic information, more speci¯cally some vegetative indices, from plants, in order to further improve the vegetation representation and health by means of a speci¯c semantic robotic system; then, we study in detail the tracked robot's behavior, by emulating the real settings in a ¯eld and analytically analyze the simulation of the robot on an up and down slope path.

Robotic Computing addresses the synergetic interaction of computing technologies and robotic technologies. Researches include either incorporating software into a robot to make it more intelligent or applying an algorithm to a robot to visualize the e®ectiveness of software physically. There are many types of robots used in many di®erent environments for di®erent uses. As time goes on, we can expect to see many more cyber-physical systems developed in the future to act upon the physical world. The special issue consists of nine papers. The selected papers underwent a rigorous extra refereeing and revision process. In particular, the papers have been extended with at least 30% new and unpublished materials. We want to thank all the authors who made this special issue possible. Although many good papers did not make it into this special issue, we hope that this issue will provide a further impetus to the developments in the ¯eld of robotic computing.

The human elements of personality working behind the creation of a write-up play an important part in determining the final dominant mood of a text. This paper presents a tool, PsycheTagger, which extracts the emotive content of a text in English Language in its context and tags each open-class word of the text with one of the predefined psyche categories that represent the emotive content. Working in the lines of statistical Partsof-Speech Taggers, this tool is an example of a semantic tagger. The tagger self-ranks its choices with a probabilistic score, calculated using Viterbi algorithm run on a Hidden Markov Model of the psyche categories. The results of the tagging exercise are critically evaluated on the Likert scale. These results strongly justify the validity and determine high accuracy of tagging using the probabilistic parser.

We investigate the effects of semantically-based crossover operators in genetic programming, applied to real-valued symbolic regression problems. We propose two new relations derived from the semantic distance between subtrees, known as semantic equivalence and semantic similarity. These relations are used to guide variants of the crossover operator, resulting in two new crossover operatorssemantics aware crossover (SAC) and semantic similarity-based crossover (SSC). SAC, was introduced and previously studied, is added here for the purpose of comparison and analysis. SSC extends SAC by more closely controlling the semantic distance between subtrees to which crossover may be applied. The new operators were tested on some real-valued symbolic regression problems and compared with standard crossover (SC), context aware crossover (CAC), Soft Brood Selection (SBS), and No Same Mate (NSM) selection. The experimental results show on the problems examined that, with computational effort measured by the number of function node evaluations, only SSC and SBS were significantly better

Semantic Web is, without a doubt, gaining momentum in both industry and academia. The word "Semantic" refers to "meaning" -a semantic web is a web of meaning. In this fast changing and result oriented practical world, gone are the days where an individual had to struggle for finding information on the Internet where knowledge management was the major issue. The semantic web has a vision of linking, integrating and analysing data from various data sources and forming a new information stream, hence a web of databases connected with each other and machines interacting with other machines to yield results which are user oriented and accurate. With the emergence of Semantic Web framework the naïve approach of searching information on the syntactic web is cliché. This paper proposes an optimised semantic searching of keywords exemplified by simulation an ontology of Indian universities with a proposed algorithm which ramifies the effective semantic retrieval of information which is easy to access and time saving.

Knowledge graphs (KGs) have become a foundational paradigm for representing structured knowledge and supporting semantic reasoning across diverse domains. Traditionally, KGs have been constructed from textual and structured data, queried using symbolic languages such as SPARQL, and applied in areas ranging from search engines to recommendation systems. The rapid proliferation of multimodal data, including images, audio, video, and sensor signals, has led to the emergence of multimodal knowledge graphs (MMKGs), which integrate multiple modalities into unified representations. In parallel, advances in natural language interfaces, neural embeddings, and large language models (LLMs) have transformed both the construction and querying of KGs, enabling greater scalability, adaptability, and semantic richness. This survey presents a comprehensive review of recent developments in KG construction, querying methods, and multimodal extensions. We analyze manual, semi-automatic, automatic, and LLM-assisted construction pipelines; compare querying paradigms from symbolic approaches to hybrid neuralenhanced systems; and examine multimodal integration techniques such as entity alignment, feature fusion, and cross-modal reasoning. Through comparative analysis, we identify persistent challenges, including ontology alignment, data quality, interpretability, and the standardization of multimodal queries, while also highlighting opportunities for hybrid symbolic-neural frameworks and domain-specific MMKG applications.

The study examined semantic properties of kinship terms in Ibeme. It specifically analyzed the internal organization of the terms as well as meaning properties of the terms as constituting a semantic field. Drawing on a dataset collected through oral interviews with informants comprising twenty aged people and traditional titleholders, the study discovered that Ibeme kinship terms reveal some bits of information about descent (patrilineal and matrilineal as well as generations), age differences, and gender nuances in interesting and complex ways that significantly vary from what obtains in other Igbo-speaking communities and culture areas. We observed that Ibeme kinship terms neatly fall into the three main relations of descent, 'siblingship' and affinity. The study concludes that kinship terms as salient features of linguistic communities, exhibit variation across languages and even dialects of a language.

This paper discusses the evolving sophistication of the Internet and the World Wide Web abuses in Africa. It begins by reviewing known abuses worldwide. It also reviews the various technology solutions in place to reduce the abuses. The paper notes that the various security measures and protocols identified require human intervention based on trust. It argues that human behaviors are unpredictable, and that human trust cannot always be absolutely guaranteed as they can at any point in time compromise their integrity. The paper also notes ongoing efforts of some African countries at updating their country laws. The author proposes the creation and enforcement of new international laws that would compliment country laws and preserve basic civil rights in the electronic environment as a way of bringing sanity into the use of the Internet and the World Wide Web. Such laws must particularly incorporate measures that would prevent governmental abuses. The paper also suggests the formation of a separate international court with exclusive jurisdiction over the Internet.

Several deep learned lossy compression techniques have been proposed in the recent literature. Most of these are optimized by using either MS-SSIM (multi-scale structural similarity) or MSE (mean squared error) as a loss function. Unfortunately, neither of these correlate well with human perception and this is clearly visible from the resulting compressed images. In several cases, the MS-SSIM for deep learned techniques is higher than say a conventional, non-deep learned codec such as JPEG-2000 or BPG. However, the images produced by these deep learned techniques are in many cases clearly worse to human eyes than those produced by JPEG-2000 or BPG. We propose the use of an alternative, deep perceptual metric, which has been shown to align better with human perceptual similarity. We then propose Deep Perceptual Compression (DPC) which makes use of an encoder-decoder based image compression model to jointly optimize on the deep perceptual metric and MS-SSIM. Via extensive human evalua...

The rapid growth of wireless communications has created a significant demand for high through-put, seamless connectivity, and extremely low latency. To meet these goals, a novel technology - stacked intelligent metasurfaces (SIMs) - has been developed to perform signal processing by directly utilizing electromagnetic waves, thus achieving incredibly fast computing speed while reducing hardware requirements. In this article, we provide an overview of SIM technology, including its underlying hardware, benefits, and exciting applications in wireless communications. Specifically, we examine the utilization of SIMs in realizing transmit beamforming and semantic encoding in the wave domain. Additionally, channel estimation in SIM-aided communication systems is discussed. Finally, we highlight potential research opportunities and identify key challenges for deploying SIMs in wireless networks to motivate future research.

This paper describes an algorithm for semantic decomposition. For that we surveys languages used to enrich contextual information with semantic descriptions. Such descriptions can be e.g. applied to enable reasoning when collecting vast amounts of information. In particular, we focus on the elements of the languages that make up their semantic. To do so, we compare the expressiveness of the well-known languages OWL, PDDL and MOF with a theory from linguistic called the Natural Semantic Metalanguage. We then analyze how the semantic of the language is build up and describe how semantic decomposition based on the semantic primes can be used for a so called mental lexicon. This mental lexicon can be used to reason upon semantic service description in the research domain of service match making.

March 9, 2026
Today I submitted a formal audit request to federal authorities regarding Medicaid billing for Applied Behavior Analysis (ABA) services in California. This follows a pilot investigation I conducted in San Diego examining how autism “therapy” services are billed to Medi-Cal.

The full report is now public: https://doogri.org/2026/03/09/cms-audit-request-san-diego-pilot-march-6-2026/

For months I have been analyzing how the ABA industry bills Medicaid. What I found raises serious program-integrity concerns that deserve federal scrutiny.

Enterprise telecommunications has historically been shaped by the Public Switched Telephone Network (PSTN), a circuit-switched infrastructure that defined voice communication for over a century. While PSTN provided reliability and global reach, the architectural limitations of circuit switching, geographic dependency, and capitalintensive infrastructure have increasingly constrained modern enterprises operating in distributed digital environments. Voice over Internet Protocol (VoIP) represents a structural transformation in telecommunications by migrating voice services onto packet-switched IP networks and cloud-native infrastructures. This paper examines the architectural advantages of cloud-based VoIP systems over legacy telephony, with emphasis on enterprise scalability, global virtual numbering, economic efficiency, and advanced operational capabilities such as analytics and AI-driven voice agents. By analyzing both infrastructure design and operational outcomes, the research demonstrates how cloud telephony platforms enable organizations to transcend geographic telecom constraints while delivering flexible, device-agnostic communication environments. The study further evaluates the implications for enterprise decision-makers responsible for communications infrastructure modernization.

Unauthorized access (hacking); malware and viruses; software piracy (unauthorized copying, distribution, use, or installation of software that is protected by copyright), hardware misuse; computer-related fraud. It regulates misuse of computers, such as illegal access, data theft. It covers offences targeting computer systems. Key characteristic: Device-centered regulation. Limitation: Too narrow for modern digital realities.  Internet Law (Law of the network) It's a law governing legal issues arising from use of the internet (online content). Online transactions and speech.

From its first introduction in this same conference, the original prototype of the Semantic Bookmarking tool Semantic Turkey has undergone a deep and extensive revision process, breaking the boundaries of its original intents and going more and more towards an extensible platform for Knowledge Management and Acquisition based on Semantic Web technologies. Following its recent official release, we discuss here the main innovations of this system, its potential applications and future plans for its improvement

In this work we describe Semantic Turkey, a Semantic Extension for the popular web browser Mozilla Firefox. Semantic Turkey can be used to keep track of relevant information from visited web sites and organize collected content according to a personally defined ontology. In this sense, Semantic Turkey can be seen both as an advanced semantic bookmarking system as well as an ontology editing assistant, which allows domain experts and ontology developers to build ontologies starting from the very raw source of information which they find on the web. The open architecture of Semantic Turkey and the specific threelayered approach of its design also allows for scaling the basic personal desktop application embodied by this tool up to a distributed framework for collaborative semantic annotation and ontology editing. This paper describes the architecture and the functionalities of the Semantic Turkey extension for Firefox, and describe possible evolutions for future improvement of the tool.

In this paper, we construct simultaneously multi-layer corpus annotation of 7 linguistic layers as a gold set. And we design the validation procedure using the gold set and report the results of a validation procedure for other large-scale corpus annotation of 7 linguistic layers. Furthermore, we present a simplified validation method without a gold set using annotation models learned by the gold set. Based on the validation results, the tendency of annotation across the entire corpus is observed macroscopically, and the corpus annotation validation results are analyzed microscopically to verify the validation methodology to address the case study.

Semantic Noise affects text analytics activities for the domain-specific industries significantly. It impedes the text understanding which holds prime importance in the critical decision making tasks. In this work, we formalize semantic noise as a sequence of terms that do not contribute to the narrative of the text. We look beyond the notion of standard statistically-based stop words and consider the semantics of terms to exclude the semantic noise. We present a novel Semantic Infusion technique to associate meta-data with the categorical corpus text and demonstrate its near-lossless nature. Based on this technique, we propose an unsupervised textpreprocessing framework to filter the semantic noise using the context of the terms. Later we present the evaluation results of the proposed framework using a web forum dataset from the automobile-domain.

We present a parallel wikified data set of parallel texts in eleven language pairs from the educational domain. English sentences are lined up to sentences in eleven other languages (BG, CS, DE, EL, HR, IT, NL, PL, PT, RU, ZH) where names and noun phrases (entities) are manually annotated and linked to their respective Wikipedia pages. For every linked entity in English, the corresponding term or phrase in the target language is also marked and linked to its Wikipedia page in that language. The annotation process was performed via crowdsourcing. In this paper we present the task, annotation process, the encountered difficulties with crowdsourcing for complex annotation, and the data set in more detail. We demonstrate the usage of the data set for Wikification evaluation. This data set is valuable as it constitutes a rich resource consisting of annotated data of English text linked to translations in eleven languages including several languages such as Bulgarian and Greek for which n...

The evolution of the World Wide Web, increase in processing power, and more network bandwidth have contributed to the proliferation of digital multimedia data. Since multimedia data has become a critical resource in many organisations, there is an increasing need to gain efficient access to data, in order to share, extract knowledge, and ultimately use the knowledge to inform business decisions. Existing methods for multimedia semantic understanding are limited to the computable low-level features; which raises the question of how to identify and represent the high-level semantic knowledge in multimedia resources. In order to bridge the semantic gap between multimedia low-level features and highlevel human perception, this thesis seeks to identify the possible contextual dimensions in multimedia resources to help in semantic understanding and organisation. This thesis investigates the use of contextual knowledge to organise and represent the semantics of multimedia data aimed at eff...

The increasing availability of biological data is improving our understanding of diseases and providing new insight into their underlying relationships. Thanks to the improvements on both text mining techniques and computational capacity, the combination of biological data with semantic information obtained from medical publications has proven to be a very promising path. However, the limitations in the access to these data and their lack of structure pose challenges to this approach. In this document we propose the use of Wikipedia -the free online encyclopedia -as a source of accessible textual information for disease understanding research. To check its validity, we compare its performance in the determination of relationships between diseases with that of PubMed, one of the most consulted data sources of medical texts. The obtained results suggest that the information extracted from Wikipedia is as relevant as that obtained from PubMed abstracts (i.e. the free access portion of its articles), although further research is proposed to verify its reliability for medical studies.