Knowledge Organization Systems (KOS) in the Semantic Web: A Multi-Dimensional Review

Since the Simple Knowledge Organization System (SKOS) specification and its SKOS eXtension for Labels (SKOS-XL) became formal W3C recommendations in 2009 a significant number of conventional knowledge organization systems (KOS) (including thesauri, classification schemes, name authorities, and lists of codes and terms, produced before the arrival of the ontology-wave) have made their journeys to join the Semantic Web mainstream. This paper uses "LOD KOS" as an umbrella term to refer to all of the value vocabularies and lightweight ontologies within the Semantic Web framework. The paper provides an overview of what the LOD KOS movement has brought to various communities and users. These are not limited to the colonies of the value vocabulary constructors and providers, nor the catalogers and indexers who have a long history of applying the vocabularies to their products. The LOD dataset producers and LOD service providers, the information architects and interface designers, and researchers in sciences and humanities, are also direct beneficiaries of LOD KOS. The paper examines a set of the collected cases (experimental or in real applications) and aims to find the usages of LOD KOS in order to share the practices and ideas among communities and users. Through the viewpoints of a number of different user groups, the functions of LOD KOS are examined from multiple dimensions. This paper focuses on the LOD dataset producers, vocabulary producers, and researchers (as end-users of KOS).Comment: 31 pages, 12 figures, accepted paper in International Journal on Digital Librarie

Understanding and Managing Medical Data and Knowledge Dynamics

For several decades, Artificial Intelligence is concerned with the definition of the concept of knowledge in order to exploit it for various purposes such as information retrieval, decision support or semantic interoperability. This is mainly done thanks to knowledge representation models such as ontologies aiming at representing and organising the concepts of a given domain. However, the evolution of these models and its impact on depending artefacts remain open research problems.The biomedical domain is specific in a sense that its associated knowledge is complex and constantly evolving as underlined by the ever increasing number of published scientific communications. This is why I have focus on this domain and its specificities have been the various lines I have followed during my research work.Three main thematics have focused my efforts over the past years:1- Biomedical knowledge representation2- The management of the evolution of biomedical knowledge representation models 3- The validation of biomedical knowledge representation models These topics are more largely detailed in this manuscriptDepuis plusieurs décennies, le domaine de l’Intelligence Artificielle s’intéresse à définir la notion de connaissance afin de l’exploiter à des fins diverses dans plusieurs cadres d’application tels que la recherche d’information, l’aide à la décision ou encore l’interopérabilité sémantique. Ceci est en partie réalisé grâce à l’utilisation de modèles de représentation des connaissances tels que les ontologies permettant la spécification d’une conceptualisation. Cependant, les aspects liés à l'évolution des connaissances et des modèles qui leur sont associés restent largement inexplorés et demeurent des problèmes de recherche ouverts.Le domaine biomédical est un domaine très riche dans la mesure où les connaissances qu’il intègre sont complexes et en perpétuelle évolution comme le démontre le nombre sans cesse croissant de communications scientifiques publiées au quotidien. C’est pour ces raisons qu’il a suscité mon intérêt et ses spécificités ont constitué la ligne directrice de mes activités de recherche.Trois grandes thématiques ont focalisé mes efforts au cours de ces dernières années et ont concentré la majeure partie de mes contributions scientifiques et collaborations dans ce domaine particulier.1- La représentation des connaissances en santé.2- La gestion de l'évolution des modèles de représentation des connaissances biomédicales.3- La validation des modèles de représentation des connaissances biomédicales.Mes travaux autour de ces trois thématiques sont détaillés dans ce manuscrit

Requirements for Implementing Mappings Adaptation Systems

International audienceOntologies, or more generally speaking, Knowledge Organization Systems (KOS) have been developed to support the correct interpretation of shared data in collaborative applications. The quantity and the heterogeneity of domain knowledge often require several KOS to describe their content. In order to assure unambiguous interpretation, overlapped concepts of different, but domain-related KOS are semantically connected via mappings. However, in various domains, KOS periodically evolve creating the necessity of reviewing the validity of associated mappings. The size of KOS remains a barrier for a manual review of mappings, and rather requires the support of (semi-) automatic solutions. This article describes our experiences in understanding how KOS evolution affects mappings. We present our lessons learned from various empirical experiments, and we derive primary elements and requirements for improving the automation of mapping maintenance

Resource discovery in heterogeneous digital content environments

The concept of 'resource discovery' is central to our understanding of how users explore, navigate, locate and retrieve information resources. This submission for a PhD by Published Works examines a series of 11 related works which explore topics pertaining to resource discovery, each demonstrating heterogeneity in their digital discovery context. The assembled works are prefaced by nine chapters which seek to review and critically analyse the contribution of each work, as well as provide contextualization within the wider body of research literature. A series of conceptual sub-themes is used to organize and structure the works and the accompanying critical commentary. The thesis first begins by examining issues in distributed discovery contexts by studying collection level metadata (CLM), its application in 'information landscaping' techniques, and its relationship to the efficacy of federated item-level search tools. This research narrative continues but expands in the later works and commentary to consider the application of Knowledge Organization Systems (KOS), particularly within Semantic Web and machine interface contexts, with investigations of semantically aware terminology services in distributed discovery. The necessary modelling of data structures to support resource discovery - and its associated functionalities within digital libraries and repositories - is then considered within the novel context of technology-supported curriculum design repositories, where questions of human-computer interaction (HCI) are also examined. The final works studied as part of the thesis are those which investigate and evaluate the efficacy of open repositories in exposing knowledge commons to resource discovery via web search agents. Through the analysis of the collected works it is possible to identify a unifying theory of resource discovery, with the proposed concept of (meta)data alignment described and presented with a visual model. This analysis assists in the identification of a number of research topics worthy of further research; but it also highlights an incremental transition by the present author, from using research to inform the development of technologies designed to support or facilitate resource discovery, particularly at a 'meta' level, to the application of specific technologies to address resource discovery issues in a local context. Despite this variation the research narrative has remained focussed on topics surrounding resource discovery in heterogeneous digital content environments and is noted as having generated a coherent body of work. Separate chapters are used to consider the methodological approaches adopted in each work and the contribution made to research knowledge and professional practice.The concept of 'resource discovery' is central to our understanding of how users explore, navigate, locate and retrieve information resources. This submission for a PhD by Published Works examines a series of 11 related works which explore topics pertaining to resource discovery, each demonstrating heterogeneity in their digital discovery context. The assembled works are prefaced by nine chapters which seek to review and critically analyse the contribution of each work, as well as provide contextualization within the wider body of research literature. A series of conceptual sub-themes is used to organize and structure the works and the accompanying critical commentary. The thesis first begins by examining issues in distributed discovery contexts by studying collection level metadata (CLM), its application in 'information landscaping' techniques, and its relationship to the efficacy of federated item-level search tools. This research narrative continues but expands in the later works and commentary to consider the application of Knowledge Organization Systems (KOS), particularly within Semantic Web and machine interface contexts, with investigations of semantically aware terminology services in distributed discovery. The necessary modelling of data structures to support resource discovery - and its associated functionalities within digital libraries and repositories - is then considered within the novel context of technology-supported curriculum design repositories, where questions of human-computer interaction (HCI) are also examined. The final works studied as part of the thesis are those which investigate and evaluate the efficacy of open repositories in exposing knowledge commons to resource discovery via web search agents. Through the analysis of the collected works it is possible to identify a unifying theory of resource discovery, with the proposed concept of (meta)data alignment described and presented with a visual model. This analysis assists in the identification of a number of research topics worthy of further research; but it also highlights an incremental transition by the present author, from using research to inform the development of technologies designed to support or facilitate resource discovery, particularly at a 'meta' level, to the application of specific technologies to address resource discovery issues in a local context. Despite this variation the research narrative has remained focussed on topics surrounding resource discovery in heterogeneous digital content environments and is noted as having generated a coherent body of work. Separate chapters are used to consider the methodological approaches adopted in each work and the contribution made to research knowledge and professional practice

U-sphere: strengthening scalable flat-name routing for decentralized networks

Supporting decentralized peer-to-peer communication between users is crucial for maintaining privacy and control over personal data. State-of-the-art protocols mostly rely on distributed hash tables (DHTs) in order to enable user-to-user communication. They are thus unable to provide transport address privacy and guaranteed low path stretch while ensuring sub-linear routing state together with tolerance of insider adversaries. In this paper we present U-Sphere, a novel location-independent routing protocol that is tolerant to Sybil adversaries and achieves low O (1) path stretch while maintaining View the MathML source per-node state. Departing from DHT designs, we use a landmark-based construction with node color groupings to aid flat name resolution while maintaining the stretch and state bounds. We completely remove the need for landmark-based location directories and build a name-record dissemination overlay that is able to better tolerate adversarial attacks under the assumption of social trust links established between nodes. We use large-scale emulation on both synthetic and actual network topologies to show that the protocol successfully achieves the scalability goals in addition to mitigating the impact of adversarial attacks

U-sphere: strengthening scalable flat-name routing for decentralized networks

Supporting decentralized peer-to-peer communication between users is crucial for maintaining privacy and control over personal data. State-of-the-art protocols mostly rely on distributed hash tables (DHTs) in order to enable user-to-user communication. They are thus unable to provide transport address privacy and guaranteed low path stretch while ensuring sub-linear routing state together with tolerance of insider adversaries. In this paper we present U-Sphere, a novel location-independent routing protocol that is tolerant to Sybil adversaries and achieves low O (1) path stretch while maintaining View the MathML source per-node state. Departing from DHT designs, we use a landmark-based construction with node color groupings to aid flat name resolution while maintaining the stretch and state bounds. We completely remove the need for landmark-based location directories and build a name-record dissemination overlay that is able to better tolerate adversarial attacks under the assumption of social trust links established between nodes. We use large-scale emulation on both synthetic and actual network topologies to show that the protocol successfully achieves the scalability goals in addition to mitigating the impact of adversarial attacks

Integration of distributed terminology resources to facilitate subject cross-browsing for library portal systems

With the increase in the number of distributed library information resources, users may have to interact with different user interfaces, learn to switch their mental models between these interfaces, and familiarise themselves with controlled vocabularies used by different resources. For this reason, library professionals have developed library portals to integrate these distributed information resources, and assist end-users in cross-accessing distributed resources via a single access point in their own library. There are two important subject-based services that a library portal system might be able to provide. The first is a federated search service, which refers to a process where a user can input a query to cross-search a number of information resources. The second is a subject cross-browsing service, which can offer a knowledge navigation tree to link subject schemes used by distributed resources. However, the development of subject cross-searching and browsing services has been impeded by the heterogeneity of different KOS (Knowledge Organisation System) used by different information resources. Due to the lack of mappings between different KOS, it is impossible to offer a subject cross-browsing service for a library portal system. [Continues.

ACAI: Protecting Accelerator Execution with Arm Confidential Computing Architecture

Trusted execution environments in several existing and upcoming CPUs demonstrate the success of confidential computing, with the caveat that tenants cannot securely use accelerators such as GPUs and FPGAs. In this paper, we reconsider the Arm Confidential Computing Architecture (CCA) design, an upcoming TEE feature in Armv9-A, to address this gap. We observe that CCA offers the right abstraction and mechanisms to allow confidential VMs to use accelerators as a first-class abstraction. We build ACAI, a CCA-based solution, with a principled approach of extending CCA security invariants to device-side access to address several critical security gaps. Our experimental results on GPU and FPGA demonstrate the feasibility of ACAI while maintaining security guarantees.Comment: Extended version of the Usenix Security 2024 pape

A model for information retrieval driven by conceptual spaces

A retrieval model describes the transformation of a query into a set of documents. The question is: what drives this transformation? For semantic information retrieval type of models this transformation is driven by the content and structure of the semantic models. In this case, Knowledge Organization Systems (KOSs) are the semantic models that encode the meaning employed for monolingual and cross-language retrieval. The focus of this research is the relationship between these meanings’ representations and their role and potential in augmenting existing retrieval models effectiveness. The proposed approach is unique in explicitly interpreting a semantic reference as a pointer to a concept in the semantic model that activates all its linked neighboring concepts. It is in fact the formalization of the information retrieval model and the integration of knowledge resources from the Linguistic Linked Open Data cloud that is distinctive from other approaches. The preprocessing of the semantic model using Formal Concept Analysis enables the extraction of conceptual spaces (formal contexts)that are based on sub-graphs from the original structure of the semantic model. The types of conceptual spaces built in this case are limited by the KOSs structural relations relevant to retrieval: exact match, broader, narrower, and related. They capture the definitional and relational aspects of the concepts in the semantic model. Also, each formal context is assigned an operational role in the flow of processes of the retrieval system enabling a clear path towards the implementations of monolingual and cross-lingual systems. By following this model’s theoretical description in constructing a retrieval system, evaluation results have shown statistically significant results in both monolingual and bilingual settings when no methods for query expansion were used. The test suite was run on the Cross-Language Evaluation Forum Domain Specific 2004-2006 collection with additional extensions to match the specifics of this model