Journal of Web Semantics最新文献

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MobiRDF: A cloud-based collaborative editing service for mobile RDF data sharing

IF 2.1 3区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Web Semantics

Pub Date : 2025-02-17 DOI: 10.1016/j.websem.2025.100864

Nadir Guetmi , Abdessamad Imine , Moulay Driss Mechaoui

In this paper, we present MobiRDF, a novel cloud-based approach designed for the efficient and scalable management of RDF data, enabling real-time sharing and editing. MobiRDF offers two main services: (i) Partial Replication of RDF Graphs: This service facilitates the selective replication of RDF graphs on mobile devices, addressing their inherent resource limitations. Our partial graph selector allows using only the useful data requested by the user from the RDF graph instead of storing the entire RDF graph, which enables efficient data storage and retrieval. (ii) Collaboration Protocol: This protocol provides synchronization mechanisms for collaborative work in a fully decentralized manner. It uses commutativity-based consistency model to maintain the consistency of the shared RDF graph, ensuring seamless collaboration among users. The heavier computational tasks, such as dynamic group management, synchronization merging, and reasoning processes, are managed in the Cloud, optimizing the performance of resource-constrained mobile devices. The key novelty of MobiRDF is its ability to ensure both syntactic and semantic consistency of shared RDF data, through reasoning processes using the Closed-World Assumption (CWA) for inferring new triples. Experimental evaluations show that MobiRDF is efficient in terms of network bandwidth and energy consumption, validating its effectiveness in real-world scenarios.

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引用次数: 0

Two ontology design patterns in the domain of collections

IF 2.1 3区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Web Semantics

Pub Date : 2025-02-15 DOI: 10.1016/j.websem.2025.100863

Idoia Berges, Arantza Illarramendi

Collections are objects used to arrange, into a single unit, multiple data items that form a natural group. Different types of collections exist, due to different constraints based on whether or not they impose an order on their elements and whether or not they allow repetition of elements. Any of them are easily found in several domains of our everyday life. For instance, a deck of cards, the prime divisors of a number or the teams that compete in a championship can be seen as a collection. Thus, an effective modeling of collections is a recurring issue in information management.

In the ontology design field, recurring modeling problems can be addressed by the use of Ontology Design Patterns (ODPs). In the case of collections, ODPs have been proposed for representing sequences, lists, sets and bags. However, none of these patterns are completely adequate for representing collections of ordered elements without repetition. In this paper we present an ODP for representing that notion, which we have named Permutation. Moreover, another ODP named ListOfPermutations is also introduced, which allows to represent how the order of a Permutation varies along time. Because not all constraints required by these ODPs can be represented in OWL 2, SHACL shapes have been used in their definitions.

集合是一种对象，用于将构成自然组的多个数据项排列成一个单元。不同类型的集合存在不同的约束条件，这些约束条件基于集合是否对其元素施加顺序以及是否允许元素重复。在我们日常生活的多个领域中，都能很容易地发现这些集合。例如，一副扑克牌、一个数字的素除数或参加锦标赛的队伍都可以看作是一个集合。因此，对集合进行有效建模是信息管理中经常出现的问题。在本体设计领域，经常出现的建模问题可以通过使用本体设计模式（ODPs）来解决。就集合而言，已经提出了用于表示序列、列表、集合和包的 ODPs。然而，这些模式都不能完全胜任表示无重复的有序元素集合。在本文中，我们提出了一种用于表示这一概念的 ODP，并将其命名为 Permutation。此外，我们还引入了另一种名为 ListOfPermutations 的 ODP，它可以表示 Permutation 的顺序是如何随时间变化的。由于这些 ODP 所需的所有约束并非都能用 OWL 2 表示，因此在定义时使用了 SHACL 形状。

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引用次数: 0

Accelerating knowledge graph and ontology engineering with large language models

IF 2.1 3区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Web Semantics

Pub Date : 2025-02-06 DOI: 10.1016/j.websem.2025.100862

Cogan Shimizu , Pascal Hitzler

Large Language Models bear the promise of significant acceleration of key Knowledge Graph and Ontology Engineering tasks, including ontology modeling, extension, modification, population, alignment, as well as entity disambiguation. We lay out LLM-based Knowledge Graph and Ontology Engineering as a new and coming area of research, and argue that modular approaches to ontologies will be of central importance.

引用次数: 0

Logic Augmented Generation

IF 2.1 3区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Web Semantics

Pub Date : 2025-01-18 DOI: 10.1016/j.websem.2024.100859

Aldo Gangemi , Andrea Giovanni Nuzzolese

Semantic Knowledge Graphs (SKG) face challenges with scalability, flexibility, contextual understanding, and handling unstructured or ambiguous information. However, they offer formal and structured knowledge enabling highly interpretable and reliable results by means of reasoning and querying. Large Language Models (LLMs) may overcome those limitations, making them suitable in open-ended tasks and unstructured environments. Nevertheless, LLMs are hardly interpretable and often unreliable. To take the best out of LLMs and SKGs, we envision Logic Augmented Generation (LAG) to combine the benefits of the two worlds. LAG uses LLMs as Reactive Continuous Knowledge Graphs that can generate potentially infinite relations and tacit knowledge on-demand. LAG uses SKGs to inject a discrete heuristic dimension with clear logical and factual boundaries. We exemplify LAG in two tasks of collective intelligence, i.e., medical diagnostics and climate projections. Understanding the properties and limitations of LAG, which are still mostly unknown, is of utmost importance for enabling a variety of tasks involving tacit knowledge in order to provide interpretable and effective results.

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引用次数: 0

Knowledge Graphs as a source of trust for LLM-powered enterprise question answering

IF 2.1 3区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Web Semantics

Pub Date : 2025-01-15 DOI: 10.1016/j.websem.2024.100858

Juan Sequeda, Dean Allemang, Bryon Jacob

Generative AI provides an innovative and exciting way to manage knowledge and data at any scale; for small projects, at the enterprise level, and even at a world wide web scale. It is tempting to think that Generative AI has made other knowledge-based technologies obsolete; that anything we wanted to do with knowledge-based systems, Knowledge Graphs or even expert systems can instead be done with Generative AI. Our position is counter to that conclusion.

Our practical experience on implementing enterprise question answering systems using Generative AI has shown that Knowledge Graphs support this infrastructure in multiple ways: they provide a formal framework to evaluate the validity of a query generated by an LLM, serve as a foundation for explaining results, and offer access to governed and trusted data. In this position paper, we share our experience, present industry needs, and outline the opportunities for future research contributions.

引用次数: 0

The ESW of Wikidata: Exploratory search workflows on Knowledge Graphs

IF 2.1 3区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Web Semantics

Pub Date : 2025-01-04 DOI: 10.1016/j.websem.2024.100860

Matteo Lissandrini , Gianmarco Prando , Gianmaria Silvello

Exploratory search on Knowledge Graphs (KGs) arises when a user needs to understand and extract insights from an unfamiliar KG. In these exploratory sessions, the users issue a series of queries to identify relevant portions of the KG that can answer their questions, with each query answer informing the formulation of the next query. Despite the widespread adoption of KGs, the needs of current KG exploration use cases are not well understood. This work presents the “Exploratory Search Workflows” (ESW) collection focusing on real-world exploration sessions of an open-domain KG, Wikidata, conducted by 57 M.Sc. Computer Engineering students in two advanced Graph Database course editions. This resource includes 234 real exploratory workflows, each containing an average of 45 SPARQL queries and reference workflows that serve as gold-standard solutions to the proposed tasks. The ESW collection is also available as an RDF graph and accessible via a public SPARQL endpoint. It allows for analysis of real user sessions, understanding query evolution and complexity, and serves as the first query benchmark for KG management systems for exploratory search.

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引用次数: 0

Knowledge graph based entity selection framework for ad-hoc retrieval

IF 2.1 3区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Web Semantics

Pub Date : 2025-01-01 DOI: 10.1016/j.websem.2024.100848

Pankaj Singh , Plaban Kumar Bhowmick

Recent entity-based retrieval models utilizing knowledge bases have shown significant improvement in ad-hoc retrieval. However, a lack of coherence between candidate entities can lead to query intent drift at retrieval time. To address this issue, we present an entity selection algorithm that utilizes a graph clustering framework to discover the semantics between entities and encompass the query with highly coherent entities accumulated from different resources, including knowledge bases, and pseudo-relevance feedback documents. Through this work, we propose: (1) An entity acquisition strategy to systematically acquire coherent entities for query expansion. (2) We propose a graph representation of entities to capture the coherence between entities where nodes correspond to the entities and edges represent semantic relatedness between entities. (3) We propose two different entity ranking approaches to select candidate entities based on the coherence with query entities and other coherent entities. A set of experiments on five TREC collections: ClueWeb09B, ClueWeb12B, Robust04, GOV2, and MS-Marco dataset under document retrieval task were conducted to verify the proposed algorithm’s performance. The reported results indicated that the proposed methodology outperforms existing state-of-the-art retrieval approaches in terms of MAP, NDCG, and P@20. The code and relevant data are available in https://github.com/pankajkashyap65/KnowledgeGraph.

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引用次数: 0

Leveraging Knowledge Graphs for AI System Auditing and Transparency

IF 2.1 3区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Web Semantics

Pub Date : 2025-01-01 DOI: 10.1016/j.websem.2024.100849

Laura Waltersdorfer , Marta Sabou

Auditing complex Artificial Intelligence (AI) systems is gaining importance in light of new regulations and is particularly challenging in terms of system complexity, knowledge integration, and differing transparency needs. Current AI auditing tools however, lack semantic context, resulting in difficulties for auditors in effectively collecting and integrating, but also for analysing and querying audit data. In this position paper, we explore how Knowledge Graphs (KGs) can address these challenges by offering a structured and integrative approach to collecting and transforming audit traces. This work discusses the current limitations in both AI auditing processes and tools. Furthermore, we examine how KGs can play a transformative role in overcoming these obstacles to achieve improved auditability and transparency of AI systems.

引用次数: 0

Process Knowledge Graphs (PKG): Towards unpacking and repacking AI applications

IF 2.1 3区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Web Semantics

Pub Date : 2025-01-01 DOI: 10.1016/j.websem.2024.100846

Enrico Daga

In the past years, a new generation of systems has emerged, which apply recent advances in generative Artificial Intelligence (AI) in combination with traditional technologies. Specifically, generative AI is being delegated tasks in natural language or vision understanding within complex hybrid architectures that also include databases, procedural code, and interfaces. Process Knowledge Graphs (PKG) have a long-standing tradition within symbolic AI research. On the one hand, PKGs can play an important role in describing complex, hybrid applications, thus opening the way for addressing fundamental challenges such as explaining and documenting such systems (unpacking). On the other hand, by organising complex processes in simpler building blocks, PKGs can potentially increase accuracy and control over such systems (repacking). In this position paper, we discuss opportunities and challenges of PGRs and their potential role towards a more robust and principled design of AI applications.

引用次数: 0

Procedural knowledge management in Industry 5.0: Challenges and opportunities for knowledge graphs

IF 2.1 3区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Web Semantics

Pub Date : 2025-01-01 DOI: 10.1016/j.websem.2024.100850

Irene Celino, Valentina Anita Carriero, Antonia Azzini, Ilaria Baroni, Mario Scrocca

With digital transformation, industrial companies today are facing the challenges to change and innovate their business, by leveraging digital technologies and tools to support their processes and their operations. One of their main challenges is the management of the company knowledge, especially when tacit and owned by industry workers. In this paper, we illustrate how knowledge graphs can be the turning point to allow industry workers digitize and exploit the knowledge about the “what”, the “how” and the “why” of their everyday activities.

In particular, we focus on the “how” by illustrating the challenges related to procedural knowledge management, i.e., the knowledge about processes and workflows that employees need to follow, and comply with, to correctly execute their tasks, in order to improve efficiency and effectiveness, to reduce risks and human errors and to optimize operations. We also explain the relationship in this context between knowledge graphs and sub-symbolic AI approaches.

引用次数: 0

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