Abstract Converting natural language text into structured, logically coherent knowledge graphs (KGs) enhances the ability to retrieve, organize, and analyze vast amounts of information at scale. This paper introduces Text2AMR2FRED, a text-to-KG pipeline that converts multilingual natural language text into logically coherent, interoperable KGs. Designed to support large-scale information retrieval and knowledge extraction, this pipeline addresses key limitations of existing semantic parsers and machine readers, including issues with logical consistency and interoperability. By adhering to Semantic Web standards, Text2AMR2FRED systematically structures text-based information and enhances it through integration with external knowledge sources, delivering enriched, semantically sound KGs ready for diverse applications. We obtain the output KGs by leveraging Abstract Meaning Representation (AMR) as an intermediate semantic parsing formalism, exploiting the progress achieved by text-to-AMR parsers employing pre-trained language models. We produce a manually validated KG s bank created by transforming a dataset of natural language sentences into KGs using Text2AMR2FRED and applying an intrinsic evaluation method that leverages Open Knowledge Extraction motifs.

The development of artificial intelligence systems capable of understanding and reasoning about complex real-world scenarios is a significant challenge. In this work we present a novel approach to enhance and exploit LLM reactive capability to address complex problems and interpret deeply contextual real-world meaning. We introduce a method and a tool for creating a multimodal, knowledge-augmented formal representation of meaning that combines the strengths of large language models with structured semantic representations. Our method begins with an image input, utilizing state-of-the-art large language models to generate a natural language description. This description is then transformed into an Meaning Representation (AMR) graph, which is formalized and enriched with logical design patterns, and layered semantics derived from linguistic and factual knowledge bases. The resulting graph is then fed back into the LLM to be extended with implicit knowledge activated by complex heuristic learning, including semantic implicatures, moral values, embodied cognition, and metaphorical representations. By bridging the gap between unstructured language models and formal semantic structures, our method opens new avenues for tackling intricate problems in natural language understanding and reasoning. • Neurosymbolic approach combining Large Language Models (LLMs) and knowledge graphs. • Extended Knowledge Graphs for implicit knowledge in multiple semantic dimensions. • 11 heuristics to enrich formal representations of meaning from multimodal inputs. • Three-tiered evaluation: logical validation, ontology alignment, human assessment.

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.

Abstract Values, as intended in ethics, determine the shape and validity of moral and social norms, grounding our everyday individual and community behavior on commonsense knowledge. The attempt to untangle human moral and social value-oriented structure of relations requires investigating both the dimension of subjective human perception of the world, and socio-cultural dynamics and multi-agent social interactions. Formalising latent moral content in human interaction is an appealing perspective that would enable a deeper understanding of both social dynamics and individual cognitive and behavioral dimension. To formalize this broad knowledge area, in the context of ValueNet, a modular ontology representing and operationalising moral and social values, we present two modules aiming at representing two main informal theories in literature: (i) the Basic Human Values theory by Shalom Schwartz and (ii) the Moral Foundations Theory by Graham and Haidt. ValueNet is based on reusable Ontology Design Patterns, is aligned to the DOLCE foundational ontology, and is a component of the Framester factual-linguistic knowledge graph.

In this work, we fill the gap in the Semantic Web in the context of Cultural Symbolism. Building upon earlier work in \citesartini_towards_2021, we introduce the Simulation Ontology, an ontology that models the background knowledge of symbolic meanings, developed by combining the concepts taken from the authoritative theory of Simulacra and Simulations of Jean Baudrillard with symbolic structures and content taken from "Symbolism: a Comprehensive Dictionary'' by Steven Olderr. We re-engineered the symbolic knowledge already present in heterogeneous resources by converting it into our ontology schema to create HyperReal, the first knowledge graph completely dedicated to cultural symbolism. A first experiment run on the knowledge graph is presented to show the potential of quantitative research on symbolism.

The Semantic Web Dog Food (SWDF) is the reference linked dataset of the Semantic Web community about papers, people, organisations, and events related to its academic conferences. In this paper we analyse the existing problems of generating, representing and maintaining Linked Data for the SWDF. With this work (i) we provide a refactored and cleaned SWDF dataset; (ii) we use a novel data model which improves the Semantic Web Conference Ontology, adopting best ontology design practices and (iii) we provide an open source workflow to support a healthy growth of the dataset beyond the Semantic Web conferences. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This paper describes the outcome of an e-government project named FOOD, FOod in Open Data, which was carried out in the context of a collaboration between the Institute of Cognitive Sciences and Technologies of the Italian National Research Council, the Italian Ministry of Agriculture (MIPAAF) and the Italian Digital Agency (AgID). In particular, we implemented several ontologies for describing protected names of products (wine, pasta, fish, oil, etc.). In addition, we present the process carried out for producing and publishing a LOD dataset containing data extracted from existing Italian policy documents on such products and compliant with the aforementioned ontologies.

Governing the life cycle of data on the web is a challenging issue for organisations and users. Data is distributed under certain policies that determine what actions are allowed and in which circumstances. Assessing what policies propagate to the output of a process is one crucial problem. Having a description of policies and data flow steps implies a huge number of propagation rules to be specified and computed (number of policies times number of actions). In this paper we provide a method to obtain an abstraction that allows to reduce the number of rules significantly. We use the Datanode ontology, a hierarchical organisation of the possible relations between data objects, to compact the knowledge base to a set of more abstract rules. After giving a definition of Policy Propagation Rule, we show (1) a methodology to abstract policy propagation rules based on an ontology, (2) how effective this methodology is when using the Datanode ontology, (3) how this ontology can evolve in order to better represent the behaviour of policy propagation rules.

This paper describes a novel method for generating and integrating knowledge graphs extracted from multiple natural language sources by FRED, a machine reading tool for generating abstract representations of text documents. This is a key problem in human-robot spoken dialogue interaction, issue which arises from a current research project related to active and healthy ageing using caring service robots where we are involved. The problem is also relevant in many application scenarios requiring the creation and dynamic evolution of a knowledge base, such as automatic news summarisation. Solving this problem requires solving sub-tasks that have only been studied individually, so far. We propose a holistic approach to handle FRED's graphs related to different input texts and output a knowledge graph representing the reconciled knowledge.

Most Knowledge Representation (KR) research follows a topdown approach: i) formalisms are designed on the basis of modelling needs and computational considerations, and ii) tools and applications based on these formalisms are realized and tested on application domains. As a result, there has traditionally been little attention in the KR research community to user issues, in particular to the usability of alternative modelling solutions. When statements about the intuitiveness of different solutions are found in the literature, these tend to reflect an author's epistemological standpoint, rather than any concrete user experience. In this paper we take a bottom-up, user-centric perspective and we report on an empirical study where subjects have been asked to represent temporal information and have been provided with alternative design patterns to do so. The study shows that, depending on their experience and level of expertise in KR, users tend to select different patterns for the given modelling problems. In particular, experts appear to choose on the basis of representation power, while naïve users appear to select on the basis of surface features and perceived user-friendliness. Interestingly, while some patterns are indeed perceived to be more intuitive than others, these considerations seem to apply primarily to less experienced users. Indeed, our findings appear to indicate that experts consider issues of 'intuitiveness' as secondary and, in contrast with naïve users, may be happy to apply patterns, which can be regarded as counter-intuitive, if they provide the right tool for the job.

The SPIM workshop focuses especially on people that are working on the social or semantic Web, machine learning, user modeling, recommender systems, information retrieval, semantic interaction, or their combination. The goal is to bring together researchers and practitioners to initiating discussions on the different requirements and challenges coming with the social and semantic Web for personalized information retrieval systems. The workshop aims at improving the exchange of ideas between the different research communities and practitioners involved in the research on semantic personalized information management.

FrameNet is an important lexical knowledge base featuring cognitive plausibility, and grounded in a large corpus. Besides being actively used by the NLP community, frames are a great source of knowledge patterns once converted into a knowledge representation language. In this paper we present our experience in converting the 1.5 XML version of FrameNet into RDF datasets published on the Linked Open Data cloud, which are interoperable with WordNet and other resources. In the conversion we have used Semion, a new tool that allows a rule-based, customized pipeline from XML to RDF and OWL data. In addition, we introduce a method to select and refactor part of the information related to frames as full-fledged OWL knowledge patterns. This last result has required non-trivial assumptions on how to interpret FrameNet relations as formal knowledge.

We present a Linked Data analysis method which relies on knowledge patterns for constructing a logical architecture of the knowledge in a dataset. This can then be exploited to compare heterogeneous datasets, enhance interoperability between them and make implicit knowledge emerge.

This paper addresses the evaluation of pattern-based ontology design through experiments. An initial method for reuse of content ontology design patterns (Content ODPs) was used by the participants during the experiments. Hypotheses considered include the usefulness of Content ODPs for ontology developers, and we additionally study in what respects they are useful and what open issues remain. The main positive conclusions when using Content ODPs include: ontology developers perceived them as useful, ontology quality is improved, coverage of the task increases, usability is improved, and common modelling mistakes can be avoided.

One of the main strengths of the Web is that it allows any party of its global community to share information with any other party. This goal has been achieved by making use of a unique and uniform mechanism of identification, the uniform resource identifiers (URI). Although URIs succeed when used for retrieving resources on the Web, their suitability for identifying any kind of thing, for example, resources that are not on the Web, is not guaranteed. In this article we investigate the meaning of the identity of a Web resource, and how the current situation, as well as existing and possible future improvements, can be modeled and implemented on the Web. In particular, we propose an ontology, IRE, that provides a formal way to model both the problem and the solution spaces. IRE describes the concept of resource from the viewpoint of the Web, by reusing an ontology of information objects, built on top of DOLCE+ and its extensions. In particular, we formalize the concept of Web resource, as distinguished from the concept of a generic entity, and how those and other concepts are related, for example, by different proxy for relations. Based on the analysis formalized in IRE, we propose a formal pattern for modeling and comparing different solutions to the problems of the identity of resources.

Clarity in semantics and a rich formalization of this semantics are important requirements for ontologies designed to be deployed in large-scale, open, distributed systems such as the envisioned Semantic Web This is especially important for the description of Web Services, which should enable complex tasks involving multiple agents. As one of the first initiatives of the Semantic Webcommunity for describing Web Services, OWL-S attracts a lot of interest even though it is still under development. We identify problematic aspects of OWL-S and suggest enhancements through alignment to a foundational ontology. Another contribution of ourwork is the Core Ontology of Services that tries to fill the epistemological gap between the foundational ontology and OWL-S. It can be reused to align other Web Service description languages as well. Finally, we demonstrate the applicability of our work byaligning OWL-S' standard example called CongoBuy.