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.

In this paper, we present an evaluation of two different approaches to the free-form Question Answering (QA) task. The main difference between the two approaches is that one is based on latent representations of knowledge, and the other uses explicit knowledge representation. For the evaluation, we developed DynaKnowledge, a new benchmark composed of questions concerning Wikipedia low-frequency entities. We wanted to ensure, on the one hand, that the questions are answerable and, on the other, that the models can provide information about very specific facts. The evaluation that we conducted highlights that the proposed benchmark is particularly challenging. The best model answers correctly only on 50% of the questions. Analysing the results, we also found that ChatGPT shows low reliance on low-frequency entity questions, manifesting a popularity bias. On the other hand, a simpler model based on explicit knowledge is less affected by this bias. With this paper, we want to provide a living benchmark for open-form QA to test knowledge and latent representation models on a dynamic benchmark.

The process of developing ontologies – a formal, explicit specification of a shared conceptualisation – is addressed by well-known methodologies. As for any engineering development, its fundamental basis is the collection of requirements, which includes the elicitation of competency questions. Competency questions are defined through interacting with domain and application experts or by investigating existing datasets that may be used to populate the ontology i.e. its knowledge graph. The rise in popularity and accessibility of knowledge graphs provides an opportunity to support this phase with automatic tools. In this work, we explore the possibility of extracting competency questions from a knowledge graph. This reverses the traditional workflow in which knowledge graphs are built from ontologies, which in turn are engineered from competency questions. We describe in detail RevOnt, an approach that extracts and abstracts triples from a knowledge graph, generates questions based on triple verbalisations, and filters the resulting questions to yield a meaningful set of competency questions; the WDV dataset. This approach is implemented utilising the Wikidata knowledge graph as a use case, and contributes a set of core competency questions from 20 domains present in the WDV dataset. To evaluate RevOnt, we contribute a new dataset of manually-annotated high-quality competency questions, and compare the extracted competency questions by calculating their BLEU score against the human references. The results for the abstraction and question generation components of the approach show good to high quality. Meanwhile, the accuracy of the filtering component is above 86%, which is comparable to the state-of-the-art classifications.

This paper describes a pattern to formalise context-free grammars in OWL and its use for sequence classification. The proposed approach is compared to existing methods in terms of computational complexity as well as pragmatic applicability, with examples in the music domain.

Abstract In the music domain, several ontologies have been proposed to annotate musical data, in both symbolic and audio form, and generate semantically rich Music Knowledge Graphs. However, current models lack interoperability and are insufficient for representing music history and the cultural heritage context in which it was generated; risking the propagation of recency and cultural biases to downstream applications. In this article, we propose the Polifonia Ontology Network (PON) for music cultural heritage, centred around four modules: Music Meta (metadata), Representation (content), Source (provenance) and Instrument (cultural objects). We design PON with a strong accent on cultural stakeholder requirements and competency questions (CQs), contributing an NLP-based toolkit to support knowledge engineers in generating, validating, and analysing them; and a novel, high-quality CQ dataset produced as a result. We show current and future use of these resources by internal project pilots, early adopters in the music industry, and opportunities for the Semantic Web and Music Information Retrieval communities.

Computationally creative systems for music have recently achieved impressive results, fuelled by progress in generative machine learning. However, black-box approaches have raised fundamental concerns for ethics, accountability, explainability, and musical plausibility. To enable trustworthy machine creativity, we introduce the Harmonic Memory, a Knowledge Graph (KG) of harmonic patterns extracted from a large and heterogeneous musical corpus. By leveraging a cognitive model of tonal harmony, chord progressions are segmented into meaningful structures, and patterns emerge from their comparison via harmonic similarity. Akin to a music memory, the KG holds temporal connections between consecutive patterns, as well as salient similarity relationships. After demonstrating the validity of our choices, we provide examples of how this design enables novel pathways for combinational creativity. The memory provides a fully accountable and explainable framework to inspire and support creative professionals – allowing for the discovery of progressions consistent with given criteria, the recomposition of harmonic sections, but also the co-creation of new progressions.

Visual question answering has recently been settled as a fundamental multi-modal reasoning task of artificial intelligence that allows users to get information about visual content by asking questions in natural language. In the cultural heritage domain, this task can contribute to assisting visitors in museums and cultural sites, thus increasing engagement. However, the development of visual question answering models for cultural heritage is prevented by the lack of suitable large-scale datasets. To meet this demand, we built a large-scale heterogeneous and multilingual (Italian and English) dataset for cultural heritage that comprises approximately 500K Italian cultural assets and 6.5M question-answer pairs. We propose a novel formulation of the task that requires reasoning over both the visual content and an associated natural language description, and present baselines for this task. Results show that the current state of the art is reasonably effective but still far from satisfactory; therefore, further research in this area is recommended. Nonetheless, we also present a holistic baseline to address visual and contextual questions and foster future research on the topic.

The success of the interaction between the robotics community and the users of these services is an aspect of considerable importance in the drafting of the development plan of any technology. This aspect becomes even more relevant when dealing with sensitive services and issues such as those related to interaction with specific subgroups of any population. Over the years, there have been few successes in integrating and proposing technologies related to deafness and sign language. Instead, in this paper, we propose an account of successful interaction between a signatory robot and the Italian deaf community, which occurred during the Smart City Robotics Challenge (SciRoc) 2021 competition1. Thanks to the use of a participatory design and the involvement of experts belonging to the deaf community from the early stages of the project, it was possible to create a technology that has achieved significant results in terms of acceptance by the community itself and could lead to significant results in the technology development as well.

Understanding large ontologies is still an issue, and has an impact on many ontology engineering tasks. We describe a novel method for identifying and extracting conceptual components from domain ontologies, which are used to understand and compare them. The method is applied to two corpora of ontologies in the Cultural Heritage and Conference domain, respectively. The results, which show good quality, are evaluated by manual inspection and by correlation with datasets and tool performance from the ontology alignment evaluation initiative.

This paper presents Amnestic Forgery, an ontology for metaphor semantics, based on MetaNet, which is inspired by the theory of Conceptual Metaphor. Amnestic Forgery reuses and extends the Framester schema, as an ideal ontology design framework to deal with both semiotic and referential aspects of frame and role mappings. The description of the resource is supplied by a discussion of its applications, with examples taken from metaphor generation, and the referential problems of metaphoric mappings. Both schema and data are available from the Framester SPARQL endpoint.

The Web and its Semantic extension (i.e. Linked Open Data) contain open global-scale knowledge and make it available to potentially intelligent machines that want to benefit from it. Nevertheless, most of Linked Open Data lack ontological distinctions and have sparse axiomatisation. For example, distinctions such as whether an entity is inherently a class or an individual, or whether it is a physical object or not, are hardly expressed in the data, although they have been largely studied and formalised by foundational ontologies (e.g. DOLCE, SUMO). These distinctions belong to common sense too, which is relevant for many artificial intelligence tasks such as natural language understanding, scene recognition, and the like. There is a gap between foundational ontologies, that often formalise or are inspired by pre-existing philosophical theories and are developed with a top-down approach, and Linked Open Data that mostly derive from existing databases or crowd-based effort (e.g. DBpedia, Wikidata). We investigate whether machines can learn foundational distinctions over Linked Open Data entities, and if they match common sense. We want to answer questions such as “does the DBpedia entity for dog refer to a class or to an instance?”. We report on a set of experiments based on machine learning and crowdsourcing that show promising results.

The need of handling semantic heterogeneity of resources is a key problem of the Semantic Web. State of the art techniques for ontology matching are the key technology for addressing this issue. However, they only partially exploit the natural lan- guage descriptions of ontology entities and they are mostly unable to find correspondences between entities having dif- ferent logical types (e.g. mapping properties to classes). We introduce a novel approach aimed at finding correspondences between ontology entities according to the intensional mean- ing of their models, hence abstracting from their logical types. Lexical linked open data and frame semantics play a crucial role in this proposal. We argue that this approach may lead to a step ahead in the state of the art of ontology matching, and positively affect related applications such as question an- swering and knowledge reconciliation.

Adjectives carry a lot of discourse semantics, often being the core elements to understand the literal, emotional, cultural, and metaphoric meaning of a sentence. Their formal semantics has been investigated specially in lexical semantics and formal linguistics, with some contributions from formal ontology and the semantic web. However, no standard formal treatment of adjectives is available yet, which is capable to address the concerns of both theoretical and computational natural language understanding. In this paper we summarize the existing approaches, present some alternative solutions to approximate a rigorous but pragmatic representation, and describe an implementation of a lightweight adjective ontology as a core resource in FRED, a state-of-the-art open knowledge extraction tool.

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.

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.

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.

Recent innovations in the Social and Semantic Web fields have resulted in large amounts of data created, published and consumed by users of the Web. This vast amount of data exists in a variety of formats, from the traditional ones such as text, image, video to the more recent additions such as streams of status information from Twitter and Facebook. The ability to easily integrate such vast amounts of data raises significant and exciting research challenges, not least of which how to provide effective access to and navigation across heterogeneous data sources on different platforms (e.g. computers, mobile devices, set-top boxes). Building on the success of the VISSW2009 workshop, the IUI2010 workshop on Visual Interfaces to the Social and Semantic Web aims to bring together researchers and practitioners from different fields to discuss the latest research results and challenges in designing, implementing, and evaluating intelligent interfaces supporting access, navigation and publishing of different types of contents on the Social and Semantic Web. This paper outlines the context of the workshop and provides an overview of the research to be presented at the event.

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.