The paradigm of neuro-symbolic Artificial Intelligence is receiving an increasing attention in the last years to improve the results of intelligent systems by combining symbolic and subsymbolic methods. For example, existing Large Language Models (LLMs) could be enriched by taking into account background knowledge encoded using semantic technologies, such as Linguistic Linked Data (LLD). In this paper, we claim that LLD can aid Large Language Models by providing the necessary information to compute the number of poetic syllables, which would help LLMs to correctly generate poems with a valid metric. To do so, we propose an encoding for syllabic structure based on an extension of RDF vocabularies widely used in the field: POSTDATA and OntoLex-Lemon.

Extracting interesting patterns from data is the main objective of Data Mining. In this context, Frequent Itemset Mining has shown its usefulness in providing insights from transactional databases, which, in turn, can be used to gain insights about the structure of Knowledge Graphs. While there have been a lot of advances in the field, due to the NP-hard nature of the problem, the main approaches still struggle when they are faced with large databases with large and sparse vocabularies, such as the ones obtained from graph propositionalizations. There have been efforts to propose parallel algorithms, but, so far, the goal has not been to tackle this source of complexity (i.e., vocabulary size), thus, in this paper, we propose to parallelize frequent itemset mining algorithms by partitioning the database horizontally (i.e., transaction-wise) while not neglecting all the possible vertical information (i.e., item-wise). Instead of relying on pure item co-appearance metrics, we advocate for the adoption of a different approach: modeling databases as documents, where each transaction is a sentence, and each item a word. In this way, we can apply recent language modeling techniques (i.e., word embeddings) to obtain a continuous representation of the database, clusterize it in different partitions, and apply any mining algorithm to them. We show how our proposal leads to informed partitions with a reduced vocabulary size and a reduced entropy (i.e., disorder). This enhances the scalability, allowing us to speed up mining even in very large databases with sparse vocabularies. We have carried out a thorough experimental evaluation over both synthetic and real datasets showing the benefits of our proposal.

Public and private organizations produce and store huge amounts of documents which contain information about their domains in non-structured formats. Although from the final user’s point of view we can rely on different retrieval tools to access such data, the progressive structuring of such documents has important benefits for daily operations. While there exist many approaches to extract information in open domains, we lack tools flexible enough to adapt themselves to the particularities of different domains.

Probabilistic Range Queries (PRQ) retrieve objects which, according to imprecise object properties, are (with a given probability) inside a precise range. When the query range is based on some imprecise object properties, which makes the query range imprecise as well, then Uncertain Probabilistic Range Queries (UPRQ) arise. Unfortunately, in the literature UPRQs ranges are constrained to be balls, i.e., the range is defined by providing a certain radius around an imprecise object property. Moreover, another important issue is the efficiency of answering UPRQs due to the necessary numerical operations to calculate probabilities. In this work we give a novel definition for UPRQs with query ranges of any shape; in addition we prove that any UPRQ can be reduced to a PRQ. Concerning the efficiency of UPRQs, we adopt and improve the usual way to address this family of queries (i.e., constructing indexes to prune/validate which objects belong to the answer, avoiding unnecessary numerical calculations) presenting: (1) a method to improve the filtering capabilities of the indexes when dealing with uniform distributions over rectangles or balls; and (2) a new index (eUD-Index), which enhances the state of the art, for any type of probability distribution. Our experiments show the feasibility of the proposals.

In this article, the authors introduce the notion of ABox intensity in the context of predicting reasoner performance to improve the representativeness of ontology metrics, and they develop new metrics that focus on ABox features of OWL 2 EL ontologies. Their experiments show that taking into account the intensity through the proposed metrics contributes to overall prediction accuracy for ABox intensive ontologies.

This short paper motivates the need for incremental and serializable semantic reasoners. Two possible scenarios where semantic reasoners with these feature could be interesting are outlined, namely reasoning on mobile devices and managing dynamic knowledge.

During the last years, mobile computing has been the focus of many research efforts, due mainly to the ever-growing use of mobile devices. In this context, there is a need to manage dynamic data, such as location data or other data provided by sensors. As an example, the continuous processing of location-dependent queries has been the subject of thorough research. However, there is still a need of highly expressive ways of formulating queries, augmenting in this way the systems' answer capabilities. Regarding this issue, the modeling power of Description Logics (DLs) and the inferring capabilities of their attached reasoners could fulfill this new requirement. The main problem is that DLs are inherently oriented to model static knowledge, that is, to capture the nature of the modeled objects, but not to handle changes in the property values (which requires a full ontology reclassification), as it is common in mobile computing environments (e.g., the location is expected to vary continually). In this paper, the authors present a novel approach to process continuous queries that combines 1) the DL reasoning capabilities to deal with static knowledge, with 2) the efficient data access provided by a relational database to deal with volatile knowledge. By marking at modeling time the properties that are expected to change during the lifetime of the queries, the authors'system is able to exploit both the results of the classification process provided by a DL reasoner, and the low computational costs of a database when accessing changing data (mobile environments, semantic sensors, etc.), following a two-step continuous query processing that enables us to handle continuous DL queries efficiently. Experimental results show the feasibility of the authors' approach.

Location-based services have become an increasingly interesting research area in the last two decades. However, in many scenarios, dealing with the most precise location coordinates is not the best solution since people structure the world in geographic areas instead of coordinates. Since humans work with abstractions, and names are the way we refer to those abstractions, introducing semantics in geographic definitions becomes natural. For example, users can be interested in states with vacation resorts and may want to retrieve the state names, instead of the exact geographic limits of such states. Moreover, semantics introduces new challenges, such as how to exploit the location semantics to infer new information from known definitions. For instance, we may want a system to automatically obtain the value added tax (VAT) that should be applied by a shop in Madrid, inferring the applicable tax by considering the economic area where Madrid is included (in this case, Spain); notice that the VAT should not be inferred from a bigger economic area, like Europe, although it also includes Madrid geographically. Thus, the expression of locations at different granularities extends the traditional location-based query processing to consider the most appropriate semantics for each user. In this article, adopting description logics (DLs) as a base formalism, we provide a formalization of the notion of semantic location granule and semantic granule map. We benefit from the underlying semantics of the different granularities to extend the expressivity of location-based queries and automatically discover and infer new knowledge. The model we propose uses a DL reasoner to infer new granules relationships. In particular, a DL reasoner can infer containment and intersection relationships between location granules (and help to obtain several more relationships), which provides the way to introduce semantics in location-based queries. This is done within the logical frame of DLs, thus ensuring that our approach can be supported by existing regular DL reasoners (such as Pellet, Racer Pro, and HermiT) without the need to extend their reasoning capabilities.

The syntactic approach of most of Web search engines still has the drawback of not considering the semantics of the keywords entered by the user. So, users usually have to browse many hits looking for the information they want. In this paper, we present a system that, given a set of keywords with well defined semantics, automatically generates a set of formal queries, in the query language of the user's choice, which attempt to capture what the user had in mind when she or he wrote those keywords. The system uses ontologies and a description logics reasoner to perform a semantic enrichment of user keywords to improve the discovering of possible user queries and to reject semantically inconsistent queries.