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Modal logic S5 has attracted significant attention and has led to several practical applications, owing to its simplified approach to dealing with nesting modal operators. Efficient implementations for evaluating satisfiability of S5 formulas commonly rely on Skolemisation to convert them into propositional logic formulas, essentially by introducing copies of propositional atoms for each set of interpretations (possible worlds). This approach is simple, but often results into large formulas that are too difficult to process, and therefore more parsimonious constructions are required. In this work, we propose to use Answer Set Programming for implementing such constructions, and in particular for identifying the propositional atoms that are relevant in every world by means of a reachability relation. The proposed encodings are designed to take advantage of other properties such as entailment relations of subformulas rooted by modal operators. An empirical assessment of the proposed encodings shows that the reachability relation is very effective and leads to comparable performance to a state-of-the-art S5 solver based on SAT, while entailment relations are possibly too expensive to reason about and may result in overhead. This paper is under consideration for acceptance in TPLP., Comment: Paper presented at the 37th International Conference on Logic Programming (ICLP 2021), September 2021, 16 pages, 3 figures

Knowledge-based artificial intelligence has been extensively used in numerous application areas leading to the development of a vast number of methods and tools. In recent years, focus has shifted on non-symbolic approaches, and neural networks in particular have achieved human-level performance in various applications where accountability is a very important issue, closely related to the interpretability of artificial intelligence methods in general. Lack of interpretability of neural networks and various machine learning methods has led to the adoption of knowledge-based methods instead, which offer models compliant with explainability and interpretability requirements. In this article, an overview of knowledge-based methods is presented along with the state of the art in this area, offering to the AI practitioner guidance for applying these important methods in practice.

Summarization: This paper describes work in progress about using AI technologies to support diagnostic decision making. In particular, we analyse clinical data of past cases to develop a data-driven prediction model for future cases. To do so, we use a versatile AutoML platform that applies a multitude of machine learning algorithms and their configurations. Our results show initial promise, but also point to limitations of currently available data, opening up avenues for further research. Παρουσιάστηκε στο: 13th International Conference on Management of Digital EcoSystems

Qualitative reasoning involves expressing and deriving knowledge based on qualitative terms such as natural language expressions, rather than strict mathematical quantities. Well over 40 qualitative calculi have been proposed so far, mostly in the spatial and temporal domains, with several practical applications such as naval traffic monitoring, warehouse process optimisation and robot manipulation. Even if a number of specialised qualitative reasoning tools have been developed so far, an important barrier to the wider adoption of these tools is that only qualitative reasoning is supported natively, when real-world problems most often require a combination of qualitative and other forms of reasoning. In this work, we propose to overcome this barrier by using ASP as a unifying formalism to tackle problems that require qualitative reasoning in addition to non-qualitative reasoning. A family of ASP encodings is proposed which can handle any qualitative calculus with binary relations. These encodings are experimentally evaluated using a real-world dataset based on a case study of determining optimal coverage of telecommunication antennas, and compared with the performance of two well-known dedicated reasoners. Experimental results show that the proposed encodings outperform one of the two reasoners, but fall behind the other, an acceptable trade-off given the added benefits of handling any type of reasoning as well as the interpretability of logic programs. This paper is under consideration for acceptance in TPLP., Paper presented at the 36th International Conference on Logic Programming (ICLP 2020), University Of Calabria, Rende (CS), Italy, September 2020, 18 pages, 3 figures

The representation of temporal information has been in the center of intensive research activities over the years in the areas of knowledge representation, databases and more recently, the Semantic Web. The proposed approach extends the existing framework of representing temporal information in ontologies by allowing for representation of concepts evolving in time (referred to as “dynamic” information) and of their properties in terms of qualitative descriptions in addition to quantitative ones (i.e., dates, time instants and intervals). For this purpose, we advocate the use of natural language expressions, such as “before” or “after”, for temporal entities whose exact durations or starting and ending points in time are unknown. Reasoning over all types of temporal information (such as the above) is also an important research problem. The current work addresses all these issues as follows: The representation of dynamic concepts is achieved using the “4D-fluents” or, alternatively, the “N-ary relations” mechanism. Both mechanisms are thoroughly explored and are expanded for representing qualitative and quantitative temporal information in OWL. In turn, temporal information is expressed using either intervals or time instants. Qualitative temporal information representation in particular, is realized using sets of SWRL rules and OWL axioms leading to a sound, complete and tractable reasoning procedure based on path consistency applied on the existing relation sets. Building upon existing Semantic Web standards (OWL), tools and member submissions (SWRL), as well as integrating temporal reasoning support into the proposed representation, are important design features of our approach.

Spatial information is often expressed using qualitative terms such as natural language expressions instead of coordinates; reasoning over such terms has several practical applications, such as bus routes planning. Representing and reasoning on trajectories is a specific case of qualitative spatial reasoning that focuses on moving objects and their paths. In this work, we propose two versions of a trajectory calculus based on the allowed properties over trajectories, where trajectories are defined as a sequence of non-overlapping regions of a partitioned map. More specifically, if a given trajectory is allowed to start and finish at the same region, 6 base relations are defined (TC-6). If a given trajectory should have different start and finish regions but cycles are allowed within, 10 base relations are defined (TC-10). Both versions of the calculus are implemented as ASP programs; we propose several different encodings, including a generalised program capable of encoding any qualitative calculus in ASP. All proposed encodings are experimentally evaluated using a real-world dataset. Experiment results show that the best performing implementation can scale up to an input of 250 trajectories for TC-6 and 150 trajectories for TC-10 for the problem of discovering a consistent configuration, a significant improvement compared to previous ASP implementations for similar qualitative spatial and temporal calculi.

Summarization: We propose CHRONOS, a system for reasoning over temporal information in OWL ontologies. Representing both qualitative temporal (i.e., information whose temporal extents are unknown such as “before”, “after” for temporal relations) in addition to quantitative information (i.e., where temporal information is defined precisely e.g., using dates) is a distinctive feature of the proposed approach. Qualitative representations are very common in natural language expressions such as in free text or speech and can be proven to be valuable in the Semantic Web. CHRONOS reasoner applies over temporal relations in order to infer implied relations and to detect inconsistencies while retaining soundness, completeness and tractability over the supported relations set. The experimental results demonstrated that CHRONOS runs up to several times faster compared to its SWRL counterpart and scales-up well for large relation sets. Παρουσιάστηκε στο: 17th International Conference in Knowledge Based and Intelligent Information and Engineering Systems

The rapid growth of sensor networks and smart devices has led to the generation of an increasing amount of information. Such information typically originates from various sources and is published in different formats. One of the key prerequisites for the Internet of Things (IoT) is interoperability. The Hypercat specification defines a lightweight JSON-based hypermedia catalogue, and is tailored towards the existing needs of industry. In this work, we propose a semantic enrichment of Hypercat, defining an RDF-based catalogue. We propose an ontology that captures the core of the Hypercat RDF specification and provides a mapping mechanism between existing JSON and proposed RDF properties. Finally, we propose a new type of search, called Semantic Search, which allows SPARQL-like queries on top of semantically enriched Hypercat catalogues and discuss how this semantic approach offers advantages over what was previously available.

Representation of temporal and spatial information for the Semantic Web often involves qualitative defined information (i.e., information described using natural language terms such as “before” or “overlaps”) since precise dates or coordinates are not always available. This work proposes several temporal representations for time points and intervals and spatial topological representations in ontologies by means of OWL properties and reasoning rules in SWRL. All representations are fully compliant with existing Semantic Web standards and W3C recommendations. Although qualitative representations for temporal interval and point relations and spatial topological relations exist, this is the first work proposing representations combining qualitative and quantitative information for the Semantic Web. In addition to this, several existing and proposed approaches are compared using different reasoners and experimental results are presented in detail. The proposed approach is applied to topological relations (RCC5 and RCC8) supporting both qualitative and quantitative (i.e., using coordinates) spatial relations. Experimental results illustrate that reasoning performance differs greatly between different representations and reasoners. To the best of our knowledge, this is the first such experimental evaluation of both qualitative and quantitative Semantic Web temporal and spatial representations. In addition to the above, querying performance using SPARQL is evaluated. Evaluation results demonstrate that extracting qualitative relations from quantitative representations using reasoning rules and querying qualitative relations instead of directly querying quantitative representations increases performance at query time.

Summarization: Representing information that evolves in time in ontologies, as well as reasoning over static and dynamic ontologies are the areas of interest in this work. Building upon well established standards of the semantic Web and the 4D-fluents approach for representing the evolution of temporal information in ontologies, this work demonstrates how qualitative temporal relations that are common in natural language expressions (i.e., relations between time intervals like “before”, “after”, etc.) are represented in ontologies. Existing approaches allow for representations of temporal information, but do not support representation of qualitative relations and reasoning. Παρουσιάστηκε στο: 2nd International Workshop on Combinations of Intelligent Methods and Applications

Summarization: Representing dynamic information in the form of ontologies, as well as querying and reasoning over static and dynamic ontologies are the areas of interest in this work. Building upon well established standards of the semantic Web and the 4D-fluents approach for representing the evolution of temporal information in ontologies, SOWL illustrates how spatial and spatio-temporal information and evolution in space and time can be efficiently represented in OWL. It also demonstrates how qualitative temporal and spatial relations that are common in natural language expressions (i.e., relations between time intervals like "before", "after", etc.) are represented in the ontology. Existing approaches allow for representations of only static, spatial or temporal information, but do not support combinations on the nature of information or representation of qualitative relations. Part of the proposed representation model is the SOWL (high-level) query language, which does not require users to be familiar with peculiarities of the underlying ontology representation (which can be complex) and handles ontologies similar to relational databases. A distinct feature of SOWL is the incorporation spatio-temporal reasoning in SOWL, which is capable of inferring spatio-temporal information from representations in the underlying ontology model. Παρουσιάστηκε στο: 6th International Conference on Semantic Systems

Summarization: TOQL, is a query language for querying time information in ontologies. An application has been developed that supports translation and execution of TOQL queries on temporal ontologies. A Graphical User Interface (GUI) has been also developed to facilitate user interaction and supports operations such as syntax highlighting, code autosuggestion, loading of the ontology into the main memory, results and error display. Παρουσιάστηκε στο: 11th International Symposium on Spatial and Temporal Databases

Summarization: We propose SOWL, an ontology for representing and reasoning over spatio-temporal information in OWL. Building upon well established standards of the semantic web (OWL 2.0, SWRL) SOWL enables representation of static as well as of dynamic information based on the 4D-fluents (or, equivalently, on the N-ary) approach. Both RCC-8 topological and cone-shaped directional relations are integrated in SOWL. Representing both qualitative temporal and spatial information (i.e., information whose temporal or spatial extents are unknown such as “left-of” for spatial and “before” for temporal relations) in addition to quantitative information (i.e., where temporal and spatial information is defined precisely) is a distinctive feature of SOWL. The SOWL reasoner is capable of inferring new relations and checking their consistency, while retaining soundness, completeness, and tractability over the supported sets of relations. Παρουσιάστηκε στο: th International Symposium on Rules: Research Based and Industry Focused

Summarization: We present SOWL, a model for representing spatio-temporal information in OWL. SOWL enables representation of dynamic information such as objects, concepts and properties changing in time (e.g., moving objects in a video). Παρουσιάστηκε στο: 7th Extended Semantic Web Conference

Summarization: We propose an approach for reasoning and querying over temporal information in OWL ontologies. Representing both qualitative temporal (i.e., information whose temporal extents are unknown such as “before”, “after” for temporal relations) in addition to quantitative information (i.e., where temporal information is defined precisely e.g., using dates) is a distinctive feature of the proposed ontology. Qualitative representations are very common in natural language expressions such as in free text or speech and can be proven to be valuable in the Semantic Web. Reasoning rules applying over temporal relations, infer implied relations, detect inconsistencies and retain soundness, completeness and tractability over the supported sets of relations using path consistency. Temporal representations are defined on time instants rather than on intervals (as it is typical in the literature), resulting into simpler yet equivalent representations. A SPARQL-based temporal query language capable of exploiting the characteristics of the underlying representation is also implemented and discussed. Παρουσιάστηκε στο: 5th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems

Summarization: This work addresses issues related to the design and implementation of focused crawlers. Several variants of state-of-the-art crawlers relying on web page content and link information for estimating the relevance of web pages to a given topic are proposed. Particular emphasis is given to crawlers capable of learning not only the content of relevant pages (as classic crawlers do) but also paths leading to relevant pages. A novel learning crawler inspired by a previously proposed Hidden Markov Model (HMM) crawler is described as well. The crawlers have been implemented using the same baseline implementation (only the priority assignment function differs in each crawler) providing an unbiased evaluation framework for a comparative analysis of their performance. All crawlers achieve their maximum performance when a combination of web page content and (link) anchor text is used for assigning download priorities to web pages. Furthermore, the new HMM crawler improved the performance of the original HMM crawler and also outperforms classic focused crawlers in searching for specialized topics. Presented on: Data and Knowledge Engineering

Summarization: We introduce TOQL, a query language for querying time information in ontologies. TOQL is a high level query language that handles ontologies almost like relational databases. Queries are issued as SQL-like statements involving time (i.e., time points or intervals) or high-level ontology concepts that vary in time. Although independent from TOQL, this work suggests a mechanism for representing time evolving concepts in ontologies based on the four-dimensional perdurantist mechanism. However, TOQL prevents users from being familiar with the representation of time in ontologies. To show proof of concept, an application has been developed that supports translation and execution of TOQL queries on temporal ontologies combined with a reasoning mechanism based on event calculus. A real world temporal ontology is also implemented on which several TOQL example queries are processed and discussed. Παρουσιάστηκε στο: 11th International Symposium on Spatial and Temporal Databases

Representing topological information for the Semantic Web often involves qualitative defined natural language terms such as "Into" or "Overlapping". This can be the case when\ud exact coordinates of spatial regions are not available, they are incomplete or unreliable. Topological spatial relations are the most important aspect of spatial representation and\ud reasoning, thus embedding such relations into an ontology along with their semantics as expressed using reasoning rules\ud is an important issue. In this work we propose a representation of RCC-5 topological relations using OWL object properties and axioms, combined with reasoning rules expressed\ud using SWRL embedded into the ontology.\ud Three alternative representations are proposed and compared: the first is based on a straightforward implementation of the path consistency method for spatial reasoning,\ud the second is an optimized version of the path consistency\ud based representation implemented using an alternative representation of the topological equality relation and the third\ud is based on the decomposition of RCC-5 relations to simpler\ud ones. To the best of our knowledge this is the first work\ud dealing with topological RCC-5 relations representation for\ud the Semantic Web. In addition, our work improves the performance of topological RCC-8 relations reasoning when the\ud best method, in terms of reasoning time, for RCC-5 relations\ud is applied over RCC-8 relations.

Summarization: Improving a city's infrastructure is seen as a crucial part of its sustainability, leading to efficiencies and opportunities driven by technology integration. One significant step is to support the integration and enrichment of a broad variety of data, often using state of the art linked data approaches. Among the many advantages of such enrichment is that this may enable the use of intelligent processes to autonomously manage urban facilities such as traffic signal controls. In this paper we document an attempt to integrate sets of sensor and historical data using a data hub and a set of ontologies for the data. We argue that access to such high level integrated data sources leads to the enhancement of the capabilities of an urban transport operator. We demonstrate this by documenting the development of a planning agent which uses such data as inputs in the form of logic statements, and when given traffic goals to achieve, outputs complex traffic signal strategies which help transport operators deal with exceptional events such as road closures or road traffic saturation. The aim is to create an autonomous agent which reacts to commands from transport operators in the face of exceptional events involving saturated roads, and creates, executes and monitors plans to deal with the effects of such events. We evaluate the intelligent agent in a region of a large urban area, under the direction of urban transport operators. Παρουσιάστηκε στο: Transportation Research Part C: Emerging Technologies