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We propose a method for estimating argument diversity and interactivity in online discussion threads. Using a case study on the subject of Black Pete ("Zwarte Piet") in the Netherlands, the approach for automatic detection of echo chambers is presented. Dynamic thread scoring calculates the status of the discussion on the thread level, while individual messages receive a contribution score reflecting the extent to which the post contributed to the overall interactivity in the thread. We obtain platform-specific results. Gab hosts only echo chambers, while the majority of Reddit threads are balanced in terms of perspectives. Twitter threads cover the whole spectrum of interactivity. While the results based on the case study mirror previous research, this calculation is only the first step towards better understanding and automatic detection of echo effects in online discussions., OASIcs, Vol. 93, 3rd Conference on Language, Data and Knowledge (LDK 2021), pages 39:1-39:9

Automatic lifestyle profiling to categorize users according to their daily routine-based lifestyles is an unexplored area. Despite the current trends on having wearable devices that generate large amounts of heterogeneous data, figuring out the lifestyle patterns of people is not a trivial task. We present Lifestyles-KG, a knowledge graph (fuzzy ontology) for semantic reasoning from wearable sensors. It can serve as a pre-processing taxonomical step that can be integrated into further prediction techniques for intuitively categorizing fuzzy lifestyle concepts, treats or profiles. The ultimate aim is to help tasks such as long-term human behavior classification and consequently, improve virtual coaching or customize lifestyle recommendation and intervention programs from free form non-labelled sensor data.

The aim of this chapter is to present a detailed, selfcontained and comprehensive account of the state of the art in representing and reasoning with fuzzy knowledge in Semantic Web Languages such as triple languages RDF/RDFS, conceptual languages of the OWL 2 family and rule languages. We further show how one may generalise them to so-called annotation domains, that cover also e.g. temporal and provenance extensions.

Objectives To compare intensive care unit (ICU) mortality in patients with severe community-acquired pneumonia (SCAP) caused by Legionella pneumophila receiving combined therapy or monotherapy. Methods A prospective multicenter study was made, including all patients with sporadic, community-acquired Legionnaires’ disease (LD) admitted to the ICU. Admission data and information on the course of the disease were recorded. Antibiotic prescriptions were left to the discretion of the attending physician and were not standardized. Results Twenty-five cases of SCAP due to L. pneumophila were included, and 7 patients (28%) out of 25 died after a median of 7 days of mechanical ventilation. Fifteen patients (60%) presented shock. Levofloxacin and clarithromycin were the antibiotics most commonly used in monotherapy, while the most frequent combination was rifampicin plus clarithromycin. Patients subjected to combination therapy presented a lower mortality rate versus patients subjected to monotherapy (odds ratio for death [OR] 0.15; 95%CI 0.02–1.04; p = 0.08). In patients with shock, this association was stronger and proved statistically significant (OR for death 0.06; 95%CI 0.004–0.86; p = 0.04). Conclusions Combined antibiotic therapy decreases mortality in patients with SCAP and shock caused by L. pneumophila.

Fuzzy ontologies allow the representation of imprecise structured knowledge, typical in many real-world application domains. A key factor in the practical success of fuzzy ontologies is the availability of highly optimized reasoners. This short paper discusses a novel optimization technique: a reduction of the size of the optimization problems obtained during the inference by the fuzzy ontology reasoner fuzzyDL.

Fuzzy Description Logics (DLs) are a logical formalism for the representation of structured knowledge affected by imprecision or vagueness. The chapter provides insights in terms of Mathematical Fuzzy Logic.

To overcome the inability of Description Logics (DLs) to represent vague or imprecise information, several fuzzy extensions have been proposed in the literature. In this context, an important family of reasoning algorithms for fuzzy DLs is based on a combination of tableau algorithms and Operational Research (OR) problems, specifically using Mixed Integer Linear Programming (MILP). In this paper, we present a MILP-based tableau procedure that allows to reason within fuzzy $\alcb$, \ie, $\alc$ with individual value restrictions. Interestingly, unlike classical tableau procedures, our tableau algorithm is deterministic, in the sense that it defers the inherent non-determinism in $\alcb$ to a MILP solver.

General Concept Inclusion (GCIs) absorption algorithms have shown to play an important role in classical Description Logics (DLs) reasoners, as they allow to transform GCIs into simpler forms to which apply specialised inference rules, resulting in an important performance gain. In this work, we develop a first absorption algorithm for fuzzy DLs, and evaluate it over some ontologies.

Fuzzy Description Logics (Fuzzy DLs) are logics that allow to deal with structured knowledge affected by fuzziness. Fuzzy DLs are at the heart of Fuzzy OWL 2, a fuzzy version of the standard ontology language OWL 2. Although a relatively important amount of work has been carried out in the last years, fuzzy DLs are open to be extended with several features worked out in other fields. In particular, the integration of aggregation operators (AOs) in fuzzy DLs has received little attention so far. In this work, we show how to support aggregation operators in fuzzy DLs. We provide syntax and semantics of a fuzzy DL extended with AOs, and provide a calculus for a family of AOs (weighted sum, OWA and quantifier-guided OWA). We also show how to encode them into our proposal for Fuzzy OWL 2.

Fuzzy Description Logics are a family of logics which allow to deal with structured knowledge affected by vagueness. Although a relatively important amount of work has been carried out in the last years, current fuzzy DLs are open to be extend with several features worked out in the fuzzy logic literature. In this work, we extend fuzzy DLs with fuzzy truth values, allowing to state sentences such as ``Tina is young is almost true''.

We present a novel Fuzzy Description Logic (DL) based approach to automate matchmaking in e-marketplaces. We model traders' preferences with the aid of Fuzzy DLs and, given a request, use utility values computed w.r.t. Pareto agreements to rank a set of offers. In particular, we introduce an expressive Fuzzy DL, extended with concrete domains in order to handle numerical, as well as non numerical features, and to deal with vagueness in buyer/seller preferences. Hence, agents can express preferences as eg textit{I am searching for a passenger car costing about 22000euro, yet if the car has a GPS system and more than two-year warranty I can spend up to 25000euro}. Noteworthy our matchmaking approach, among all the possible matches, chooses the mutually beneficial ones.

We present a novel Fuzzy Description Logic (DL) based approach to automate matchmaking in e-marketplaces. We model traders' preferences with the aid of Fuzzy DLs and, given a request, use utility values computed w.r.t. Pareto agreements to rank a set of offers. In particular, we introduce an expressive Fuzzy DL, extended with concrete domains in order to handle numerical, as well as non numerical features, and to deal with vagueness in buyer/seller preferences. Hence, agents can express preferences as eg textit{I am searching for a passenger car costing about 22000euro, yet if the car has a GPS system and more than two-year warranty I can spend up to 25000euro}. Noteworthy our matchmaking approach, among all the possible matches, chooses the mutually beneficial ones.

Fuzzy Description Logics have been proposed as a family of languages to describe vague or imprecise structured knowledge. This work deals with one of the less studied constructors, qualified cardinality restrictions, showing some counter-intuitive behaviours under Lukasiewicz semantics, and proposing a new semantics and the corresponding reasoning algorithm.

Fuzzy Description Logics (fuzzy DLs) have been proposed as a language to describe structured knowledge with vague concepts. In [19], a solution based on Mixed Integer Linear Programming has been proposed to deal with fuzzy DLs under Lukasiewicz semantics in which typical membership functions, such as triangular and trapezoidal functions, can be explicitly represented in the language. A major theoretical and computational limitation so far is the inability to deal with General Concept Inclusions (GCIs), which is an important feature of classical DLs. In this paper, we address this issue and develop a calculus for fuzzy DLs with GCIs under various semantics: classical logic, \Zadeh semantics", and Lukasiewicz logic.

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We present a novel Fuzzy Description Logic (DL) based approach to automate matchmaking in e-marketplaces. We model traders' preferences with the aid of Fuzzy DLs and, given a request, use utility values computed w.r.t. Pareto agreements to rank a set of offers. In particular, we introduce an expressive Fuzzy DL, extended with concrete domains in order to handle numerical, as well as non numerical features, and to deal with vagueness in buyer/seller preferences. Hence, agents can express preferences as e.g. "I am searching for a passenger car costing about 22000 euro, yet if the car has a GPS system and more than two-year warranty I can spend up to 25000 euro". Noteworthy our matchmaking approach, among all the possible matches, chooses the mutually beneficial ones.