Abstract We introduce an extension of classical probabilistic propositional logic $\mathsf{LPP}_{1}$, understood as an extension of classical propositional calculus with real-valued probability functions and iterated probability operators, by incorporating similarity operators based on the Jaccard index. The binary operators $J_{\geqslant s}(\alpha ,\beta )$ and $J_{\leqslant s}(\alpha ,\beta )$ allow us to formally reason about the degree of similarity between propositions, defined through the ratio of the probability of their conjunction and the probability of their disjunction. This addition enriches the expressive power of probabilistic logic and provides a natural way to capture relationships between formulas that go beyond absolute probability. We present the syntax and semantics of the resulting system $\mathsf{LP}_{J}$, establish a sound and complete axiomatization, and prove decidability by reducing satisfiability problems to finite systems of linear inequalities over real closed fields. The logic thus provides a mathematically robust framework that combines probability and similarity, with potential applications in artificial intelligence, knowledge representation and decision-making, especially in contexts where clustering and comparison of structured knowledge are essential.

Bipolar neutrosophic soft sets are powerful tools for modeling data under conditions of uncertainty and imprecision due to their rich parametric structure and the useful mathematical properties of the operations defined on them. In this paper, motivated by the limitations of existing decision‐making algorithms, we introduce a new numerical characteristic, the energy of a bipolar neutrosophic soft set defined using singular values, analogous to the graph energy and nuclear norm. Our goal is to develop an efficient decision‐making algorithm that successfully identifies the optimal alternative even in cases where other algorithms provide inaccurate or inconsistent results. Our research is motivated by the need for more reliable decision‐making methods in complex soft environments and the potential of the energy‐based approach to overcome the weaknesses of existing methods, which we demonstrate through a comparative analysis using concrete examples.

Abstract We develop a system of basic probability reasoning founded on two great logical concepts, Gentzen’s natural deduction systems and Carnap–Popper probability of sentences. Our system makes it possible to manipulate with probabilized sentences and justify their causal relationships: if probabilities of sentences $A$ and $B$ are in $[r,1]$ and $[s,1]$, respectively, then the probability of sentence $C$ belongs to $[t,1]$, i.e. $A^{r},B^{s}\vdash C^{t}$, for $r,s,t\in [0,1]$. We prove that our system is sound and complete with respect to the traditional Carnap–Popper type probability semantics. This approach opens up a new perspective of proof-theoretic treatment of sentence probability, potentially allowing immediate algorithmic use of the pure syntactic convenience of natural deductions in programming.

The aim of this article is developing a formal system suitable for reasoning about the distance between propositional formulas. We introduce and study a formal language which is the extension of the classical propositional language obtained by adding new binary operators D ≤s and D ≥s , s ∈ Range, where Range is a fixed finite set. In our language it is allowed to make formulas of the form D ≤s ( α; β) with the intended meaning ’distance between formulas α and β is less than or equal to s’. The semantics of the proposed language consists of possible worlds with a distance function defined between sets of worlds.

In this paper we present a novel approach for real-time anomaly detection in the flexible production, an emerging area in the manufacturing, esp. in the context of Industry4.0. It is based on an advanced usage of Complex Event Processing combined with the massive data analytics, which enables learning of the clusters, which represent normal/usual and unusual/anomalous behaviour. The main innovation is in the combination of the model-based and data- driven approaches, which enables a continuous anomaly detection. The approach has been implemented using the D2Lab (Data Diagnostics Laboratory) framework for big data processing. The results have been tested in an industry case study, enabling efficient anomaly detection in the shoe manufacturing.

In this paper we present a novel approach for the process improvement based on the data-driven modelling. The idea is that by performing Big data analytics on the past process data we can model what is (statistically analyzed) usual/normal for a selected period and check the variations from that model in the real-time (as Six Sigma requires). Additionally, these data-driven models can support the root- cause analysis that should provide insights what can be eliminated as a waste in the process (as Lean requires). However, due to the above mentioned variety and volume of data, the analytics must be a) robust - dealing with differences efficiently and b) scalable - realized in an extremely parallel way. We propose a novel method for process control that uses big data analytics approaches to deal with the multidimensionality and the large size of the process space. In order to realize this idea we develop a new concept of self- aware digital twins which are able to reason about own behaviour and react if needed. Indeed, we revolutionize the concept of digital twins by extending their "virtual replica" (of physical objects) nature into "digital self-awareness" of physical objects (assets, systems), leading to the new generation of digital twins, so called self-aware DTs, which can "reasons" about the behaviour of an object (and not only mimic it) and actively participate in its improvement. We present the outcomes from the case study related to 3D laser cutting process.

Anomaly detection is the process of discovering some anomalous behaviour in the real-time operation of a system. It is a difficult task, since in a general case (multivariate anomaly detection) an anomaly can be related to the behaviour of several parameters which are not necessarily behaving anomalously per se, but their (complex) relation is anomalous (not usual/normal). This implies the need for a very efficient modeling of the normal behaviour in order to know what should be treated as anomalous/outlier/unusual. Consequently, classical model-driven approaches, due to their focus on the selected parameters for creating models, are not able to model the behaviour of the whole system. This is why data-driven approaches for anomaly detection are getting even more important for the industry use cases where hundreds (thousands) of parameters should be taken into account. However, current approaches are usually focused on the univariate anomaly detection (or some variations of it), so without observing the entire space of relations since the computation is very complex. In this paper we present a novel approach for the multivariate anomaly detection that is based on modeling and managing the streams of variations in a multidimensional space. The main advantage of this approach is the possibility to observe the relations between variations in a large set of parameters and create clusters of “normal/usual” variations. In order to ensure scaling, which is one of the most challenging requirements, the approach is based on the usage of the big data technologies for realizing data analytics tasks/calculations. The approach is realized as a part of D2Lab (Data Diagnostics Laboratory) framework and has been applied in several industrial use cases. In this paper we present an interesting usage for the anomaly detection in the process of functional testing of home appliances (in particular case refrigerators) after manufacturing/assembling process. It has been done for a big vendor (Whirlpool), who expects huge saving in testing and improved customer satisfaction from this approach.

In this paper we present a novel approach for data-driven Quality Management in industry processes that enables a multidimensional analysis of the anomalies that can appear and their real-time detection in the running system. The approach revolutionizes the way how quality control (and esp. anomaly detection) will be realized in production processes influenced by many parameters that can be in complex nonlinear correlations. It consists of two main steps: learning the normal behavior of the system (based on past data) and detecting an anomalous behavior in the real-time (by processing real-time data). The approach is especially suitable for modern industry systems that follow Industry 4.0 principles of ubiquity sensing and proactive responding. One of the main advantages is the self-adaptive nature of the approach due to its data-driven orientation, so that the model and parameters of the approach will be continuously updated to the dynamicity of data. The approach has been applied in the process of manufacturing microwave ovens (Whirlpool) and in this paper we present results for the data-driven quality control of one of the most critical parts - microwave oven fan. Due to the high speed of the rotation, every item has to be very precisely produced (according to the CAD model), which requires very strong quality control process.

One of the most important challenges in manufacturing is the continuous process improvement that requires new insights about the behavior/quality control of processes in order to understand the optimization/improvement potential. The paper elaborates on usage of big data-driven clustering for an efficient discovering of real-time unusualities in the process and their route-cause analysis. Our approach extends traditional clustering algorithms (like k-Means) with methods for better understanding the nature of clusters and provides a very efficient big data realization. We argue that this approach paves the way for a new generation of quality management tools based on big data analytics that will extend traditional statistical process control and empower Lean Six Sigma through big data processing. The proposed approach has been applied for improving process control in Whirlpool (washing machine tests, factory in Italy) and we present the most important finding from the evaluation study.

In this paper we present a novel approach for dynamic remote activity monitoring based on mobile complex event processing that has been used in a use case in the eHealth domain. Using complex event processing (CEP) in mobile environment enables a more flexible and efficient processing of personal sensor data and environment data, which are detected by sensors embedded in a mobile device. The main advantages of our approach are: an efficient combination of the mobile and server-side event processing through the semantic event model, optimal usage of mobile resources through dynamic management of mobile event processing and modelling of complex situations by using more expressive knowledge representation formalism. We present the settings for the use case and the results from the preliminary evaluation.

Adaptive behavior is one of the main challenges in building computerized systems, especially in the case of systems which are delivering information to the end users. Indeed, since the information overload has become the main drawback for the future development of such systems (cf. Big Data challenge), there is a huge movement in the research community to develop concepts for better adaptation of the form and size of information that will be delivered to a user (usually taking different forms of the personalization). However, the main effort has been dedicated to the contextualization of the user's task in order to determine what is the best way to tailor/adapt the presentation of information to the user, neglecting the role of the user's internal context, expressed as the user's (short-term) interest. The same is valid for the AR systems. In this tutorial we present novel results in modeling users' interest in the context of AR systems and demonstrate some practical results in realizing such an approach in a multisensor AR system based on the usage of the see-through AR glasses. Due to the need for continuously adapt the AR content to the user's interest, such models are facing many challenges in sensing the user's behavior (using acoustic-, video-, gesture- and bio-sensors), interpreting it as an interest and deciding in real-time what kind of the adaptation to perform. We argue that this lead to a new class of AR system that we coined as adaptive AR (AR) systems. This work has been partially realized within the scope of the FP7 ICT research project ARtSENSE (www.artsense.eu), that is developing new AR concepts for improving personalized museum's experience. The tutorial will present practical results from applying the approach in three cultural heritage institutions in Europe (Paris, Madrid and Liverpool).

Streams of events appear increasingly today in various Web applications such as blogs, feeds, sensor data streams, geospatial information, on-line financial data, etc. Event Processing (EP) is concerned with timely detection of compound events within streams of simple events. State-of-the-art EP provides on-the-fly analysis of event streams, but cannot combine streams with background knowledge and cannot perform reasoning tasks. On the other hand, semantic tools can effectively handle background knowledge and perform reasoning thereon, but cannot deal with rapidly changing data provided by event streams.

In this paper we present the idea of inverse search - an approach that aims to stimulate the diffusion of documents from the private spaces of individual workers into the shared space of an organization. Therefore we derive an organizational information need (OIN) based on query logs and further usage statistics of our system. This information is used to recommend people to share private documents containing relevant knowledge.

In this paper we present a method for tagging web pages using more formal conceptual structures than a set of keywords. We have developed a formal Tag model and a method to map a set of keywords on it. Moreover, this model is used as the basis for the conceptual tag refinement, which searches for terms which are conceptually related to the tags that are assigned to an information source. In that way the meaning of the tags can be disambiguated, which supports a better usage of tags for further management of tagged documents. We have developed a software tool, an annotation framework, which realizes this approach. We present results from the first evaluation studies regarding its application for ontology pruning.

The main problem in traditional information retrieval systems is an ad-hoc modeling of the interaction with users, which results in a very low retrieval's precision regarding a user's information need. In this paper we discuss the knowledge level of that interaction, i.e. how an analysis of a user's knowledge gap (that initiated the retrieval process) can be used for designing an efficient interaction model, especially regarding the query refinement task. Moreover, we indicate the role that the background knowledge (i.e. a domain ontology) plays in that model. We present conceptually a comprehensive query refinement process that enables a user to fulfil his need in a gradual, step-by-step querying process.This research shows that, complementary to the mainstream IR research that is focused on the improvement of retrieval algorithms, there is a lot of playroom for the improvement of the retrieval process by better modelling user's working context, especially his task and the information need that causes that task.

One of the vital problems in the searching for information is the ranking of the retrieved results, because users make typically very short queries (2-3 terms) and tend to consider only the first ten results. In traditional IR approaches the relevance of the results is determined only by analysing the underlying information repository (content and hyperlink structure), which leads to the weak relevance model. On the other hand, in the ontology-based IR the querying process is supported by an ontology such that other important sources for determining the relevance of results can be considered: the structure of the underlying domain and the characteristics of the searching process. In this paper we present a novel approach for determining relevance in ontology-based searching for information, which exploits the "full potential" of the semantics of such a semantically-based link structure.

In our previous work we have developed a logic-based approach for the refinement of ontology-based queries that enables a user to search through a repository in a step-by-step fashion. Since the set of refinements in a step can be large, they should be ranked according to their relevance for fulfilling a user's need. In this paper we present such a ranking model, which takes into account the information content (informativeness) of a refinement as well as the preferences of the user.

We present an approach that uses domain knowledge in order to support of queries posted to an information portal. This approach enables a user to navigate through the information content incrementally and interactively. In each refinement step a user is provided with a complete but minimal set of refinements, which enables him to develop/express his information need in a step-by-step fashion. In a case study regarding searching a bibliographic database we demonstrate the benefits of using our approach in the traditional information retrieval tasks, especially the combination of the free-text based querying and the ontology-based query refinement.

We present a logic-based approach for query refinement in ontology-based information portal. The approach enables a user to navigate through the information content incrementally and interactively. In each refinement step a user is provided with a complete but minimal set of refinements, which enables him to develop/express his information need in a step-by-step fashion. The approach is based on the model-theoretic interpretation of the refinement problem, so that the query refinement process can be considered as the process of inferring all queries which are subsumed by a given query. Moreover, query refinements are ranked according to their relevance to user's needs, whereas these needs are online discovered by analysing a user's behaviour. The approach is very suitable for modelling information retrieval tasks based on the database repositories, like searching product catalogues.

In this paper we present a novel approach for the refinement of relational queries that enables so-called step-by-step refinement of a user's query. The approach is based on discovering causal relationships between queries regarding the inclusion relation between the answers of these queries. We define a formal model for these query-answering pairs and use methods from inductive logic programming for the efficient calculation of a (lattice) order between them. The approach is very suitable for modelling information retrieval tasks based on the database repositories, like searching product catalogues.

In this paper we present a comprehensive approach for the refinement of ontology-based queries, which is based on incrementally (step-by-step) and interactively tailoring a query to the current information needs of a user, whereas these needs are implicitly and on-line elicited by analysing the user's behaviour during the searching process. The gap between a user's need and his query is quantified by measuring several types of query ambiguities. Consequently, in the refinement process a user is provided with a ranked list of refinements, which leads to a decrease of some of these ambiguities. Moreover, by exploiting the ontology background, the approach supports finding "similar" results that can help a user to satisfy his information need.

In this paper we present the conceptual architecture of ONTOLOGER, a system for usage-driven management of querying for information in ontology-based information portals. The system is based on the analysis of users' activities that are acquired in the so-called semantic log file, which contains information about the content of a visited page as well. These analyses are used for managing (i) the underlying ontology, (ii) querying mechanism and (iii) the content of the information repository, in order to ensure that (a) the structure of the vocabulary reflects the needs of users, (b) the highly-relevant results can be found by users although they make short queries and read only top-ranked results and (c) the information repository contains (only) resources users are interested in. The focus of the paper is on improving querying (retrieval) mechanism based on the usage data. We present a case study, which shows how a portal can benefit from implementing presented management system.

In this paper we present an approach to model ontology evolution as reconfiguration-design problem solving. The problem is reduced to a graph search where the nodes are evolving ontologies and the edges represent the changes that transform the source node into the target node. The search is guided by the constraints provided partially by a user and partially by a set of rules defining ontology consistency. In this way we allow a user to specify an arbitrary request declaratively and ensure its resolving. The approach is implemented in the KAON framework and the evaluation study shows its benefits.

We present a framework for query refinement that is driven by users' information needs. Since a query just approximates a user's need, we analyze the ambiguities of that query with respect to the vocabulary used for querying and the information repository. The goal is to determine the refinements that can help the user to express his original need more clearly. Particularly, a user is provided with the queries that are "nearby" the given query and a "compass" for determining the ways of changing the ambiguities of the query. The neighborhood of a query is defined regarding the result of that query. The formal concept analysis is used for its efficient calculation. We present an evaluation study that shows the benefits of our approach.

In this paper we present an approach for the query refinement called librarian agent query refinement process, which simulates the role a human librarian plays in the searching for information resources in a library. The process consists of two phases: problem discovery, in which the potential problems (ambiguities) of the initial query are discovered and estimated and query refinement, which analyses these ambiguities in order to provide suitable modification of that query that increases the precision and recall of the searching process. The agent performs comprehensive analyses of a query, based on the structure of the used vocabulary in an information portal and the content of its information repository. Moreover, the information about the previous users' behaviour and the activities of the current user are used for making the query refinement process more efficient (i.e. usage-driven and personalized). From the user's point of view, the main advantage of such an approach is that it enables a user to satisfy his information need in a more intuitive and efficient way.

The core idea of the Semantic Web is to make information accessible to human and software agents on a semantic basis. Hence, Web sites may feed directly from the Semantic Web exploiting the underlying structures for human and machine access. We have developed a domain-independent approach for developing semantic portals, viz. SEAL (SEmantic portAL), that exploits semantics for providing and accessing information at a portal as well as constructing and maintaining the portal. In this paper we focus on semantics-based means that make semantic Web sites accessible from the outside, i.e. semantics-based browsing, semantic querying, querying with semantic similarity, and machine access to semantic information. In particular, we focus on methods for acquiring and structuring community information as well as methods for sharing information.As a case study we refer to the AIFB portal - a place that is increasingly driven by Semantic Web technologies. We also discuss lessons learned from the ontology development of the AIFB portal..