The measurement of knowledge gain in information retrieval has garnered significant attention, particularly in evaluating its relationship with user behaviors and its role in enhancing the learning experience by tracking progress toward learning objectives.Previous studies have focused on estimating knowledge acquisition based on a static and predefined representation of learning objectives for each search topic, assessing users' progress toward these fixed goals.However, users often differ in their interests, focusing on various aspects or subtopics within the same broader topic, which they express through diverse, topic-related queries.In this paper, we propose a personalized approach to knowledge gain measurement by adapting and extending an existing method for inferring learning subgoals based on user queries.Our approach extends this method by dynamically determining the number of subgoals for each user query, rather than using a fixed number.Knowledge gain estimation is then conducted based on these individualized subgoals while also incorporating the user's prior knowledge of the search topic to enhance personalization.Using 10 different topics, we compare our approach to a baseline method in which the learning goal representation remains uniform for all users within a given topic.

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In the context of search as learning, users engage in search sessions to fill their information gaps and achieve their learning goals. Tracking the user’s state of knowledge is therefore essential for estimating how close they are to achieve these learning goals. In this respect, we extend a recently proposed approach that uses the recognition of entities present in the text to track the user’s knowledge. Our approach introduces a more complete representation by considering both the entities and their relations. More precisely, we represent both the user’s knowledge and the user’s learning goals (or target knowledge) as knowledge graphs.

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International audience

A reliable representation of the user’s knowledge state during a learning search session is crucial to understand their real information needs. When a search system is aware of such a state, it can adapt the search results and provide greater support for the user’s learning objectives. A common practice to track the user’s knowledge state is to consider the content of the documents they read during their search session(s). However, most current work ignores entity mentions in the documents, which, when linked to knowledge graphs, can be a source of valuable information regarding the user’s knowledge. To fill this gap, we extend RULK—Representing User Knowledge in Search-as-Learning—with entity linking capabilities. The extended framework RULK represents and tracks user knowledge as a collection of such entities. It eventually estimates the user knowledge gain—learning outcome—by measuring the similarity between the represented knowledge and the learning objective. We show that our methods allow for up to 10% improvements when estimating user knowledge gains.

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Association rule mining often leads the analyst into a rough rummaging process to identify rules that are relevant to understand specific problems. We propose a visualization interface to assist the rule selection process and evaluate it on an RDF knowledge graph derived from the COVID-19 Open Research Dataset. The user interface supports data exploration with focus on the overview of rules through a scatter plot, subsets of rules through a chord diagram chart, and itemsets through an association graph which is dynamically created by entering an item of interest (i.e. a named entity). Further, the analyst can interactively recover a list of publications containing the named entities involved in a particular rule. Among the original aspects of our approach, we highlight the representation of attributes describing measures of interest (i.e. confidence and interestingness), a visual indication of existence (or not) of symmetry in association rules, the exploration of subsets of rules according to clusters of publications and named entities, and an interactive prompting that aims at expanding the discovery of named entities within selected association rules. We assess our approach through a semi-structured interview involving experts in the domains of data mining and biomedicine, whose feedback could assist the refinement of the visual and interaction tools.

Information of the geographical and spatial type is found in numerous text documents and constitutes a very challenging target for extraction. Geoparsing applications have been developed to extract geographic terms. However, off-the-shelf Named Entity Recognition (NER) models are mainly designed for Toponym recognition and are very sensitive to language specificity. In this paper, we propose a workflow to first extract geographic and spatial entities based on a BiLSTM-CRF architecture with a concatenation of several text representations. We also propose a Relation Extraction module, particularly aimed at spatial relationships extraction, to build a structured Geospatial knowledge base. We demonstrate our pipeline by applying it to the case of French housing advertisements, which generally provide information about a property's location and neighbourhood. Our results show that the workflow tackles French language and the variability and irregularity of housing advertisements, generalizes Geoparsing to all geographic and spatial terms, and successfully retrieves most of the relationships between entities from the text.

Discovering disjointness axioms is a very important task in ontology learning and knowledge base enrichment. To help overcome the knowledge-acquisition bottleneck, we propose a grammar-based genetic programming method for mining OWL class disjointness axioms from the Web of data. The effectiveness of the method is evaluated by sampling a large RDF dataset for training and testing the discovered axioms on the full dataset. First, we applied Grammatical Evolution to discover axioms based on a random sample of DBpedia, a large open knowledge graph consisting of billions of elementary assertions (RDF triples). Then, the discovered axioms are tested for accuracy on the whole DBpedia. We carried out experiments with different parameter settings and analyze output results as well as suggest extensions.

Within multi-domain sentiment analysis, we study how different domain-dependent polarities can be learned for the same concepts.To this aim, we extend an existing approach based on the propagation of fuzzy polarities over a semantic graph capturing background linguistic knowledge to learn concept polarities with respect to various domains and their uncertainty from labeled datasets.In particular, we use POS tagging to refine the association between terms and concepts and word embedding to enhance the construction of the semantic graph.The proposed approach is then evaluated on a standard benchmark, showing that the combined use of POS tagging and word embedding improves its performance.One particularly strong point of the proposed approach is its recall, which is always very close to 100%.In addition, we observe that it exhibits good cross-domain generalization capabilities.

Axiom learning is an essential task in enhancing the quality of an ontology, a task that sometimes goes under the name of ontology enrichment. To overcome some limitations of recent work and to contribute to the growing library of ontology learning algorithms, we propose an evolutionary approach to automatically discover axioms from the abundant RDF data resource of the Semantic Web. We describe a method applying an instance of an Evolutionary Algorithm, namely Grammatical Evolution, to the acquisition of OWL class disjointness axioms, one important type of OWL axioms which makes it possible to detect logical inconsistencies and infer implicit information from a knowledge base. The proposed method uses an axiom scoring function based on possibility theory and is evaluated against a Gold Standard, manually constructed by knowledge engineers. Experimental results show that the given method possesses high accuracy and good coverage.

Agent-based recommender systems have been exploited in the last years to provide informative suggestions to users, showing the advantage of exploiting components like beliefs, goals and trust in the recommenda-tions' computation. However, many real-world scenarios, like the traffic one, require the additional feature of representing and reasoning about spatial and temporal knowledge, considering also their vague connotation. This paper tackles this challenge and introduces CARS, a spatio-temporal agent-based recommender system based on the Belief-Desire-Intention (BDI) architecture. Our approach extends the BDI model with spatial and temporal information to represent and reason about fuzzy beliefs and desires dynamics. An experimental evaluation about spatio-temporal reasoning in the traffic domain is carried out using the NetLogo platform, showing the improvements our recommender system introduces to support agents in achieving their goals.

In the Semantic Web context, OWL ontologies represent the conceptualization of domains of interest while the corresponding assertional knowledge is given by RDF data referring to them. Because of its open, distributed, and collaborative nature, such knowledge can be incomplete, noisy, and sometimes inconsistent. By exploiting the evidence coming from the assertional data, we aim at discovering hidden knowledge patterns in the form of multi-relational association rules while taking advantage of the intensional knowledge available in ontological knowledge bases. An evolutionary search method applied to populated ontological knowledge bases is proposed for finding rules with a high inductive power. The proposed method, EDMAR, uses problem-aware genetic operators, echoing the refinement operators of ILP, and takes the intensional knowledge into account, which allows it to restrict and guide the search. Discovered rules are coded in SWRL, and as such they can be straightforwardly integrated within the ontology, thus enriching its expressive power and augmenting the assertional knowledge that can be derived. Additionally, discovered rules may also suggest new axioms to be added to the ontology. We performed experiments on publicly available ontologies, validating the performances of our approach and comparing them with the main state-of-the-art systems.

The interpretation of a norm is often uncertain and conflicting. In this paper we propose a model for arguing about legal interpretation, which considers the problems of vagueness. After motivating our adoption of graded categories as a tool to tackle the problem of open texture in legal interpretation, we introduce a model based on fuzzy logic and argumentation. Then, we conduct a case study by using an example from medically assisted reproduction.

We study the feasibility of using Belief, Desire and Intention agents for modeling the phenomena of urban growth and segregation. Uncertainty, typical of real world situations is modeled using possibility theory. We have also implemented a simple visualization tool whose aim is to track the changes in the model. Some preliminary experiments suggest that such an approach might allow a decision-maker to dynamically track the changes in the model. Besides, it is also possible to interact with the different steps of the simulation via the model. Our proposal makes it possible to simulate the interactions between cognitive agents in an economical environment while taking the spatial context into account.

In this paper, we propose a multi-agent recommender system based on the Belief-Desire-Intention (BDI) model applied to multi-context systems. First, we extend the BDI model with additional contexts to deal with sociality and information uncertainty. Second, we propose an ontological representation of planning and intention contexts in order to reason about plans and intentions. Moreover, we show a simple real-world scenario in healthcare in order to illustrate the overall reasoning process of our model.

In this paper, a simulation of a multi-agent recommender system is presented and developed in the NetLogo platform. The specification of this recommender system is based on the well known Belief-Desire-Intention agent architecture applied to multi-context systems, extended with contexts for additional reasoning abilities, especially social ones. The main goal of this simulation study is, besides illustrating the usefulness and feasibility of our agent-based recommender system in a realistic scenario, to understand how groups of agents behave in a social network compared to individual agents. Results show that agents within a social network have better collective performance than individual ones. The utility and the satisfaction of agents is increased by the exchange of messages when executing intentions.

Axiom scoring is a critical task both for the automatic enrichment/learning and for the automatic validation of knowledge bases and ontologies. We designed and developed an axiom scoring heuristic based on possibility theory, which aims at overcoming some limitations of scoring heuristics based on statistical inference and taking into account the open-world assumption of the linked data on the Web. Since computing the possibilistic score can be computationally quite heavy for some candidate axioms, we propose a method based on time capping to alleviate the computation of the heuristic without giving up the precision of the scores. We evaluate our proposal by applying it to the problem of testing SubClassOf axioms against the DBpedia RDF dataset.

We propose syntactic deliberation and goal election algorithms for possibilistic agents which are able to deal with incomplete and imprecise information in a dynamic world. We show that the proposed algorithms are equivalent to their semantic counterparts already presented in the literature. We show that they lead to an efficient implementation of a possibilistic BDI model of agency which integrates goal generation.

We propose a syntactic possibilistic belief-change operator, which operates on a belief base of necessity-valued formulas. Such a base may be regarded as a finite and compact encoding of a possibility distribution over a possibly infinite set of inte

The aim of our research is to find an efficient solution to the services QoS optimization problem. This NP-hard problem is well known in the service-oriented computing field: given a business workflow that includes a set of abstract services and a set of concrete service implementations for each abstract service, the goal is to find the optimal combination of concrete services. The majority of recent proposals indicate the Genetic Algorithms (GA) as the best approach for complex workflows. But this problem usually needs to be solved at runtime, a task for which GA may be too slow. We propose a new approach, based on Differential Evolution (DE), that converges faster and it is more scalable and robust than the existing solutions based on Genetic Algorithms.

International audience

This work presents SimBa-2, an improved version of a novel crossover specifically adapted to the evolutionary optimization of neural network designs that aims at overcoming one of the major problems of recombination, known as the permutation problem. The crossover is based on a so-called `local similarity' between two individuals selected for the recombination process from the population, and it is applied according to a similarity threshold. An approach exploiting this operator has been implemented and applied to five benchmark classification problems in machine learning, chosen among some of the well known classification problems provided by the UCI Machine Learning Repository. The application of different similarity threshold values has been investigated and the experimental results show how the behavior of the operator changes with respect to this parameter.

The way in which the relationships between beliefs, goals, and intentions are captured by a formalism can have a significant impact on the design of a rational agent. In particular, what Rao and Georgeff underline about the relationships between goals and beliefs is that it is reasonable to require a rational agent not to allow goal-belief inconsistency, while goal-belief incompleteness can be allowed.

A rational agent adopts (or changes) its goals when new information (beliefs) becomes available or its desires (e.g., tasks it is supposed to carry out) change. In this paper we propose a non-conventional approach for adopting goals which takes the degree of trust in the sources of information into account. Beliefs, desires, and goals, as a consequence, are gradual. Incoming information may be any propositional formula.

We propose a supervised approach to word sense disambiguation based on neural networks combined with evolutionary algorithms. Large tagged datasets for every sense of a polysemous word are considered, and used to evolve an optimized neural network that correctly disambiguates the sense of the given word considering the context in which it occurs. The viability of the approach has been demonstrated through experiments carried out on a representative set of polysemous words.

This paper presents an approach to single-position, intraday automated trading based on a neuro-genetic algorithm.An artificial neural network is evolved which provides trading signals to a very unsophisticated automated trading agent.

No abstract available.

The task of reasoning with fuzzy description logics with fuzzy quantification is approached by means of an evolutionary algorithm. An essential ingredient of the proposed method is a heuristic, implemented as an intelligent mutation operator, which observes the evolutionary process and uses the information gathered to guess at the mutations most likely to bring about an improvement of the solutions. The viability of the method is demonstrated by applying it to reasoning on a resource sheduling problem.

Type-2 fuzzy sets, an elaboration over type-1 fuzzy sets, are an interesting method for handling uncertainty in rules and parameters in fuzzy systems. However, their adoption has not been as wide as one could have expected. In this paper we provide a simple introduction to type-2 fuzzy sets; then we propose a novel method for calculating operations on type-2 fuzzy sets with normal type-1 membership values, for which we redefine set ordering. Finally, based on the max ordering of fuzzy set and highest degree of separation, we propose an approximation for performing the operations, which ensures that the calculation is accurate for the most important parts of the membership values.

This paper presents an approach to the joint optimization of neural network structure and weights which can take advantage of backpropagation as a specialized decoder. The approach has been applied to a financial problem, whereby a factor model capturing the mutual relationships among several financial instruments is sought for. A sample application of such a model to statistical arbitrage is also presented. Categories and Subject Descriptors I.2.6 [Artificial Intelligence]: Learning—connectionism

We present discrete stochastic mathematical models for the growth curves of synchronous and synchronous evolutionary algorithms with populations structured ccording to a random graph. We show that, to good approximation, randomly structured and panmictic populations have the some growth behavior. Furthermore, we show that global selection intensity depends on the update policy. The validity of the models is confirmed by comparison with experimental results of simulations. We also present experimental results on small-world nd scale-free population graph topologies. We show that they lead to qualitatively similar results. However, the different nature of the nodes can be exploited to obtain more varied evolutionary behavior.

In this paper, we present quantitative models for the selection pressure of cellular evolutionary algorithms on regular one- and two-dimensional (2-D) lattices. We derive models based on probabilistic difference equations for synchronous and several asynchronous cell update policies. The models are validated using two customary selection methods: binary tournament and linear ranking. Theoretical results are in agreement with experimental values, showing that the selection intensity can be controlled by using different update methods. It is also seen that the usual logistic approximation breaks down for low-dimensional lattices and should be replaced by a polynomial approximation. The dependence of the models on the neighborhood radius is studied for both topologies. We also derive results for 2-D lattices with variable grid axes ratio.

Scheduling the production for plating machines is a tedious and difficult task of critical importance for the economic exploitation of this equipment. The paper describes a promising approach to solving a simple version of this problem, namely cyclical hoist scheduling, based on evolutionary algorithms. The issues of solution encoding and specialised genetic operators are discussed and some preliminary results are presented.

Parallel evolutionary algorithms, over the past few years, have proven empirically worthwhile, but there seems to be a lack of understanding of their workings. In this paper we concentrate on cellular (fine-grained) models, our objectives being: (1) to introduce a suite of statistical measures, both at the genotypic and phenotypic levels, which are useful for analyzing the workings of cellular evolutionary algorithms; and (2) to demonstrate the application and utility of these measures on a specific example-the cellular programming evolutionary algorithm. The latter is used to evolve solutions to three distinct (hard) problems in the cellular-automata domain: density, synchronization, and random number generation. Applying our statistical measures, we are able to identify a number of trends common to all three problems (which may represent intrinsic properties of the algorithm itself), as well as a host of problem-specific features. We find that the evolutionary algorithm tends to undergo a number of phases which we are able to quantitatively delimit. The results obtained lead us to believe that the measures presented herein may prove useful in the general case of analyzing fine-grained evolutionary algorithms.

Although genetic algorithms (GAs) are easy to implement and are powerful tools to solve difficult problems featuring huge search spaces, they usually require human supervision to be exploited successfully. It seems that fuzzy logic techniques can help reduce the amount of human intervention needed to use GAs. The paper concentrates on a particular application to symbolic regression to illustrate how to build a fuzzy knowledge-based system, or, to use a suggestive term, a fuzzy government, for GA control.>