International audience

International audience

Recruiting job candidates is a crucial activity for consulting companies to fulfill their consulting activities for customers or internal projects. Due to the complexity of the recruitment process, companies adopt standardized resume formats to be able to identify or compare candidates. However, this process requires significant involvement from human resource specialists and project leaders. In this paper, we present AI4HR Recruiter, a recommender system designed to improve the internal recruitment process of consultants. The system uses artificial intelligence-based techniques, specifically pre-trained sentence transformers. It aims at supporting recruiters in identifying and selecting candidates, rather than automating the process. We measure improvement in efficiency in terms of gains in recruiters time induced by the recommendation explanation presence. We also measure the impact on users’ satisfaction of two different types of algorithm to generate recommendations. To do so, we have been conducting a two fold study in a real-life setting for six months, where on the one hand, telemetry data was gathered while recommendation process user interaction was being performed and on the other hand, surveys about users’ satisfaction were conducted afterward. Our results evaluation demonstrates the effectiveness of the system in assisting recruiters in candidates selection process.

IoT applications can be naturally modeled as a graph where the edges represent the interactions between devices, sensors, and their environment. Thing'in11https://www.thinginthefuture.com/ is a platform, initiated by Orange22Orange is a French multinational telecommunication operator. The platform manages a graph of millions of connected and non-connected objects using a commercial graph database. The graph of Thing'in is dynamic because loT devices create temporary connections between each other. Analyzing the history of these connections paves the way to new promising applications such as object tracking, anomaly detection, and forecasting the future behavior of devices. However, existing com-mercial graph databases are not designed with native temporal support which limits their usability in such use cases. In this paper, we discuss the design of a temporal graph management system Clock-G and introduce a new space-efficient storage technique δ-Copy+Log, Clock-G is designed by the devel-opers of the Thing'in platform and is currently being deployed into production. It differentiates from existing temporal graph management systems by adopting the δ-Copy+Log technique. This technique targets the mitigation of the apparent trade-off between the conflicting goals of the reduction of space usage and acceleration of query execution time. Our experimental results demonstrate that the δ-Copy+Log presents an overall better performance as compared to traditional storage methods in terms of space usage and query evaluation time.

Agglomerative clustering methods have been widely used by many research communities to cluster their data into hierarchical structures. These structures ease data exploration and are understandable even for non-specialists. But these methods necessarily result in a tree, since, at each agglomeration step, two clusters have to be merged. This may bias the data analysis process if, for example, a cluster is almost equally attracted by two others. In this paper we propose a new method that allows clusters to overlap until a strong cluster attraction is reached, based on a density criterion. The resulting hierarchical structure, called a quasi-dendrogram, is represented as a directed acyclic graph and combines the advantages of hierarchies with the precision of a less arbitrary clustering. We validate our work with extensive experiments on real data sets and compare it with existing tree-based methods, using a new measure of similarity between heterogeneous hierarchical structures.

Data models capture the structure and characteristic properties of data entities, e.g., in terms of a database schema or an ontology. They are the backbone of diverse applications, reaching from information integration , through peer-to-peer systems and electronic commerce to social networking . Many of these applications involve models of diverse data sources. Effective utilisation and evolution of data models, therefore, calls for matching techniques that generate correspondences between their elements. Various such matching tools have been developed in the past. Yet, their results are often incomplete or erroneous, and thus need to be reconciled, i.e., validated by an expert. This paper analyses the reconciliation process in the presence of large collections of data models, where the network induced by generated correspondences shall meet consistency expectations in terms of integrity constraints. We specifically focus on how to handle data models that show some internal structure and potentially differ in terms of their assumed level of abstraction. We argue that such a setting calls for a probabilistic model of integrity constraints, for which satisfaction is preferred, but not required. In this work, we present a model for probabilistic constraints that enables reasoning on the correctness of individual correspondences within a network of data models, in order to guide an expert in the validation process. To support pay-as-you-go reconciliation, we also show how to construct a set of high-quality correspondences, even if an expert validates only a subset of all generated correspondences. We demonstrate the efficiency of our techniques for real-world datasets comprising database schemas and ontologies from various application domains.

Besides the simple human intelligence tasks such as image labeling, crowdsourcing platforms propose more and more tasks that require very specific skills, especially in participative science projects. In this context, there is a need to reason about the required skills for a task and the set of available skills in the crowd, in order to increase the resulting quality. Most of the existing solutions rely on unstructured tags to model skills (vector of skills). In this paper we propose to finely model tasks and participants using a skill tree, that is a taxonomy of skills equipped with a similarity distance within skills. This model of skills enables to map participants to tasks in a way that exploits the natural hierarchy among the skills. We illustrate the effectiveness of our model and algorithms through extensive experimentation with synthetic and real data sets.

Schema matching supports data integration by establishing correspondences between the attributes of independently designed database schemas. In recent years, various tools for automatic pair-wise matching of schemas have been developed. Since the matching process is inherently uncertain, the correspondences generated by such tools are often validated by a human expert. In this work, we consider scenarios in which attribute correspondences are identified in a network of schemas and not only in a pairwise setting. Here, correspondences between different schemas are interrelated, so that incomplete and erroneous matching results propagate in the network and the validation of a correspondence by an expert has ripple effects. To analyse and reconcile such matchings in schema networks, we present the Schema Matching Analyzer and Reconciliation Tool (SMART). It allows for the definition of network-level integrity constraints for the matching and, based thereon, detects and visualizes inconsistencies of the matching. The tool also supports the reconciliation of a matching by guiding an expert in the validation process and by offering semi-automatic conflict-resolution techniques.

Schema matching is the process of establishing correspondences between the attributes of database schemas for data integration purposes. Although several automatic schema matching tools have been developed, their results are often incomplete or erroneous. To obtain a correct set of correspondences, a human expert is usually required to validate the generated correspondences. We analyze this reconciliation process in a setting where a number of schemas needs to be matched, in the presence of consistency expectations about the network of attribute correspondences. We develop a probabilistic model that helps to identify the most uncertain correspondences, thus allowing us to guide the expert's work and collect his input about the most problematic cases. As the availability of such experts is often limited, we develop techniques that can construct a set of good quality correspondences with a high probability, even if the expert does not validate all the necessary correspondences. We demonstrate the efficiency of our techniques through extensive experimentation using real-world datasets.

TweetSpector is a tool for demonstrating entity-based of retrieval of tweets. The various features of this tool include: entity profile creation, real-time tweet classification, active improvement of the created profiles through user feedback, and the dashboard displaying different metrics.

The generalized hypertree width GHW( H ) of a hypergraph H is a measure of its cyclicity. Classes of conjunctive queries or constraint satisfaction problems whose associated hypergraphs have bounded GHW are known to be solvable in polynomial time. However, it has been an open problem for several years if for a fixed constant k and input hypergraph H it can be determined in polynomial time whether GHW( H ) ≤ k . Here, this problem is settled by proving that even for k = 3 the problem is already NP-hard. On the way to this result, another long standing open problem, originally raised by Goodman and Shmueli [1984] in the context of join optimization is solved. It is proven that determining whether a hypergraph H admits a tree projection with respect to a hypergraph G is NP-complete. Our intractability results on generalized hypertree width motivate further research on more restrictive tractable hypergraph decomposition methods that approximate generalized hypertree decomposition (GHD). We show that each such method is dominated by a tractable decomposition method definable through a function that associates a set of partial edges to a hypergraph. By using one particular such function, we define the new Component Hypertree Decomposition method, which is tractable and strictly more general than other approximations to GHD published so far.

The generalized hypertree width GHW(H) of a hypergraph H is a measure of its cyclicity. Classes of conjunctive queries or constraint satisfaction problems whose associated hypergraphs have bounded GHW are known to be solvable in polynomial time. However,it has been an open problem for several years if for a fixed constant k and input hypergraph H it can be determined in polynomial time whether GHW(H)< k. Here, this problem is settled by proving that even for k=3 the problem is already NP-hard. On the way to this result, another long standing open problem, originally raised by Goodman and Shmueli in 1984 all in the context of join optimization is solved. It is proven that determining whether a hypergraph H admits a tree projection with respect to a hypergraph G is NP-complete. Our intractability results on generalized hypertree width motivate further research on more restrictive tractable hypergraph decomposition methods that approximate general hypertree decomposition (GHD). We show that each such method is dnominated by a tractable decomposition method definable through a function that associates a set of partial edges to a hypergraph. By using one particular such function, we define the new Component Hypertree Decomposition method, which is tractable and strictly more general than other approximations to GHD published so far.

This case study summarizes the demonstration of a semantic network of interoperable educational systems referred to as Smart Space for Learningtrade. We started connecting several educational nodes in projects such as Elena, Prolearn, and Icamp. Integration was achieved by using the interaction standard SQI, common schemas for querying and results presentation, and query exchange language, e.g. QEL. The paper particularly focuses on how heterogeneous nodes can be made interoperable by reusing generalizations of mediating components - building blocks for a Smart Space for Learningtrade