2025​​Activity reportProject-TeamCEDAR​​​‌

7.1.1 ConnectionLens

• Name:
  Integration‌ of heterogeneous data using‌​‌ information extraction
• Keyword:
  Data​​ analysis
• Functional Description:
  ConnectionLens​​​‌ treats a set of‌ heterogeneous, independently authored data‌​‌ sources as a single​​ virtual graph, whereas nodes​​​‌ represent fine-granularity data items‌ (relational tuples, attributes, key-value‌​‌ pairs, RDF, JSON or​​ XML nodes…) and edges​​​‌ correspond either to structural‌ connections (e.g., a tuple‌​‌ is in a database,​​ an attribute is in​​​‌ a tuple, a JSON‌ node has a parent…)‌​‌ or to similarity (sameAs)​​ links. To further enrich​​​‌ the content journalists work‌ with, we also apply‌​‌ entity extraction which enables​​ to detect the people,​​​‌ organizations etc. mentioned in‌ text, whether full-text or‌​‌ text snippets found e.g.​​​‌ in RDF or XML.​ ConnectionLens is thus capable​‌ of finding and exploiting​​ connections present across heterogeneous​​​‌ data sources without requiring​ the user to specify​‌ any join predicate.
• URL:​​
  https://­team.­inria.­fr/­cedar/­connectionlens/
• Publications:
  hal-02934277,​​​‌ hal-02904797, hal-01841009
• Contact:​
  Manolescu Ioana

7.1.2 Abstra​‌

• Name:
  Abstra: Toward Generic​​ Abstractions for Data of​​​‌ Any Model
• Keywords:
  Heterogeneous​ Data, Data Exploration, Data​‌ analysis, Databases, LOD -​​ Linked open data
• Functional​​​‌ Description:
  Abstra computes a​ description meant for humans,​‌ based on the idea​​ that, regardless of the​​​‌ syntax or the data​ model, any dataset holds​‌ some collections of entities/records,​​ that are possibly linked​​​‌ with relationships. Abstra relies​ on a common graph​‌ representation of any incoming​​ dataset, it leverages Information​​​‌ Extraction to detect what​ the dataset is about,​‌ and relies on an​​ original algorithm for selecting​​​‌ the core entity collections​ and their relations. Abstractions​‌ are shown both as​​ HTML text and a​​​‌ lightweight Entity-Relationship diagram.
• URL:​
  https://­team.­inria.­fr/­cedar/­projects/­abstra/
• Publications:
  hal-04131974,​‌ hal-04131974, hal-03767967,​​ hal-03774599
• Contact:
  Madhulika Mohanty​​​‌
• Participants:
  Ioana Manolescu Goujot,​ Madhulika Mohanty, Nelly Barret,​‌ Prajna Devi Upadhyay

7.1.3​​ StatCheck

• Name:
  Fact-checking Multidimensional​​​‌ Statistic Claims in French​
• Keywords:
  Machine learning, Databases,​‌ Natural language processing, Software​​ engineering
• Scientific Description:
  To​​​‌ strengthen public trust and​ counter disinformation, computational fact-checking,​‌ leveraging digital data sources,​​ attracts interest from the​​​‌ journalists and the computer​ science community. A particular​‌ class of interesting data​​ sources comprises statistics, that​​​‌ is, numerical data compiled​ mostly by governments, administrations,​‌ and international organizations. Statistics​​ are often multidimensional datasets,​​​‌ where multiple dimensions characterize​ one value, and the​‌ dimensions may be organized​​ in hierarchies. This paper​​​‌ describes STATCHECK, a statistic​ fact-checking system jointly developed​‌ by the authors, which​​ are either computer science​​​‌ researchers or fact-checking journalists​ working for a French-language​‌ media with a daily​​ audience of more than​​​‌ 15 millions (aud, 2022).​ The technical novelty of​‌ STATCHECK is twofold: (i)​​ we focus on multidimensional,​​​‌ complex-structure statistics, which have​ received little attention so​‌ far, despite their practical​​ importance, and (ii) novel​​​‌ statistical claim extraction modules​ for French, an area​‌ where few resources exist.​​ We validate the efficiency​​​‌ and quality of our​ system on large statistic​‌ datasets (hundreds of millions​​ of facts), including the​​​‌ complete INSEE (French) and​ Eurostat (European Union) datasets,​‌ as well as French​​ presidential election debates.
• Functional​​​‌ Description:
  StatCheck firstly allows​ the collection of data​‌ for its operation. Two​​ types of data are​​​‌ collected: statistical tables and​ posts from social networks:​‌ - Acquisition of statistical​​ files on the site​​​‌ of referent organisations (INSEE,​ Eurostat) - Extraction of​‌ statistical tables from these​​ files, and storage of​​​‌ the extracted tables -​ Acquisition of political tweets​‌ from a list of​​ accounts The application allows​​​‌ the detection, extraction and​ search of statistical facts:​‌ - Detection and extraction​​ of statistical facts from​​​‌ Twitter posts (e.g. "Unemployment​ rate increased by 30%​‌ in 2023) - Search​​ for statistical facts in​​​‌ our database. Display of​ the twenty most relevant​‌ statistical tables for a​​ statistical fact - Automatic​​ transcription of audio files​​​‌ to detect and extract‌ transcripts of statistical facts.‌​‌
• Release Contributions:
  - Redesign​​ of the user interface​​​‌ - Modification of the‌ software architecture - Addition‌​‌ of audio transcription
• URL:​​
  https://­cedar-rf.­saclay.­inria.­fr/
• Publications:
  hal-01496700,​​​‌ hal-01745768, hal-02121389,‌ hal-01915148, hal-03767992,‌​‌ hal-03791175
• Contact:
  Ioana Manolescu​​ Goujot
• Participants:
  Antoine Gauquier,​​​‌ Tien Duc Cao, Ioana‌ Manolescu Goujot, Xavier Tannier,‌​‌ Oana-Denisa Balalau, Simon Ebel,​​ Theo Galizzi

7.1.4 ConnectionStudio​​​‌

• Keywords:
  Heterogeneous Data, Data‌ Exploration
• Functional Description:

  ConnectionStudio‌​‌ integrates highly heterogeneous data​​ into graphs, enriched with​​​‌ extracted entities. Studio users‌ can discover the entities‌​‌ in their data, navigate​​ across connections between datasets,​​​‌ explore and query the‌ data in many ways.‌​‌ The Studio currently supports:​​ CSV, JSON, XML, RDF,​​​‌ text, property graphs, all‌ Office formats, and PDF‌​‌ datasets.

  ConnectionStudio is a​​ novel front-end to ConnectionLens,​​​‌ Abstra and PathWays (see‌ also the respective Web‌​‌ sites). Its own novel​​ features are outlined in​​​‌ a CoopIS 2023 article.‌

• URL:
  https://­connectionstudio.­inria.­fr/
• Publications:
  hal-04185938‌​‌, hal-04591897, hal-04727209​​
• Contact:
  Ioana Manolescu Goujot​​​‌
• Participants:
  Madhulika Mohanty, Simon‌ Ebel, Theo Galizzi

7.1.5‌​‌ FactSpotter

• Keywords:
  Factual Faithfulness,​​ Text generation
• Functional Description:​​​‌
  We propose a new‌ metric that correctly identifies‌​‌ factual faithfulness, i.e., given​​ a triple (subject, predicate,​​​‌ object), it decides if‌ the triple is present‌​‌ in a generated text.​​ We show that our​​​‌ metric FactSpotter achieves the‌ highest correlation with human‌​‌ annotations on data correct-​​ ness, data coverage, and​​​‌ relevance. In addition, FactSpotter‌ can be used as‌​‌ a plug-in feature to​​ improve the factual faithfulness​​​‌ of existing models.
• Contact:‌
  Kun Zhang
• Partner:
  Ecole‌​‌ Polytechnique

7.1.6 PathWays

• Name:​​
  PathWays: finding entity paths​​​‌ in heterogeneous data graphs‌
• Keywords:
  Named entities, Data‌​‌ Journalism, Heterogeneous Data
• Functional​​ Description:
  PathWays models heteroegenous​​​‌ datasets in a graph‌ (see ConnectionLens). To identify‌​‌ interesting paths in this​​ graph, Pathways works on​​​‌ its (smaller) summary (see‌ Abstra) for efficiency and‌​‌ optimisation. Then, it sorts​​ paths by their potential​​​‌ interest (metric based on‌ the entity found and‌​‌ the information diluation along​​ the path) before evaluating​​​‌ them with the help‌ of a new multi-query‌​‌ optimisation algorithm. Finally, PathWays​​ shows the most interesting​​​‌ (evaluated) paths in the‌ form of tables, wich‌​‌ are very easy to​​ understanf for journalists who​​​‌ are at the initiative‌ of this scenario.
• URL:‌​‌
  https://­team.­inria.­fr/­cedar/­projects/­pathways/
• Publications:
  hal-04131977,​​ hal-04727209, hal-04131977
• Contact:​​​‌
  Ioana Manolescu Goujot

7.1.7‌ OpenIEEntity

• Name:
  Open Information‌​‌ Extraction with Entity Focused​​ Constraints
• Keyword:
  Information extraction​​​‌
• Functional Description:
  This tool‌ takes in input a‌​‌ sentence and outputs the​​ facts contained in the​​​‌ sentence, in the format‌ (subject,predicate,object).
• Contact:
  Oana-Denisa Balalau‌​‌

7.1.8 FactCheckBureau

• Name:
  FactCheckBureau:​​ Build Your Own Fact-Check​​​‌ Analysis Pipeline
• Keywords:
  Fact‌ Check Retireval, Fact-checking
• Functional‌​‌ Description:
  FactCheckBurea is an​​ end-to-end solution that enables​​​‌ researchers to easily and‌ interactively design and evaluate‌​‌ Fact Check retrieval pipelines.​​ Further, it provides a​​​‌ query interface for non-technical‌ users to find relevant‌​‌ Fact Checks for the​​ input query in the​​​‌ form of a key‌ phrase, social media post,‌​‌ or an image.
• URL:​​​‌
  https://­gitlab.­inria.­fr/­cedar/­factcheckbureau
• Publication:
  hal-04684068
• Contact:​
  Ioana Manolescu Goujot

7.1.9​‌ FDSpotter

• Name:
  Structured Discourse​​ Representation for Factual Consistency​​​‌ Verification
• Keyword:
  LLM
• Functional​ Description:
  The repository includes​‌ the tool to test​​ for factual consistency, but​​​‌ also all the code​ necessary to compare our​‌ tool with state of​​ the art methods for​​​‌ factual consistency.
• Contact:
  Oana-Denisa​ Balalau

7.1.10 COI-OpenIE

• Keywords:​‌
  Conflict Of Interest Mining,​​ Knowledge graph, Scientific Text,​​​‌ Information extraction
• Functional Description:​
  This software expects as​‌ input a collection of​​ certain sections (Acknowledgment, Funding​​​‌ disclosure, and so on)​ of scientific publications, and​‌ produces a knowledge graph​​ that has information about​​​‌ the different interesting relations​ among Individuals and Organizations​‌ that were present in​​ the input text corpus.​​​‌
• Contact:
  Oana-Denisa Balalau

7.1.11​ ClimateNLP toolbox

• Name:
  Climate​‌ NLP toolbox
• Keywords:
  Climate​​ change, Classification, Natural language​​​‌ processing
• Functional Description:
  Python​ Scripts to train or​‌ download models (BERT-based models,​​ TF-IDF). It also contains​​​‌ scripts to run LLM​ pipelines to perform the​‌ same tasks.
• Contact:
  Tom​​ Calamai

7.1.12 MultilingualPoliticalLLMs

• Keywords:​​​‌
  LLM, Multilingual
• Functional Description:​
  We test different scenarios,​‌ where we vary the​​ language of the prompt​​​‌ while also assigning a​ nationality to the model.​‌ We evaluate models on​​ the 50 most populous​​​‌ countries and their official​ languages.
• URL:
  https://­github.­com/­ChadiHelwe/­navigating_the_political_compass
• Contact:​‌
  Oana-Denisa Balalau

8.1.1 Graph​‌ data lakes of heterogeneous​​ data sources for data​​​‌ journalism

Participants: Oana-Denisa Balalau​, Pablo Bertaud-Velten,​‌ Nikola Dobricic, Przemyslaw​​ Dominikowski, Simon Ebel​​​‌, Theo Galizzi,​ Garima Gaur, Ioana​‌ Manolescu, Maria Jesus​​ Mellado Tenorio, Madhulika​​​‌ Mohanty, Saba Shahsavari​, Georgios Siachamis.​‌

Work carried within the​​ ANR AI Chair SourcesSay​​​‌ project has focused on​ developing a platform, ConnectionLens,​‌ for integrating arbitrary heterogeneous​​ data into a graph,​​​‌ then exploring and querying​ that graph using simple,​‌ intuitive query interfaces. The​​ main technical challenges addressed​​​‌ were: (i) how to​ interconnect structured and semi-structured​‌ data sources? We address​​ this through information extraction​​​‌ (when an entity appears​ in two data sources​‌ or two places in​​ the same graph, we​​​‌ only create one node,​ thus interlinking the two​‌ locations) and through similarity​​ comparisons7.1.1; (ii)​​​‌ how to find all​ connections between nodes matching​‌ specific search criteria, or​​ certain keywords? The question​​​‌ is particularly challenging in​ our context since ConnectionLens​‌ graphs can be pretty​​ large, and query answers​​​‌ can traverse edges in​ both directions(iii) how to​‌ convert this graph into​​ standard graph data models​​​‌ like property graphs, etc.​ ConnectionLens is available online​‌ at: ConnectionLens Gitlab repository​​, while ConnectionStudio, its​​​‌ GUI, is available at​ ConnectionStudio Gitlab repository.​‌

With the ANR TopOL​​ project, we now extend​​​‌ our contributions to large​ scale data lakes of​‌ heterogeneous data sources and​​ explore novel ways of​​​‌ exploration. In this context,​ the following new contributions​‌ have been brought:

1. Efficiently​​ Profiling, Indexing and Querying​​​‌ Heterogeneous Datasets in Graph​ Data Lakes Building on​‌ the ConnectionLens 7.1.1 and​​ Abstra 7.1.2 frameworks, this​​ work focuses on enabling​​​‌ natural language question answering‌ over large-scale heterogenous data‌​‌ lake. In each dataset,​​ we have formalized the​​​‌ concept of entities and‌ their contexts, which serve‌​‌ as natural "anchors" of​​ users' questions, e.g. which​​​‌ Person interacted with which‌ Organization, and at what‌​‌ Location. To support efficient​​ search over the set​​​‌ of entities-in-context, we developed‌ an end-to-end system that‌​‌ ingests heterogenous sources into​​ a graph data lake​​​‌ (using ConnectionLens), abstracts them‌ into collections (using Abstra)‌​‌ and finally builds and​​ indexes the entities-in-context. The​​​‌ developed indexes include: Locality‌ Sensitive Hashing (LSH) for‌​‌ semantic similarity search and​​ TRIE-like structure for exact​​​‌ lookups.

   This work provides‌ a foundation for the‌​‌ future work (e.g. building​​ Retrieval-Augmented Generation system) allowing​​​‌ non-technical users like journalists‌ to uncover the interesting‌​‌ facts over the large​​ heterogenous data lakes, in​​​‌ particular in domains such‌ as investigative journalism (with‌​‌ the team's ongoing collaboration​​ with ICIJ).

2. Batch Generic​​​‌ Evaluation of Keyword Queries‌ on Graphs Keyword search‌​‌ is a popular paradigm​​ for searching for information​​​‌ in graphs: users specify‌ a few search terms‌​‌ (or keywords), and the​​ system returns subtrees of​​​‌ the graph, where each‌ keyword is matched by‌​‌ a node in each​​ returned subtree. Because the​​​‌ problem is NP-hard in‌ general, many keyword search‌​‌ algorithms consider a fixed​​ score function which is​​​‌ applied to rank result‌ trees, and explore only‌​‌ part of the search​​ space, pruning trees with​​​‌ low scores. In contrast,‌ generic algorithms explore the‌​‌ complete search space (subject​​ to space or time​​​‌ limits, due to the‌ high complexity), but can‌​‌ be used with any​​ score function. In this​​​‌ work, we consider the‌ problem of simultaneously answering‌​‌ a set (batch) of​​ keyword queries, in a​​​‌ way compatible with any‌ score function. Building upon‌​‌ our recent one-query generic​​ algorithm 36, we​​​‌ show that when graph‌ nodes match keywords from‌​‌ multiple queries, graph exploration​​ effort can be shared,​​​‌ to speed up the‌ evaluation of the query‌​‌ batch. We formally establish​​ guarantees on the correctness​​​‌ and completess of our‌ algorithm, and demonstrate its‌​‌ efficiency through comprehensive experiments​​ over synthetic and real-world​​​‌ graphs.
3. Named Entity Cleaning‌ and Enhancement with Human-in-the-loop‌​‌ Named Entities (NEs, in​​ short) are frequently encountered​​​‌ in datasets about varied‌ topics, e.g., journalistic investigations‌​‌ (people, places, companies), market​​ analysis (companies and officers),​​​‌ etc. NEs often appear‌ under different forms within‌​‌ or across datasets, due​​ to spelling variants or​​​‌ mistakes. To leverage NE-rich‌ datasets, the NEs need‌​‌ to be clean (error-free),​​ and possibly enriched with​​​‌ information from external sources.‌ While numerous data cleaning‌​‌ solutions exist, in this​​ work, we focus on​​​‌ the specific challenges raised‌ by the cleaning of‌​‌ NE sets, in particular​​ ($i$) through​​​‌ a visual workflow interface,‌ ($ii$)‌​‌ leveraging old and new​​ techniques (string distances, Knowledge​​​‌ Bases, and carefully controlled‌ access to LLMs), and‌​‌ especially ($i\mathrm{i}i$) enabling human​​​‌ inspection and interaction with‌ the NE cleaning process,‌​‌ down to the granularity​​​‌ of an individual attribute​ of a record. The​‌ latter need is crucial​​ in order to capture​​​‌ advanced knowledge that only​ domain experts have, and​‌ which may be absent​​ from all other sources​​​‌ of information (KB, LLM,​ etc.) We support this​‌ by gathering how-provenance that​​ traces the numerous ways​​​‌ in which information is​ brought to clean NEs.​‌ We built NiceT, a​​ system addressing these challenges,​​​‌ and tested on a​ variety of real-life datasets.​‌

4. Named Entity Centric Querying​​ over Heterogeneous Data Integrating​​​‌ information from diverse sources,​ particularly in investigative journalism,​‌ often hinges on linking​​ data through shared named​​​‌ entities (NEs). The same​ entity may appear across​‌ multiple sources, each providing​​ a different contextual perspective.​​​‌ For instance, when combining​ U.S. financial and political​‌ datasets, Donald Trump may​​ emerge as a common​​​‌ entity, associated with distinct​ roles such as businessperson​‌ and politician. From a​​ journalistic standpoint, the ability​​​‌ to seamlessly integrate heterogeneous​ data sources and query​‌ entity roles or inter-entity​​ relationships—without requiring advanced technical​​​‌ expertise—is critical.

   This project,​ centered on the problem​‌ of extracting and integrating​​ information about a named​​​‌ entity (NE) that may​ appear across heterogeneous datasets​‌ within a datalake, gives​​ rise to two concrete​​​‌ research challenges. First, given​ a input NE, identify​‌ the roles (context) it​​ plays across different datasets​​​‌ and aggregate relevant information​ about the NE. We​‌ refer to the aggregated​​ output as the Infocard​​​‌ of the NE. Second,​ given a collection of​‌ heterogeneous datasets and an​​ NE, find its interesting​​​‌ relationships with the other​ Named entities. We leverage​‌ the capabilities of our​​ in-house tools, ConnectionLens and​​​‌ Abstra, that can integrate​ structured, semi-structured, and unstructured​‌ datasets into a unified​​ graph, and create high-level​​​‌ semantic abstractions of the​ complex datasets.

8.1.2 RDF​‌ Query Answering in the​​ Presence of Access Restrictions​​​‌

Participants: Maxime Buron,​ Hritika Kathuria, Ioana​‌ Manolescu, Georgios Siachamis​​.

In this work,​​​‌ we explore algorithms for​ answering conjunctive RDF queries​‌ in the presence of​​ RDFS ontologies and access​​​‌ control. We consider an​ access control setting where​‌ by default all users​​ have access to the​​​‌ complete graph, and a​ restriction can forbid user​‌ a user's access to​​ specific IRIs. Here, restricting​​​‌ for user $u$ the​ access to an IRI​‌ $i$ entails that: no​​ answer to a query​​​‌ by $u$ may contain​ the IRI $i$;​‌ no triple containing $\mathrm{i}$ can be used to​​​‌ compute an answer for​ a query by $\mathrm{i‌}$, nor to entail​​ such a triple via​​​‌ reasoning with the ontology.​ We present a set​‌ of query answering algorithms​​ for this novel context,​​​‌ and prove that five​ among them are correct,​‌ i.e., sound and complete,​​ with respect to both​​​‌ the ontology and the​ access restrictions in place.​‌ We have implemented all​​ our algorithms and present​​​‌ experiments comparing their performance.​ This work was published​‌ in CoopIS 2025 28​​ where it won the​​​‌ Best Paper Award.

8.1.3​ FactCheck-KG: Towards LLM-backed FC​‌ Retrieval

Participants: Garima Gaur​​, Madhulika Mohanty.​​

There is an unprecedented​​​‌ rise in the volume‌ and reach of disinformation‌​‌ due to the popularity​​ of social media and​​​‌ the advent of generative‌ AI models. Fact-checking, that‌​‌ is, checking the veracity​​ of a certain claim,​​​‌ is unfeasible at this‌ scale, by human effort‌​‌ alone. This is primarily​​ due to the rise​​​‌ in the volume of‌ claims requiring verification, and‌​‌ also the number of​​ documents to be processed​​​‌ to verify a certain‌ claim. This process is‌​‌ further complicated by disinformation​​ re-surfacing in paraphrased forms,​​​‌ altered context, incomplete, or‌ shifted context. The fact-checkers‌​‌ often find themselves re-assessing​​ a previously evaluated claim,​​​‌ which wastes precious human‌ effort. In order to‌​‌ tackle these challenges, fact-check​​ retrieval(FCR) pipelines have been​​​‌ developed that, given a‌ newly encountered claim, aim‌​‌ to identify the most​​ relevant claims among a​​​‌ set of previously assessed‌ claims. In this work,‌​‌ we leverage NLP techniques​​ over a set of​​​‌ fact-checked claims and their‌ related articles, to build‌​‌ a Knowledge Graph (KG)​​ FactCheck-KG of named entities,​​​‌ topics, claims and articles‌ with edges capturing the‌​‌ connection across different fact-checks​​ via common topics and​​​‌ named entities. This representation‌ lays the foundation for‌​‌ more context-aware, fine-grained fact-check​​ retrieval. For example, with​​​‌ the success of retrieval‌ augmented generation (RAG) and‌​‌ its extension to a​​ Graph-based retrieval(GraphRAG) framework, our​​​‌ KG can form a‌ starting point for its‌​‌ application to solve the​​ fact-check retrieval problem.

8.1.4​​​‌ Efficient and Scalable Search‌ for Statistics

Participants: Simon‌​‌ Ebel, Helena Galhardas​​, Theo Galizzi,​​​‌ Ioana Manolescu, Aurelien‌ Peden.

Informed public‌​‌ debate needs high-quality data.​​ In this context, high-quality​​​‌ statistical data sources are‌ a valuable category of‌​‌ reference information based on​​ which a claim can​​​‌ be checked. To facilitate‌ the work of journalists‌​‌ or other fact-checkers, users'​​ questions about a specific​​​‌ claim should be automatically‌ answered based on statistical‌​‌ tables. This task is​​ complicated by the large​​​‌ number, size, and variety‌ of statistical datasets. This‌​‌ work introduces the statistical​​ table discovery problem (STD,​​​‌ in short), which aims,‌ given a natural language‌​‌ question and a set​​ of statistic datasets (multidimensional​​​‌ tables), to find the‌ tables most relevant for‌​‌ the question. We then​​ describe STAR, an algorithm​​​‌ for solving the STD‌ problem. Unlike existing table‌​‌ discovery (TD) solutions aimed​​ at relational tables, STAR​​​‌ is devised specifically for‌ multidimensional ones. Further, STAR‌​‌ treats the space and​​ time dimensions of statistical​​​‌ datasets separately. We experimentally‌ show that these features,‌​‌ together, make STAR outperform​​ state-of-the-art TD systems adapted​​​‌ to the STD problem,‌ in terms of scalability,‌​‌ search quality, preprocessing and​​ question answering time. It​​​‌ has been informally presented‌ at BDA 2025 19‌​‌ and the code is​​ available at its Gitlab​​​‌ repository.

8.1.5 Structured‌ Discourse Representation for Factual‌​‌ Consistency Verification

Participants: Oana-Denisa​​ Balalau, Ioana Manolescu​​​‌, Kun Zhang.‌

Analysing the differences in‌​‌ how events are represented​​ across texts, or verifying​​​‌ whether the language model‌ generations hallucinate, requires the‌​‌ ability to systematically compare​​​‌ their content. To support​ such a comparison, a​‌ structured representation that captures​​ fine-grained information plays a​​​‌ vital role. In particular,​ identifying distinct atomic facts​‌ and the discourse relations​​ connecting them enables deeper​​​‌ semantic comparison. Our proposed​ approach combines structured discourse​‌ information extraction with a​​ classifier, FDSpotter, for factual​​​‌ consistency verification. We show​ that adversarial discourse relations​‌ pose challenges for language​​ models, but fine-tuning on​​​‌ our annotated data, DiscInfer,​ achieves competitive performance. Our​‌ proposed approach advances factual​​ consistency verification by grounding​​​‌ in linguistic structure and​ decomposing it into interpretable​‌ components. We demonstrate the​​ effectiveness of our method​​​‌ on the evaluation of​ two tasks: data-to-text generation​‌ and text summarisation. This​​ work has been published​​​‌ in ACL (Findings) 2025​ 27 and the software​‌ is available on BIL​​ 7.1.9.

8.1.6 The​​​‌ Search for Conflicts of​ Interest: Open Information Extraction​‌ in Scientific Publications

Participants:​​ Oana-Denisa Balalau, Garima​​​‌ Gaur, Ioana Manolescu​, Prajna Upadhyay.​‌

A conflict of interest​​ (COI) appears when a​​​‌ person or a company​ has two or more​‌ interests that may directly​​ conflict. This happens, for​​​‌ instance, when a scientist​ whose research is funded​‌ by a company audits​​ the same company. For​​​‌ transparency and to avoid​ undue influence, public repositories​‌ of relations of interest​​ are increasingly recommended or​​​‌ mandated in various domains,​ and can be used​‌ to avoid COIs. In​​ this work, we propose​​​‌ an LLM-based open information​ extraction (OpenIE) framework for​‌ extracting financial or other​​ types of interesting relations​​​‌ from scientific text. We​ target scientific publications in​‌ which authors declare funding​​ sources or collaborations in​​​‌ the acknowledgment section, in​ the metadata, or in​‌ the publication, following editors’​​ requirements. We introduce an​​​‌ extraction methodology and present​ a knowledge base (KB)​‌ with a comprehensive taxonomy​​ of COI centric relations.​​​‌ Finally, we perform a​ comparative study of disclosures​‌ of two journals in​​ the field of toxicology​​​‌ and pharmacology. The work​ has been published in​‌ EMNLP (Findings) 2025 20​​ and the software is​​​‌ available on BIL 7.1.10​.

8.2.1 A Year​​​‌ Under the DSA: Ad​ Transparency's Uneven Landscape

The​‌ Digital Services Act (DSA)​​ has put platform accountability​​​‌ on center stage, requiring​ online platforms to provide​‌ greater transparency into how​​ advertisements are targeted and​​​‌ delivered to users. Central​ to these obligations are​‌ two mechanisms: user-facing ad​​ explanations, which inform individuals​​​‌ why they were shown​ a given ad, and​‌ public ad repositories, which​​ are intended to enable​​​‌ independent auditing of advertising​ practices. This study provides​‌ the first multi-platform evaluation​​ of these two mechanisms​​​‌ across Facebook, Instagram, YouTube​ and X. Using 48,511​‌ user-facing “Why am I​​ seeing this ad?” (WAIST)​​​‌ notices, and a systematic​ analysis of each platform's​‌ public ad repository, we​​ assess how well current​​​‌ implementations disclose the parameters​ and decision processes involved​‌ in targeting. To do​​ so, we develop and​​ apply an operational framework​​​‌ based on Articles 26‌ and 39 of the‌​‌ DSA—capturing the granularity, attribution​​ of targeting and delivery​​​‌ choices, data source disclosures,‌ and accuracy—and apply it‌​‌ across both user-facing notices​​ and public ad repositories.​​​‌ Our findings show that‌ transparency remains fragmented and‌​‌ inconsistent across platforms. User-facing​​ explanations vary widely in​​​‌ precision and often omit‌ key targeting information, while‌​‌ repositories provide incomplete, misattributed,​​ and at times difficult-to-interpret​​​‌ targeting data. Moreover, discrepancies‌ between explanations and repository‌​‌ entries undermine the reliability​​ of both mechanisms. Overall,​​​‌ current transparency infrastructures fall‌ short of the DSA's‌​‌ expectations and highlight the​​ need for clearer and​​​‌ more enforceable standards for‌ advertising transparency moving forward.‌​‌ It has been accepted​​ for publication in PETs/PoPETs​​​‌ 2026.

8.2.2 A Comparative‌ Study of News Exposure‌​‌ and Consumption On and​​ Off Facebook.

Social media​​​‌ giants like Meta, Google,‌ and X leverage powerful‌​‌ algorithms to personalize user​​ feeds, a practice now​​​‌ under intense public scrutiny.‌ These algorithms can inadvertently‌​‌ skew the information users​​ consume, potentially influencing political​​​‌ opinions and voting decisions.‌ This raises critical questions:‌​‌ Do social media platforms​​ foster misinformation and contribute​​​‌ to echo chambers? To‌ address this ongoing debate,‌​‌ our study directly compares​​ news exposure on Facebook​​​‌ (where algorithmic influence is‌ strong) with news consumption‌​‌ off-platform (where user behavior​​ plays a larger role).​​​‌ Specifically, we investigate: (1)‌ Are users exposed to‌​‌ more/less misinformation on Facebook​​ compared with their off-platform​​​‌ misinformation consumption? (2) Is‌ news exposure on Facebook‌​‌ more/less diverse than off-platform​​ news consumption? (3) To​​​‌ what extent do socio-demographic‌ and psychological factors influence‌​‌ misinformation exposure on Facebook​​ and consumption off Facebook?​​​‌ (4) Is there a‌ relationship between socio-demographic and‌​‌ psychological factors and news​​ diversity on and off​​​‌ Facebook? and (5) Is‌ users' exposure to misinformation‌​‌ on Facebook correlated to​​ off-platform news consumption?

Our​​​‌ study of 123,995 news-related‌ posts on Facebook and‌​‌ 70,587 news articles visits​​ off Facebook, collected from​​​‌ 642 users during 12‌ weeks, reveals the following‌​‌ central findings: (1) Only​​ a small fraction 4%​​​‌ of users' news consumption‌ off Facebook is driven‌​‌ by news exposure on​​ Facebook, and only 5.7%​​​‌ of misinformation consumption off‌ Facebook is driven by‌​‌ news exposure on Facebook.​​ (2) There is a​​​‌ higher prevalence of misinformation‌ in user-received content on‌​‌ Facebook compared to deliberately​​ consumed content off-platform. On​​​‌ Facebook, 5.9% of our‌ users' news exposure comes‌​‌ from sources known for​​ spreading misinformation, while off-platform,​​​‌ only 2.6% of our‌ users' news consumption is‌​‌ from misinformation sources. Conversely,​​ Facebook presents more diverse​​​‌ content - 22% of‌ users received content from‌​‌ only one political leaning​​ on Facebook, compared to​​​‌ 36% of users who‌ consumed content from only‌​‌ one political leaning off-platform.​​ (3) Several socio-demographic and​​​‌ psychological factors showed a‌ statistically significant correlation with‌​‌ misinformation exposure on Facebook​​ but not misinformation consumption​​​‌ off Facebook. (4) The‌ proportion of misinformation consumed‌​‌ off Facebook emerged as​​ a statistically significant predictor​​​‌ of users' exposure to‌ misinformation on Facebook, independent‌​‌ of news consumption on​​​‌ Facebook.

This work has​ been published in CSCW​‌ 2025 15.

8.2.3​​ Privacy Settings and Ad​​​‌ Perception: The Shift from​ Third-Party Cookies to the​‌ Privacy Sandbox

Online behavioral​​ advertising, heavily reliant on​​​‌ privacy-invasive third-party cookie tracking,​ faces a significant shift​‌ as browsers like Safari,​​ Brave, and Firefox have​​​‌ already deprecated them. Google​ Chrome announced its parallel​‌ move with the "Privacy​​ Sandbox Initiative" in 2019,​​​‌ proposing privacy-preserving advertising mechanisms.​ The extent to which​‌ Privacy Sandbox can deliver​​ comparable ad relevance and​​​‌ purchase intent to the​ established third-party cookie ecosystem​‌ will likely determine its​​ adoption as a widespread​​​‌ alternative. This paper presents​ the first user study​‌ evaluating the impact of​​ Privacy Sandbox APIs on​​​‌ ad perception. Our findings​ show that users perceive​‌ Privacy Sandbox ads as​​ less relevant and exhibit​​​‌ lower purchase intent compared​ to third-party cookie–based ads,​‌ without a corresponding increase​​ in perceived privacy protection.​​​‌ These results contribute to​ the ongoing assessment of​‌ Privacy Sandbox as an​​ alternative to third-party cookies.​​​‌

8.2.4 Is Contextual Advertising​ Safe? Analyzing Systemic Risks​‌ with Ads on YouTube.​​

Contextual advertising is seeing​​​‌ a resurgence in popularity​ as a privacy-preserving alternative​‌ to behavioral targeting. While​​ often regarded as a​​​‌ coarse-grained approach, advances in​ AI-driven content analysis have​‌ transformed it into a​​ highly granular form of​​​‌ targeting.This work examines the​ safety risks of contextual​‌ targeting through a two-part​​ empirical study, analyzing its​​​‌ potential to enable targeting​ of audiences with sensitive​‌ attributes and exposing users​​ to harmful or exploitative​​​‌ ads. In controlled ad​ experiments, we show that​‌ advertisers can target audiences​​ defined by sensitive attributes​​​‌ (e.g., religious belief, mental​ health condition, and political​‌ ideology) by strategically selecting​​ contextual placements—circumventing policies that​​​‌ prohibit such targeting through​ behavioral signals. To understand​‌ how this risk manifests​​ in practice, we develop​​​‌ an automated measurement framework​ to collect contextual ads​‌ delivered on high-risk content​​ environments, focusing on conspiracy​​​‌ videos. We find that​ contextual ads are highly​‌ prevalent in these environments,​​ disproportionately deliver sensitive categories​​​‌ (e.g., alternative health, religion,​ and political), and lack​‌ transparency. We argue that​​ contextual ad systems require​​​‌ deeper empirical scrutiny and​ robust transparency mechanisms to​‌ prevent exploitation and abuse,​​ and regulators should extend​​​‌ behavioral advertising risk principles​ to the contextual domain.​‌

8.2.5 A Framework for​​ Auditing Ad Delivery Responsiveness​​​‌ to Psychological Traits

Online​ advertising platforms increasingly personalize​‌ ad delivery using users'​​ behavioral signals, even when​​​‌ advertisers cannot explicitly target​ many underlying user characteristics.​‌ Auditing delivery skews for​​ traits that are latent,​​​‌ complex, or not directly​ targetable through advertiser-facing tools​‌ remains challenging. We propose​​ an experimental framework for​​​‌ auditing ad delivery across​ latent traits by constructing​‌ trait-defined audiences and examining​​ how delivery systems allocate​​​‌ ads to these audiences​ under controlled competitive conditions.​‌ We demonstrate this framework​​ on Meta's advertising platform​​​‌ using extraversion as a​ case study. We construct​‌ trait-based audiences using two​​ approaches: psychometric assessment combined​​​‌ with tracking-based retargeting, and​ behavioral profiling based on​‌ on-platform engagement. Under controlled​​ delivery conditions, we examine​​ how the platform allocates​​​‌ personality-aligned and misaligned ads‌ across these audiences. We‌​‌ find a statistically significant​​ alignment effect in ad​​​‌ delivery: ads are more‌ likely to be delivered‌​‌ when their framing matches​​ the personality of the​​​‌ target audience ($\mathrm{\beta ‌}=0.\mathrm{40‌‌},p<\mathrm{0}.001$). This​​​‌ effect is strongest in‌ behaviorally profiled segments, where‌​‌ misaligned ads also exhibit​​ reduced reach relative to​​​‌ aligned ads. Our framework‌ provides a general approach‌​‌ for auditing ad delivery​​ behavior and personalization dynamics​​​‌ driven by latent user‌ traits.

8.2.6 How Persuasive‌​‌ Are LLMs in the​​ Wild? Assessing Personalized Ads​​​‌ in Real-World Delivery

Large‌ language models (LLMs) have‌​‌ demonstrated persuasive potential in​​ controlled experiments and survey-based​​​‌ studies across commercial, political,‌ and social domains. However,‌​‌ their effectiveness in real-world​​ communication environments remains largely​​​‌ unexplored. This work addresses‌ this gap by evaluating‌​‌ LLM-generated personalized messages deployed​​ in controlled advertising experiments​​​‌ on Meta platforms. We‌ assess effectiveness along three‌​‌ complementary dimensions: (1) behavioral​​ user engagement measured through​​​‌ field experiments, (2) perceived‌ appeal captured via user‌​‌ surveys, and (3) platform-level​​ dynamics analyzed through algorithmic​​​‌ ad delivery patterns. Our‌ results show that LLM-based‌​‌ personalized messages do not​​ significantly improve user engagement​​​‌ compared to non-personalized messages.‌ We also show that‌​‌ user perceptions—measured through surveys—can​​ diverge significantly from observed​​​‌ behavioral outcomes online. This‌ highlights the limitations of‌​‌ relying on survey-based evaluations​​ alone to assess the​​​‌ persuasive capabilities of LLMs.‌ Finally, we show that‌​‌ LLM-generated personalization can influence​​ platform ad delivery—shifting impressions​​​‌ toward the intended audience‌ by up to 8%‌​‌ even without explicit targeting​​ instructions. These effects are​​​‌ often constrained by the‌ platform's relevance predictions, which‌​‌ may override the cues​​ embedded in the message.​​​‌ Together, these findings provide‌ a comprehensive real-world audit‌​‌ for the effectiveness and​​ limits of LLM-based persuasion​​​‌ in the wild. It‌ has been accepted for‌​‌ publication in AAAI ICWSM​​ 2026.

8.3.1​​​‌ Navigating the Political Compass:‌ Evaluating Multilingual LLMs across‌​‌ Languages and Nationalities

Large​​ Language Models (LLMs) have​​​‌ become ubiquitous in today's‌ technological landscape, boasting a‌​‌ plethora of applications, and​​ even endangering human jobs​​​‌ in complex and creative‌ fields. One such field‌​‌ is journalism: LLMs are​​ being used for summarization,​​​‌ generation and even fact-checking.‌ However, in today's political‌​‌ landscape, LLMs could accentuate​​ tensions if they exhibit​​​‌ political bias. In this‌ work, we evaluate the‌​‌ political bias of the​​ most used 15 multilingual​​​‌ LLMs via the Political‌ Compass Test. We test‌​‌ different scenarios, where we​​ vary the language of​​​‌ the prompt, while also‌ assigning a nationality to‌​‌ the model. We evaluate​​ models on the 50​​​‌ most populous countries and‌ their official languages. Our‌​‌ results indicate that language​​ has a strong influence​​​‌ on the political ideology‌ displayed by a model.‌​‌ In addition, smaller models​​ tend to display a​​​‌ more stable political ideology,‌ i.e. ideology that is‌​‌ less affected by variations​​​‌ in the prompt. The​ work has been published​‌ in ACL (Findings) 2025​​ 21 and the tool​​​‌ is available on BIL​ 7.1.12.

8.3.2 Benchmarking​‌ the Benchmarks: Reproducing Climate-Related​​ NLP Tasks

Significant efforts​​​‌ have been made in​ the NLP community to​‌ facilitate the automatic analysis​​ of climate-related corpora by​​​‌ tasks such as climate-related​ topic detection, climate risk​‌ classification, question answering over​​ climate topics, and many​​​‌ more. In this work,​ we perform a reproducibility​‌ study on 8 tasks​​ and 29 datasets, testing​​​‌ 6 models. We find​ that many tasks rely​‌ heavily on surface-level keyword​​ patterns rather than deeper​​​‌ semantic or contextual understanding.​ Moreover, we find that​‌ 96% of the datasets​​ contain annotation issues, with​​​‌ 16.6% of the sampled​ wrong predictions of a​‌ zero-shot classifier being actually​​ clear annotation mistakes, and​​​‌ 38.8% being ambiguous examples.These​ results call into question​‌ the reliability of current​​ benchmarks to meaningfully compare​​​‌ models and highlight the​ need for improved annotation​‌ practices. We conclude by​​ outlining actionable recommendations to​​​‌ enhance dataset quality and​ evaluation robustness. The work​‌ has been published in​​ ACL (Findings) 2025 18​​​‌ and the tool is​ available on BIL 7.1.11​‌.

8.4.1 Graph​​​‌ Transformers for Query Plan​ Representation: Potentials and Challenges​‌

Participants: Yanlei Diao,​​ Guillaume Lachaud, Gabriel​​​‌ Lozano Pinzon, Chenghao​ Lyu.

Query Plan​‌ Representation (QPR) is central​​ to workload modeling, with​​​‌ various deep-learning based architectures​ proposed in the literature.​‌ Our work is motivated​​ by two key observations:​​​‌ (i) the research community​ still lacks clarity on​‌ which model, if any,​​ best suits the QPR​​​‌ problem; and (ii) while​ transformers have revolutionized many​‌ fields, their potential for​​ QPR remains largely underexplored.​​​‌ This study examines the​ strengths and challenges of​‌ Graph Transformers for QPR.​​ We introduce a new​​​‌ taxonomy that unifies deep-learning​ based QPR techniques along​‌ key design axes. Our​​ benchmark analysis of common​​​‌ QPR architectures reveals that​ Graph Transformer Networks (GTNs)​‌ consistently outperform alternatives, but​​ can degrade under limited​​​‌ training data. To address​ this, we propose novel​‌ data augmentation techniques to​​ enhance training diversity and​​​‌ refine GTN architectures by​ replacing ineffective language-model-inspired components​‌ with techniques better suited​​ for query plans. Evaluation​​​‌ on JOB, TPC-H, and​ TPC-DS benchmarks shows that​‌ with sufficient training data,​​ enhanced GTNs outperform existing​​​‌ models for capturing complex​ queries (JOB Full and​‌ TPC-DS) and enable the​​ query embedder trained on​​​‌ TPC-DS to generalize to​ TPC-H queries out of​‌ the box. The work​​ has been accepted in​​​‌ VLDB 2026.

8.4.2 Unsupervised​ Anomaly Detection in Multivariate​‌ Time Series across Heterogeneous​​ Domains

Participants: Yanlei Diao​​​‌, Vincent Jacob.​

The widespread adoption of​‌ digital services, along with​​ the scale and complexity​​​‌ at which they operate,​ has made incidents in​‌ IT operations increasingly more​​ likely, diverse, and impactful.​​​‌ This has led to​ the rapid development of​‌ a central aspect of​​ "Artificial Intelligence for IT​​​‌ Operations" (AIOps), focusing on​ detecting anomalies in vast​‌ amounts of multivariate time​​ series data generated by​​ service entities. In this​​​‌ paper, we begin by‌ introducing a unifying framework‌​‌ for benchmarking unsupervised anomaly​​ detection (AD) methods, and​​​‌ highlight the problem of‌ shifts in normal behaviors‌​‌ that can occur in​​ practical AIOps scenarios. To​​​‌ tackle anomaly detection under‌ domain shift, we then‌​‌ cast the problem in​​ the framework of domain​​​‌ generalization and propose a‌ novel approach, Domain-Invariant VAE‌​‌ for Anomaly Detection (DIVAD),​​ to learn domain-invariant representations​​​‌ for unsupervised anomaly detection.‌ Our evaluation results using‌​‌ the Exathlon benchmark show​​ that the two main​​​‌ DIVAD variants significantly outperform‌ the best unsupervised AD‌​‌ method in maximum performance,​​ with 20% and 15%​​​‌ improvements in maximum peak‌ F1-scores, respectively. Evaluation using‌​‌ the Application Server Dataset​​ further demonstrates the broader​​​‌ applicability of our domain‌ generalization methods. The work‌​‌ has been published in​​ VLDB 2025 22.​​​‌

8.4.3 Transactional Stateful Functions‌ on Streaming Dataflows

Participants:‌​‌ Georgios Siachamis.

Developing​​ stateful cloud applications, such​​​‌ as low-latency workflows and‌ microservices with strict consistency‌​‌ requirements, remains arduous for​​ programmers. The Stateful Functions-as-a-Service​​​‌ (SFaaS) paradigm aims to‌ serve these use cases.‌​‌ However, existing approaches provide​​ weak transactional guarantees or​​​‌ perform expensive external state‌ accesses requiring inefficient transactional‌​‌ protocols that increase execution​​ latency. In this paper,​​​‌ we present Styx, a‌ novel dataflow-based SFaaS runtime‌​‌ that executes serializable transactions​​ consisting of stateful functions​​​‌ that form arbitrary call-graphs‌ with exactly-once guarantees. Styx‌​‌ extends a deterministic transactional​​ protocol by contributing: i)​​​‌ a function acknowledgment scheme‌ to determine transaction boundaries‌​‌ required in SFaaS workloads,​​ ii) a function-execution caching​​​‌ mechanism, and iii) an‌ early commit-reply mechanism that‌​‌ substantially reduces transaction execution​​ latency. Experiments with the​​​‌ YCSB, TPC-C, and Deathstar‌ benchmarks show that Styx‌​‌ outperforms state-of-the-art approaches by​​ achieving at least one​​​‌ order of magnitude higher‌ throughput while exhibiting near-linear‌​‌ scalability and low latency.​​ This work has been​​​‌ published in SIGMOD 2025‌ 24 and demonstrated in‌​‌ VLDB 2025 25.​​

8.4.4 Dynamic Graph Databases​​​‌ with Out-of-order Updates

Participants:‌ Muhammad Khan, Ioana‌​‌ Manolescu.

Dynamic graphs​​ are omnipresent in real-time​​​‌ applications that generate massive‌ amounts of data. We‌​‌ consider dynamic graphs, where​​ edges are continuously added​​​‌ and deleted to a‌ single graph, from multiple‌​‌ update streams. The dynamic​​ graphs are stored in​​​‌ a transactional graph database.‌ Each edge update or‌​‌ deletion carries a source​​ (stream) time $S\mathrm{T‌}$, assigned at the‌ moment when it was‌​‌ emitted, and an arrival​​ (or transaction) time $\mathrm{W‌}T$ , assigned when‌ the graph database receives‌​‌ it. Updates may be​​ received at the database​​​‌ out-of-order (ooo, in short):‌ due to different latencies‌​‌ on the propagation paths​​ between the data source​​​‌ and the database. We‌ proposed HAL, a novel‌​‌ in-memory dynamic graph database​​ design, addressing these challenges.​​​‌ HAL outperforms comparable systems‌ by a factor of‌​‌ up to 73$\times $ in terms of update​​​‌ processing throughput and up‌ to 357$\times$ for‌​‌ analytics, while being the​​ first to support out-of-order​​​‌ updates. We have also‌ extended it with support‌​‌ for node and edge​​​‌ properties, and for historical​ queries, whereas queries should​‌ be evaluated over the​​ graph such as it​​​‌ was at a specific​ moment in the past.​‌ This work has been​​ accepted in VLDB 2025​​​‌ 12, VLDB 2025​ Large-Scale Graph Data Analytics​‌ (LSGDA) workshop 16 and​​ demonstrated in SIGMOD 2025​​​‌ 17. The code​ is available on Gitlab​‌ (code).

Participants: Ioana​​ Manolescu, Oana Balalau​​​‌, Yanlei Diao,​ Ghufran Khan, Maxime​‌ Buron, Hritika Kathuria​​, Georgios Siachamis.​​​‌

10.1.1​ Associate Teams in the​‌ framework of an Inria​​ International Lab or in​​​‌ the framework of an​ Inria International Program

10.2.1‌ Visits of international scientists‌​‌

10.2.2 Visits​​​‌ to international teams

10.3.1 Horizon Europe​​​‌

10.3.2 H2020 projects​​

Ioana Manolescu Goujot is​​​‌ the local PI for​ the Inria partner in​‌ the project "ELIAS -​​ European Lighthouse of AI​​​‌ for Sustainability" (2,800,000 euros).​ Madhulika Mohanty and Garima​‌ Gaur have also been​​ strongly involved.

Yanlei Diao​​​‌ has been awarded the​ ERC Grant - ERC​‌ Proof of Concept -​​ on ExplainableAD: Explainable Anomaly​​​‌ Detection for Safeguarding and​ Enhancing Modern Data Industry.​‌

10.4.1​​ ANR

• Oana Goga is​​​‌ the local PI for​ LIX partner - ANR​‌ PRC 2022 - 2026​​ “FeedingBias: A multi-platform mixed-methods​​​‌ approach to news exposure​ on social media” (our​‌ part: 128,000 euros)
• Oana​​ Goga is the local​​​‌ PI for LIX partner​ - ANR PRCE 2021​‌ - 2025 “PROPEOS: Privacy-oriented​​ Personalization of Online Services”​​​‌ (our part: 202,720 euros)​
• The project "TopOL (Top​‌ of the Lake): discovery​​ and exploitation of heterogeneous​​​‌ data lakes through graph​ models", coordinated by Ioana​‌ Manolescu Goujot , has​​ been funded by the​​​‌ ANR. The project is​ a collaboration with U.​‌ Paris Saclay, U. Paris​​ Dauphine, U. Blois and​​​‌ U. Tours; the International​ Consortium of Investigative Journalism​‌ (ICIJ) is a non-funded​​ partner. Madhulika Mohanty also​​​‌ participates and is a​ Work Package co-leader.

Chair​​​‌ of conference program committees​

Ioana Manolescu Goujot was​‌ the Demonstration chair at​​ EDBT 2025.

Madhulika Mohanty​​ was the demonstration chair​​​‌ of at the French‌ data base conference, BDA‌​‌ 2025.

Member of the​​ conference program committees

The​​​‌ team members have been‌ part of the following‌​‌ program committees:

• Ioana Manolescu​​ Goujot : ACL Rolling​​​‌ Review 2025, IEEE ICDE‌ 2025, ACM PACMMOD (formerly‌​‌ SIGMOD) 2025, BDA 2025​​
• Oana-Denisa Balalau : ACL​​​‌ Rolling Review February 2025‌
• Madhulika Mohanty : VLDB‌​‌ 2025, ICDE 2025, EDBT​​ 2025 (Demo), ICDE 2025​​​‌ (Demo), VLDB 2025 (Demo),‌ CODS 2025, CMLS Workshop‌​‌ in ER 2025
• Garima​​ Gaur : CIKM 2025,​​​‌ BDA 2025, CMLS Workshop‌ in ER 2025
• Georgios‌​‌ Siachamis : ICDE 2025,​​ EDBT 2025 (Demo), DEBS​​​‌ 2025

11.1.1 Journal

11.1.2​​ Invited talks

Ioana Manolescu​​​‌ Goujot delivered a keynote‌ at AFIA (French AI‌​‌ Reseach Association) workshop “Perspectives​​ et Défis de l'IA”​​​‌ on « Désinformation, Démocratie‌ et IA », June‌​‌ 10, 2025 (link​​).

Oana-Denisa Balalau delivered​​​‌ a talk at ESSEC‌ in the workshop Comprendre‌​‌ et Changer le Monde​​ (CCM), titled “Improving the​​​‌ quality of public debate‌ with AI”.

Madhulika Mohanty‌​‌ delivered the following talks:​​

• “Intelligence Artificielle: un outil​​​‌ au service de l'investigation”‌ at VIGINUM in May‌​‌ 2025.
• “Effective Exploration of​​ Graph-Structured Data” at LHC​​​‌ and IDIA Days 2025‌ in June 2025.

Tom‌​‌ Calamai delivered a workshop​​ on “les applications de​​​‌ l'IA pour l'investissement responsable”‌ organised by the FIR‌​‌ (forum pour l'investissement responsable)​​ (link)

11.1.3​​​‌ Leadership within the scientific‌ community

Ioana Manolescu Goujot‌​‌ has been the president​​ of the informal French​​​‌ Data Management Association (BDA).‌

11.1.4 Research administration

Ioana‌​‌ Manolescu Goujot represents Inria​​ in the Comité Operationnel​​​‌ of Hi!Paris, an AI‌ Pole of Excellency comprising‌​‌ IP Paris and HEC.​​ She is also an​​​‌ elected member of IP‌ Paris' Comité Académique and‌​‌ serves on its Scientific​​ Committee.

11.2.1 Teaching

Ioana Manolescu​​ Goujot is a part-time​​​‌ professor (50%) at Ecole‌ Polytechnique. She taught:

• Courses,‌​‌ labs and TDs in​​ CSC_51053_EP (Database Management Systems);​​​‌
• She is in charge‌ of the M1 Internship‌​‌ program in Artificial Intelligence​​ and Data Science (CSC_52992_EP).​​​‌
• She is also in‌ charge of the Artificial‌​‌ Intelligence M1 program at​​ Ecole Polytechnique

Madhulika Mohanty​​​‌ has a 25% Chargée‌ d'Enseignement contract at Ecole‌​‌ Polytechnique for 10 months.​​ She taught:

• Labs and​​​‌ TDs in CSC_51053_EP (Database‌ Management Systems)
• Labs and‌​‌ TDs in CSC_52083_EP (Systems​​ for Big Data)
• She​​​‌ also taught 3h of‌ CM and 3h of‌​‌ TP for ECE_5DA04_TP (Big​​ Graph Data Management) at​​​‌ Télécom for DATAAI Masters.‌

Oana-Denisa Balalau is a‌​‌ part-time (33%) assistant professor​​ at Ecole Polytechnique, where​​​‌ she teaches “Mining, learning‌ and reasoning on Web‌​‌ Graphs”, L3

Przemyslaw Dominikowski​​ carries out a complementary​​​‌ teaching assignment (64h) at‌ Ecole Polytechnique. He teaches‌​‌ the labs in CSC_2F001_EP​​​‌ (Object Oriented Programming in​ C++).

Garima Gaur carried​‌ out following teaching duties:​​

• Course, Labs and TDs​​​‌ in CSC_52640_EP (Database Management​ Systems) offered by DMAP,​‌ Ecole Polytechnique
• Labs and​​ TDs in CSC_51053_EP (Database​​​‌ Management Systems)
• 3h of​ CM and 3h of​‌ TP for ECE_5D04_TP (Big​​ Graph Data Management) at​​​‌ Télécom for DATAAI Masters.​

Hritika Kathuria carries out​‌ a complementary teaching assignment​​ (64h) at Ecole Polytechnique​​​‌ and teaches 2 Labs​ in CSE_102.

Tom Calamai​‌ has a 30h teaching​​ assistant (Vacataire) contract at​​​‌ Télécom Paris and Ecole​ Polytechnique. He teaches:

• INF473G​‌
• Machine Learning for Text​​ Mining
• Machine learning avancé​​​‌
• Database
• Language Modeling

Georgios​ Siachamis carried out 3h​‌ of CM and 3h​​ of TP for ECE_5D04_TP​​​‌ (Big Graph Data Management)​ at Télécom for DATAAI​‌ Masters.

Yanlei Diao holds​​ a part-time (50%) full​​​‌ Professor position at Ecole​ Polytechnique. She teaches Systems​‌ for Big Data (CSC_52083_EP​​ Systems for Big Data),​​​‌ M1, Ecole Polytechnique.

Guillaume​ Lachaud has a 58h​‌ teaching assistant position at​​ Ecole Polytechnique. He teaches:​​​‌

• CSC_52087_EP- Advanced Deep Learning​
• CSC_41011_EP - Les bases​‌ de la programmation et​​ de l'algorithmique
• CSC_43M02_EP (for​​​‌ one day) - Modal​ d'informatique - Exploration et​‌ apprentissage sur les graphes​​ du Web

11.2.2 Supervision​​​‌

The team supervised the​ following PhDs:

1. Przemysław Dominikowski,​‌ Sep 2025 - Dec​​ 2025, advised by Ioana​​​‌ Manolescu Goujot and Madhulika​ Mohanty
2. Kun Zhang, Jan​‌ 2025-April 2025, advised by​​ Ioana Manolescu Goujot and​​​‌ Oana-Denisa Balalau
3. Tom Calamai,​ Jan 2025-Dec 2025, advised​‌ by Fabian Suchanek and​​ Oana-Denisa Balalau
4. Hritika Kathuria,​​​‌ Jan 2025-Dec 2025, advised​ by Ioana Manolescu Goujot​‌ and Maxime Buron
5. Ines​​ Abdelaziz, Dec 2025, advised​​​‌ by Oana Goga
6. Nardjes​ Amieur, Jan 2025-Dec 2025,​‌ advised by Oana Goga​​
7. Abir Benzaamia, Jan 2025-Dec​​​‌ 2025, advised by Oana​ Goga
8. Asmaa El Fraihi,​‌ Jan 2025-Dec 2025, advised​​ by Oana Goga
9. Gabriel​​​‌ Ben Zenou, Jan 2025-Dec​ 2025, advised by Oana​‌ Goga
10. Gabriel Lozano, Sept​​ 2025-Dec 2025, advised by​​​‌ Yanlei Diao and Guillaume​ Lachaud
11. Nazim Mezhoudi, Jan​‌ 2025-Dec 2025, advised by​​ Yanlei Diao and Mariam​​​‌ Barry (BNP Paribas)

The​ team supervised the following​‌ postdocs:

1. Chadi Helwe, Jan​​ 2025-March 2025, advised by​​​‌ Oana-Denisa Balalau and Davide​ Ceolin
2. Guillaume Lachaud, Jan​‌ 2025-Dec 2025, advised by​​ Yanlei Diao

The team​​​‌ supervised the following engineers:​

1. Simon Ebel and Théo​‌ Galizzi (January to June​​ 2025), Aurélien Peden (March​​​‌ to August 2025): Oana-Denisa​ Balalau and Ioana Manolescu​‌ Goujot supervised them on​​ their collaboration project with​​​‌ RadioFrance.
2. George Siachamis: supervised​ by Ioana Manolescu Goujot​‌ and Madhulika Mohanty on​​ efficient and expressive graph​​​‌ data management.
3. Ines Abdelaziz​ (January to November 2025):​‌ supervised by Oana Goga​​ .

The team supervised​​​‌ the following interns:

1. Pablo​ Bertaud-Velten, M1 IP Paris,​‌ advised by Ioana Manolescu​​ Goujot , Madhulika Mohanty​​​‌ , Garima Gaur and​ Georgios Siachamis
2. Przemyslaw Dominikowski,​‌ M2 UP Saclay, advised​​ by Ioana Manolescu Goujot​​​‌ , Madhulika Mohanty ,​ Garima Gaur and Georgios​‌ Siachamis
3. Nikola Dobriçic, X​​ Bachelor 3rd year, advised​​​‌ by Ioana Manolescu Goujot​ , Madhulika Mohanty and​‌ Georgios Siachamis
4. Joanne Jegou,​​ X Bachelor 3rd year,​​ co-advised by Ioana Manolescu​​​‌ Goujot and Michael Thy‌ (APHP)
5. Paul Kronlund-Drouault, ENS‌​‌ Lyon Bachelor 2nd year,​​ advised by Ioana Manolescu​​​‌ Goujot .
6. Maria Mellado,‌ M2 University of Chile,‌​‌ advised by Ioana Manolescu​​ Goujot , Madhulika Mohanty​​​‌ and Garima Gaur
7. Saba‌ Shashsavari, M1 IP Paris,‌​‌ advised by Ioana Manolescu​​ Goujot , Madhulika Mohanty​​​‌ , Garima Gaur and‌ Georgios Siachamis
8. Vlada Voronina,‌​‌ M1, advised by Oana-Denisa​​ Balalau and Marine Le​​​‌ Morvan
9. Rémi Guillou, X‌ Bachelor 3rd Year, advised‌​‌ by Yanlei Diao
10. Yanis​​ Zaamoun, X Bachelor 3rd​​​‌ year, advised by Yanlei‌ Diao

The team supervised‌​‌ the following part-time projects:​​

1. PSC "Analyse du discours​​​‌ médiatique autour du changement‌ climatique", advised by Oana-Denisa‌​‌ Balalau and Etienne Ollion​​
2. Léo Nivelle (X3A), "Automatic​​​‌ verbalisation of statistics", advised‌ by Ioana Manolescu Goujot‌​‌
3. Yiheng Chen, Antoine Delacour​​ and Elliot Thorel (X3A):​​​‌ "Natural language querying of‌ large heterogeneous datasets", advised‌​‌ by Ioana Manolescu Goujot​​ , Madhulika Mohanty ,​​​‌ Garima Gaur and Georgios‌ Siachamis
4. Cédric Trinh and‌​‌ Tom Léon (X3A): "Building​​ a Knowledge Graph for​​​‌ Fact-checks", advised by Madhulika‌ Mohanty and Garima Gaur‌​‌
5. Moritz Sommer (X and​​ RWTH Exchange Program): "Identification​​​‌ of Core Properties for‌ Semantic Concepts in Universal‌​‌ Datasets", advised by Ioana​​ Manolescu Goujot , Madhulika​​​‌ Mohanty and Garima Gaur‌
6. Maximilien Rambaud, Nicolas Gromitsaris,‌​‌ Anthony Chassagne (X3A): "Anomaly​​ detection and explaination in​​​‌ dynamic graphs, with applications‌ in finance", advised by‌​‌ Yanlei Diao and Guillaume​​ Lachaud
7. Gabriel Cheval, Armand​​​‌ Vabre (X3A): "Detecting data‌ drift in graphs for‌​‌ model retraining" advised by​​ Yanlei Diao and Guillaume​​​‌ Lachaud
8. Loric Roger, Joseph‌ de Roffignac, Sylvain Dehayem‌​‌ (X3A): "Anomaly detection in​​ dynamic graphs", advised by​​​‌ Yanlei Diao and Guillaume‌ Lachaud
9. Berthé Zié, Goly‌​‌ Kodia (X3A): "Explainable dynamic​​ graph neural networks for​​​‌ anomaly detection", advised by‌ Yanlei Diao and Guillaume‌​‌ Lachaud

11.2.3 Juries

Oana-Denisa​​ Balalau has served as​​​‌ a:

• member of the‌ recruitment comittee for assistant‌​‌ professor at Télécom Paris​​
• part of the PhD​​​‌ defense committee of Jonathan‌ Colin (Université Paris Saclay),‌​‌ William Soto (Université de​​ Lorraine)

Ioana Manolescu Goujot​​​‌ has served in the‌ following juries:

• Member of‌​‌ a Professor hiring committee​​ at Université de Paris​​​‌ Dauphine (june 2025)
• Reported‌ on the PhD thesis‌​‌ of Yifan Wang, Université​​ de Lille, defended in​​​‌ November 2025

11.3.1 Specific official responsibilities‌​‌ in science outreach structures​​

Oana-Denisa Balalau is a​​​‌ member of Inria Saclay's‌ Scientific Commission. She also‌​‌ animated the foresight seminar​​ on LLMs&Science at the​​​‌ "Data and Knowledge" Inria‌ seminar in March 2025.‌​‌

Ioana Manolescu Goujot ia​​ an elected member of​​​‌ Inria's Comité d'Evaluation.

11.3.2‌ Participation in Live events‌​‌

Ioana Manolescu Goujot had​​ several intervention in national​​​‌ media:

1. Participated to ARTE‌ "28 minutes" show on‌​‌ the impact of AI​​ on society, December 24,​​​‌ 2025.
2. Interviewed by Michaël‌ Szadkowsky (Le Monde) for‌​‌ the article "2025, l'année​​ où la vidéo par​​​‌ IA a envahi les‌ réseaux sociaux", December 22,‌​‌ 2025.
3. Interviewed by Désirée​​ de Lamarzelle (Forbes Magazine)​​​‌ for the article "Future‌ of work: is AI‌​‌ a friend or a​​​‌ foe?", November 13, 2025.​
4. Interviewed by Mélinée Le​‌ Priol (La Croix) for​​ the article Faut-il avoir​​​‌ peur de la 'superintelligence​ artificielle'?", October 30, 2025​‌
5. Interviewed by Marina Alcaraz​​ (Les Echos) on the​​​‌ frequency of fake news​ in chatbot responses, September​‌ 2025
6. Interviewed by Marina​​ Alcaraz (Les Echos) on​​​‌ disinformation sometimes present in​ Mistral outputs, July 2025​‌
7. Interviewed by Alexandre Capron​​ whether a GenAI vi​​​‌ (TF1) on fake AI​ videos, June 6, 2025.​‌
8. Guest in the radio​​ show "Je pense donc​​​‌ j'agis": Où vont nos​ données et comment les​‌ protéger?", hosted by Melchior​​ Gormand, on RCF, April​​​‌ 3, 2025
9. In a​ press conference organized as​‌ part of a "Stand​​ Up for Science" day​​​‌ on April 3, 2025​ (dépêche AEF, video recording)​‌
10. Member of a panel​​ about ethical and regulatory​​​‌ bounds on research in​ "Journée Sciences et Médias"​‌ (French Association of Science​​ Journalists), February 10, 2025.​​​‌
11. Interviewed by Chloé Woitier​ for the article "C'est​‌ une nouvelle pollution numérique​​ : le Slop, ce​​​‌ raz-de-marée de contenus IA​ qui menace internet", Le​‌ Figaro, February 2, 2025.​​
12. Authored an invited opinion​​​‌ piece in l'Humanité "Les​ réseaux sociaux nuisent-ils à​‌ la démocratie?" on January​​ 27, 2025.

11.3.3 Others​​​‌ science outreach relevant activities​

Przemyslaw Dominikowski conducted an​‌ outreach session (1.5h) for​​ high school students (stage​​​‌ de seconde), presenting CEDAR's​ team research, in particular​‌ data lake indexing.

Ioana​​ Manolescu Goujot gave a​​​‌ presentation for CPES (1st​ year higher education) students​‌ at Lycée International de​​ Palaiseau Paris-Saclay.

International journals‌

• 12 articleA. C.‌​‌Angelos Christos Anadiotis,​​ M.Muhammad Ghufran Khan​​​‌ and I.Ioana Manolescu‌. Dynamic Graph Databases‌​‌ with Out-of-Order Updates.​​Proceedings of the VLDB​​​‌ Endowment (PVLDB)1713‌February 2025, 4799-4812‌​‌HALDOIback to​​ text
• 13 articleN.​​​‌Nelly Barret, S.‌ S.Sourav S. Bhowmick‌​‌, A.Angela Bonifati​​, B.Barbara Catania​​​‌, S.Stratos Idreos‌, E.Ekaterini Ioannou‌​‌, M.Madhulika Mohanty​​, S.Sana Sellami​​​‌, R.Roee Shraga‌, U.Utku Sirin‌​‌, J.Juno Steegmans​​, P.Pinar Tozun​​​‌, S.Soror Sahri‌ and G.Genoveva Vargas-Solar‌​‌. Diversity, Equity and​​ Inclusion Activities in Database​​​‌ Conferences: A 2024 Report‌.SIGMOD record54‌​‌32025, 40-43​​HALDOIback to​​​‌ text
• 14 articleN.‌Nelly Barret, A.‌​‌Antoine Gauquier, J.-J.​​​‌Jia-Jean Law and I.​Ioana Manolescu. Finding​‌ meaningful paths in heterogeneous​​ graphs with PathWays.​​​‌Information Systems127January​ 2025, 102463HAL​‌DOI

International peer-reviewed conferences​​

• 15 inproceedingsN.Nardjes​​​‌ Amieur, S.Salim​ Chouaki, O.Oana​‌ Goga and B.Beatrice​​ Roussillon. A Comparative​​​‌ Study of News Exposure​ and Consumption On and​‌ Off Facebook.Proc.​​ ACM Hum.-Comput. Interact.CSCW​​​‌ 2025 - 28th ACM​ SIGCHI Conference on Computer-Supported​‌ Cooperative Work & Social​​ Computing97Bergen,​​​‌ NorwayNovember 2025,​ CSCW359HALDOIback​‌ to text
• 16 inproceedings​​A.-C.Angelos-Christos Anadiotis,​​​‌ M.Muhammad Ghufran Khan​ and I.Ioana Manolescu​‌. Growing Up HAL:​​ Historic and Property Graph​​​‌ Queries.LSGDA 2025​ - 4th International Workshop​‌ on Large Scale Graph​​ Data Analytics (in conjunction​​​‌ with VLDB 2025)London,​ United KingdomSeptember 2025​‌HALback to text​​
• 17 inproceedingsA. C.​​​‌Angelos C Anadiotis,​ M. G.Muhammad Ghufran​‌ Khan and I.Ioana​​ Manolescu. Catching up​​​‌ with Disorder: Dynamic Graphs​ with Out-of-Order Updates.​‌In Companion of the​​ 2025 International Conference on​​​‌ Management of Data (SIGMOD-Companion​ ’25)ACM SIGMOD/PODS 2025​‌ - International Conference on​​ Management of DataBerlin,​​​‌ GermanyJune 2025,​ 15 - 18HAL​‌DOIback to text​​
• 18 inproceedingsT.Tom​​​‌ Calamai, O.Oana​ Balalau and F. M.​‌Fabian M Suchanek.​​ Benchmarking the Benchmarks: Reproducing​​​‌ Climate-Related NLP Tasks.​ACL 2025 - The​‌ 63rd Annual Meeting of​​ the Association for Computational​​​‌ LinguisticsVienne, AustriaJuly​ 2025HALback to​‌ text
• 19 inproceedingsA.​​Antoine Gauquier, S.​​​‌Simon Ebel, H.​Helena Galhardas, T.​‌Théo Galizzi, I.​​Ioana Manolescu, A.​​​‌Aurélien Peden and P.​Pierre Senellart. Efficient​‌ and Scalable Search for​​ Statistics.ICDE 2026​​​‌ - 42nd IEEE International​ Conference on Data Engineering​‌Montréal, CanadaMay 2026​​HALback to text​​​‌
• 20 inproceedingsG.Garima​ Gaur, O.Oana​‌ Balalau, I.Ioana​​ Manolescu and P. D.​​​‌Prajna Devi Upadhyay.​ The Search for Conflicts​‌ of Interest: Open Information​​ Extraction in Scientific Publications​​​‌.EMNLP 2025 -​ Conference on Empirical Methods​‌ in Natural Language Processing​​Suzhou, ChinaNovember 2025​​​‌HALback to text​
• 21 inproceedingsC.Chadi​‌ Helwe, O.Oana​​ Balalau and D.Davide​​​‌ Ceolin. Navigating the​ Political Compass: Evaluating Multilingual​‌ LLMs across Languages and​​ Nationalities.ACL 2025​​​‌ - 63rd Annual Meeting​ of the Association for​‌ Computational LinguisticsVienna, Austria​​Association for Computational Linguistics​​​‌July 2025, 17179-17204​HALDOIback to​‌ text
• 22 inproceedingsV.​​Vincent Jacob and Y.​​​‌Yanlei Diao. Unsupervised​ Anomaly Detection in Multivariate​‌ Time Series across Heterogeneous​​ Domains.Proceedings of​​​‌ the VLDB EndowmentVLDB​ 2025 - 51th International​‌ Conference on Very Large​​ Databases186Londres,​​​‌ United KingdomACM Digital​ LibraryFebruary 2025,​‌ 1691-1704HALDOIback​​ to text
• 23 inproceedings​​​‌C.Chenghao Lyu,​ G.Guillaume Lachaud,​‌ G.Gabriel Lozano and​​ Y.Yanlei Diao.​​ Graph Transformers for Query​​​‌ Plan Representation: Potentials and‌ Challenges.VLDB 2026‌​‌ - 52th International Conference​​ on Very Large Databases​​​‌Boston (MA), United States‌August 2026HAL
• 24‌​‌ inproceedingsK.Kyriakos Psarakis​​, G.George Christodoulou​​​‌, G.Georgios Siachamis‌, M.Marios Fragkoulis‌​‌ and A.Asterios Katsifodimos​​. Styx: Transactional Stateful​​​‌ Functions on Streaming Dataflows‌.Styx: Transactional Stateful‌​‌ Functions on Streaming Dataflows​​International Conference on Management​​​‌ of Data (SIGMOD)3‌3Berlin, GermanyJune‌​‌ 2025, 1-28HAL​​DOIback to text​​​‌
• 25 inproceedingsK.Kyriakos‌ Psarakis, O.Oto‌​‌ Mraz, G.George​​ Christodoulou, G.Georgios​​​‌ Siachamis, M.Marios‌ Fragkoulis and A.Asterios‌​‌ Katsifodimos. Styx in​​ Action: Transactional Cloud Applications​​​‌ Made Easy.Styx‌ in Action: Transactional Cloud‌​‌ Applications Made EasyInternational​​ Conference on Very Large​​​‌ Data Bases (VLDB)18‌12Londres, United Kingdom‌​‌September 2025, 5275-5278​​HALDOIback to​​​‌ text
• 26 inproceedingsA.‌Atte Torri, P.‌​‌Przemysław Dominikowski, B.​​Brice Pointal, O.​​​‌Oguz Kaya, L.‌Laércio Lima Pilla and‌​‌ O.Olivier Coulaud.​​ Near-Optimal Contraction Strategies for​​​‌ the Scalar Product in‌ the Tensor-Train Format.‌​‌Euro-Par 2025: Parallel Processing​​Euro-Par 2025 - 31​​​‌ International European Conference on‌ Parallel and Distributed Computing‌​‌15902Lecture Notes in​​ Computer ScienceDresden, Germany​​​‌Springer Nature SwitzerlandAugust‌ 2025, 63-77HAL‌​‌DOI
• 27 inproceedingsK.​​Kun Zhang, O.​​​‌Oana Balalau and I.‌Ioana Manolescu. Structured‌​‌ Discourse Representation for Factual​​ Consistency Verification.ACL​​​‌ 2025 - 63rd Annual‌ Meeting of the Association‌​‌ for Computational LinguisticsVienne,​​ AustriaJuly 2025HAL​​​‌back to text

Conferences‌ without proceedings

• 28 inproceedings‌​‌M.Maxime Buron,​​ H.Hritika Kathuria,​​​‌ I.Ioana Manolescu and‌ G.George Siachamis.‌​‌ RDF Query Answering in​​ the Presence of Access​​​‌ Restrictions.LNCS Series‌ bookCoopIS 2025 -‌​‌ 31 st International Conference​​ on Cooperative Information Systems​​​‌Marbella, SpainOctober 2025‌HALback to text‌​‌back to text

Reports​​ & preprints

• 29 report​​​‌M.Maxime Buron,‌ H.Hritika Kathuria,‌​‌ I.Ioana Manolescu and​​ G.George Siachamis.​​​‌ RDF Query Answering in‌ the Presence of Access‌​‌ Restrictions.INRIA; Ecole​​ polytechniqueSeptember 2025HAL​​​‌
• 30 reportP.Paul‌ Kronlund-Drouault. Towards better‌​‌ identification of fact tables​​ in statistical spreadsheets.​​​‌Inria Saclay; LIX --‌ Ecole polytechnique; ENS Lyon‌​‌July 2025HAL
• 31​​ miscM.Marijan Soric​​​‌, C.Cécile Gracianne‌, I.Ioana Manolescu‌​‌ and P.Pierre Senellart​​. Benchmarking Table Extraction​​​‌ from Heterogeneous Scientific Extraction‌ Documents.November 2025‌​‌HAL
• 32 reportL.​​Lex Zard, O.​​​‌Oana Goga, A.‌Asmaa El fraihi and‌​‌ N.Nataliia Bielova.​​ Feedback to the European​​​‌ Data Protection Board's Guidelines‌ 3/2025 on the interplay‌​‌ between the DSA and​​ the GDPR (Version 1.1)​​​‌ - Advertisement.Inria‌ & Université Cote d'Azur,‌​‌ Sophia Antipolis, FranceOctober​​ 2025, 1-20HAL​​​‌

Other scientific publications

• 33‌ inproceedingsP.Przemysław Dominikowski‌​‌, A.Atte Torri​​​‌, B.Brice Pointal​, O.Oguz Kaya​‌, L.Laercio Lima​​ Pilla and O.Olivier​​​‌ Coulaud. Exploring Near-Optimal​ Contraction Strategies for the​‌ Scalar Product in the​​ Tensor-Train Format.IPDPS​​​‌ 2025 - 39th IEEE​ International Parallel & Distributed​‌ Processing SymposiumMilan, Italy​​June 2025, 1274-1276​​​‌HALDOI
• 34 thesis​M.Marijan Soric.​‌ Understanding and Extracting Table​​ Information from BRGM Documents​​​‌.Ecole centrale de​ LyonFebruary 2025HAL​‌

Scientific popularization

• 35 inbook​​I.Ioana Manolescu and​​​‌ P.Patrick Valduriez.​ De nouvelles architectures pour​‌ les Big Data.​​Le calcul à découvert​​​‌CNRS EditionsJanuary 2025​HAL