Optical Character Recognition (OCR) is a fundamental component of document analysis pipelines, enabling downstream tasks such as information extraction, retrieval, and serving as a key enabler of automation. The recent emergence of multimodal Large Language Models (LLMs) has introduced alternative approaches that integrate implicit OCR capabilities within unified vision–language architectures. Despite their growing prominence, a systematic comparison between traditional OCR systems and multimodal LLMs is still lacking. This paper presents a comprehensive benchmarking study encompassing 16 OCR and multimodal systems, grouped into open-source OCR engines, commercial OCR services, commercial multimodal LLMs, and open-source multimodal LLMs. The evaluation spans four publicly available datasets representing printed, scanned, handwritten, English and Chinese dense-text multicolumn documents. Performance is assessed using character- and word-level accuracy, normalized edit distance, and flexible character accuracy, complemented by latency and cost analyses to provide a holistic view of each system’s operational suitability. The results show that no single paradigm excels universally: in our settings, multimodal LLMs achieve stronger performance on unstructured and handwritten inputs, while specialized OCR pipelines remain more reliable for structured and printed layouts. Lightweight domain adaptation through fine-tuning proves beneficial for open-source OCR models, whereas general-purpose LLMs offer competitive accuracy at higher computational costs. All assessment tools and configurations are publicly released to promote reproducibility and facilitate future OCR and LLM research within the document analysis community.

Deep learning-based intrusion detection systems often depend on large labeled datasets and generating such data is both costly and sometimes impractical. To overcome this limitation, we propose a hybrid learning approach built on a transformer architecture. Our method integrates a self-supervised pretraining phase, where the model is trained to reconstruct noised segments of input traffic data from unlabeled sequences, with a supervised fine-tuning stage that requires only a fraction of labeled data. Our experiments demonstrate the effectiveness of this hybrid approach, achieving up to 98.8% of the performance of the supervised models using 50% of the labeled data. Index Terms—Intrusion Detection, Hybrid Learning

The remarkable performance achieved by Large Language Models (LLM) has driven research efforts to leverage them for a wide range of tasks and input modalities. In speech-to-text (S2T) tasks, the emerging solution consists of projecting the output of the encoder of a Speech Foundational Model (SFM) into the LLM embedding space through an adapter module. However, no work has yet investigated how much the downstream-task performance depends on each component (SFM, adapter, LLM) nor whether the best design of the adapter depends on the chosen SFM and LLM. To fill this gap, we evaluate the combination of 5 adapter modules, 2 LLMs (Mistral and Llama), and 2 SFMs (Whisper and SeamlessM4T) on two widespread S2T tasks, namely Automatic Speech Recognition and Speech Translation. Our results demonstrate that the SFM plays a pivotal role in downstream performance, while the adapter choice has moderate impact and depends on the SFM and LLM.

Abstract We focus on the key task of semantic type discovery over a set of heterogeneous sources, an important data preparation task. We consider the challenging setting of multiple Web data sources in a vertical domain, which present sparsity of data and a high degree of heterogeneity, even internally within each individual source. We assume each source provides a collection of entity specifications, i.e. entity descriptions, each expressed as a set of attribute name-value pairs. Semantic type discovery aims at clustering individual attribute name-value pairs that represent the same semantic concept. We take advantage of the opportunities arising from the redundancy of information across such sources and propose the iterativeRaF-STDsolution, which consists of three key steps: (i) a Bayesian model analysis of overlapping information across sources to match the most locally homogeneous attributes; (ii) a tagging approach, inspired by NLP techniques, to create (virtual) homogeneous attributes from portions of heterogeneous attribute values; and (iii) a novel use of classical techniques based on matching of attribute names and domains. Empirical evaluation on the DI2KG and WDC benchmarks demonstrates the superiority ofRaF-STDover alternative approaches adapted from the literature.

Entity resolution (ER) aims at matching records that refer to the same real-world entity. Although widely studied for the last 50 years, ER still represents a challenging data management problem, and several recent works have started to investigate the opportunity of applying deep learning (DL) techniques to solve this problem. In this paper, we study the fundamental problem of explainability of the DL solution for ER. Understanding the matching predictions of an ER solution is indeed crucial to assess the trustworthiness of the DL model and to discover its biases. We treat the DL model as a black box classifier and - while previous approaches to provide explanations for DL predictions are agnostic to the classification task - we propose the CERTA approach that is aware of the semantics of the ER problem. Our approach produces both saliency explanations, which associate each attribute with a saliency score, and counterfactual explanations, which provide examples of values that can flip the prediction. CERTA builds on a probabilistic framework that aims at computing the explanations evaluating the outcomes produced by using perturbed copies of the input records. We experimentally evaluate CERTA'S explanations of state-of-the-art ER solutions based on DL models using publicly available datasets, and demonstrate the effectiveness of CERTA over recently proposed methods for this problem.

Link Prediction (LP) aims at tackling Knowledge Graph incompleteness by inferring new, missing facts from the already known ones. The rise of novel Machine Learning techniques has led researchers to develop LP models that represent Knowledge Graph elements as vectors in an embedding space. These models can outperform traditional approaches and they can be employed in multiple downstream tasks; nonetheless, they tend to be opaque, and are mostly regarded as black boxes. Their lack of interpretability limits our understanding of their inner mechanisms, and undermines the trust that users can place in them. In this paper, we propose the novel Kelpie explainability framework. Kelpie can be applied to any embedding-based LP models independently from their architecture, and it explains predictions by identifying the combinations of training facts that have enabled them. Kelpie can extract two complementary types of explanations, that we dub necessary and sufficient. We describe in detail both the structure and the implementation details of Kelpie, and thoroughly analyze its performance through extensive experiments. Our results show that Kelpie significantly outperforms baselines across almost all scenarios.

The latest generations of Link Prediction (LP) models rely on embeddings to tackle incompleteness in Knowledge Graphs, achieving great performance at the cost of interpretability. Their opaqueness limits the trust that users can place in them, hindering their adoption in real-world applications. We have recently introduced Kelpie, an explainability framework tailored specifically for embedding-based LP models. Kelpie can be applied to any embedding-based LP model, and supports two explanation scenarios that we have called necessary and sufficient. In this demonstration we showcase Kelpie's capability to explain the predictions of models based on vastly different architectures on the 5 major datasets in literature.

Entity resolution (ER) aims at identifying record pairs that refer to the same real-world entity. Recent works have focused on deep learning (DL) techniques, to solve this problem. While such works have brought tremendous enhancements in terms of effectiveness in solving the ER problem, understanding their matching predictions is still a challenge, because of the intrinsic opaqueness of DL based solutions. Interpreting and trusting the predictions made by ER systems is crucial for humans in order to employ such methods in decision making pipelines. We demonstrate CERTEM an explanation system for ER based on CERTA, a recently introduced explainability framework for ER, that is able to provide both saliency explanations, which associate each attribute with a saliency score, and counterfactual explanations, which provide examples of values that can flip a prediction. In this demonstration we will showcase how CERTEM can be effectively employed to better understand and debug the behavior of state-of-the-art DL based ER systems on data from publicly available ER benchmarks.

Knowledge Graphs (KGs) have found many applications in industrial and in academic settings, which in turn, have motivated considerable research efforts towards large-scale information extraction from a variety of sources. Despite such efforts, it is well known that even the largest KGs suffer from incompleteness; Link Prediction (LP) techniques address this issue by identifying missing facts among entities already in the KG. Among the recent LP techniques, those based on KG embeddings have achieved very promising performance in some benchmarks. Despite the fast-growing literature on the subject, insufficient attention has been paid to the effect of the design choices in those methods. Moreover, the standard practice in this area is to report accuracy by aggregating over a large number of test facts in which some entities are vastly more represented than others; this allows LP methods to exhibit good results by just attending to structural properties that include such entities, while ignoring the remaining majority of the KG. This analysis provides a comprehensive comparison of embedding-based LP methods, extending the dimensions of analysis beyond what is commonly available in the literature. We experimentally compare the effectiveness and efficiency of 18 state-of-the-art methods, consider a rule-based baseline, and report detailed analysis over the most popular benchmarks in the literature.

Wrapper inference deals in generating programs to extract data from Web pages. Several supervised and unsupervised wrapper inference approaches have been proposed in the literature. On one hand, unsupervised approaches produce erratic wrappers: whenever the sources do not satisfy underlying assumptions of the inference algorithm, their accuracy is compromised. On the other hand, supervised approaches produce accurate wrappers, but since they need training data, their scalability is limited. The recent advent of crowdsourcing platforms has opened new opportunities for supervised approaches, as they make possible the production of large amounts of training data with the support of workers recruited online. Nevertheless, involving human workers has monetary costs. We present an original hybrid crowd-machine wrapper inference system that offers the benefits of both approaches exploiting the cooperation of crowd workers and unsupervised algorithms. Based on a principled probabilistic model that estimates the quality of wrappers, humans workers are recruited only when unsupervised wrapper induction algorithms are not able to produce sufficiently accurate solutions.

In Codice Ratio is a research project to study tools and techniques for analyzing the contents of historical documents conserved in the Vatican Secret Archives (VSA). In this paper, we present our efforts to develop a system to support the transcription of medieval manuscripts. The goal is to provide paleographers with a tool to reduce their efforts in transcribing large volumes, as those stored in the VSA, producing good transcriptions for significant portions of the manuscripts. We propose an original approach based on character segmentation. Our solution is able to deal with the dirty segmentation that inevitably occurs in handwritten documents. We use a convolutional neural network to recognize characters, and statistical language models to compose word transcriptions. Our approach requires minimal training effort, making the transcription process more scalable, as the production of training sets requires a few pages and can be easily crowdsourced. We have conducted experiments on manuscripts from the Vatican Registers, an unreleased corpus containing the correspondence of the popes. With training data produced by 120 high school students, our system has been able to produce good transcriptions that can be used by paleographers as a solid basis to speedup the transcription process at a large scale.

An increasing number of product pages are available from thousands of web sources, each page associated with a product, containing its attributes and one or more product identifiers. The sources provide overlapping information about the products, using diverse schemas, making web-scale integration extremely challenging. In this paper, we take advantage of the opportunity that sources publish product identifiers to perform big data linkage across sources at the beginning of the data integration pipeline, before schema alignment. To realize this opportunity, several challenges need to be addressed: identifiers need to be discovered on product pages, made difficult by the diversity of identifiers; the main product identifier on the page needs to be identified, made difficult by the many related products presented on the page; and identifiers across pages need to beresolved, made difficult by the ambiguity between identifiers across product categories. We present our RaF (Redundancy as Friend) solution to the problem of big data linkage for product specification pages, which takes advantage of the redundancy of identifiers at a global level, and the homogeneity of structure and semantics at the local source level, to effectively and efficiently link millions of pages of head and tail products across thousands of head and tail sources. We perform a thorough empirical evaluation of our RaF approach using the publicly available Dexter dataset consisting of 1.9M product pages from 7.1k sources of 3.5k websites, and demonstrate its effectiveness in practice.

General solutions to augment Knowledge Graphs (KGs) with facts extracted from Web tables aim to associate pairs of columns from the table with a KG relation based on the matches between pairs of entities in the table and facts in the KG. These approaches suffer from intrinsic limitations due to the incompleteness of the KGs. In this paper we investigate an alternative solution, which leverages the patterns that occur on the schemas of a large corpus of Wikipedia tables. Our experimental evaluation, which used DBpedia as reference KG, demonstrates the advantages of our approach over state-of-the-art solutions and reveals that we can extract more than 1.7M of facts with an estimated accuracy of 0.81 even from tables that do not expose any fact on the KG.

Tables and structured lists on Web pages are a potential source of valuable information, and several methods have been proposed to annotate them with semantics that can be leveraged for search, question answering and information extraction. This paper is concerned with the specific problem of finding and ranking relations from a given Knowledge Graph (KG) that hold over pairs of entities juxtaposed in a table or structured list. The state-of-the-art for this task is to attempt to link the entities mentioned in the table cells to objects in the KG and rank the relations that hold for those linked objects. As a result, these methods are hampered by the incompleteness and uneven coverage in even the best knowledge graphs available today. The alternative described here does not require entity linking, relying instead on ranking relations using generative language models derived from Web-scale corpora. As such, it can produce quality results even when the entities in the table are missing in the KG. The experimental validation, designed to expose the challenges posed by KG incompleteness, shows that our approach is robust and effective in practice.

Many approaches have been introduced recently to automatically create or augment Knowledge Graphs (KGs) with facts extracted from Wikipedia, particularly its structured components like the infoboxes. Although these structures are valuable, they represent only a fraction of the actual information expressed in the articles. In this work, we quantify the number of highly accurate facts that can be harvested with high precision from the text of Wikipedia articles using information extraction techniques bootstrapped from the entities and relations already in a KG. Our experimental evaluation, which uses Freebase as reference KG, reveals we can augment several relations in the domain of people by more than 10%, with facts whose accuracy are over 95%. Moreover, the vast majority of these facts are missing from the infoboxes, YAGO and DBpedia.

The development of solutions to scale the extraction of data from Web sources is still a challenging issue. High accuracy can be achieved by supervised approaches but the costs of training data, i.e., annotations over a set of sample pages, limit their scalability. Crowd sourcing platforms are making the manual annotation process more affordable. However, the tasks demanded to these platforms should be extremely simple, to be performed by non-expert people, and their number should be minimized, to contain the costs. We introduce a framework to support a supervised wrapper inference system with training data generated by the crowd. Training data are labeled values generated by means of membership queries, the simplest form of queries, posed to the crowd. We show that the costs of producing the training data are strongly affected by the expressiveness of the wrapper formalism and by the choice of the training set. Traditional supervised wrapper inference approaches use a statically defined formalism, assuming it is able to express the wrapper. Conversely, we present an inference algorithm that dynamically chooses the expressiveness of the wrapper formalism and actively selects the training set, while minimizing the number of membership queries to the crowd. We report the results of experiments on real web sources to confirm the effectiveness and the feasibility of the approach.

We present an unsupervised approach for harvesting the data exposed by a set of structured and partially overlapping data-intensive web sources. Our proposal comes within a formal framework tackling two problems: the data extraction problem, to generate extraction rules based on the input websites, and the data integration problem, to integrate the extracted data in a unified schema. We introduce an original algorithm, WEIR, to solve the stated problems and formally prove its correctness. WEIR leverages the overlapping data among sources to make better decisions both in the data extraction (by pruning rules that do not lead to redundant information) and in the data integration (by reflecting local properties of a source over the mediated schema). Along the way, we characterize the amount of redundancy needed by our algorithm to produce a solution, and present experimental results to show the benefits of our approach with respect to existing solutions.

Exploiting the huge amount of data available on the Web involves the generation of wrappers to extract data from web pages. We argue that existing approaches for web data extraction from data-intensive websites miss the opportunities related to the presence of redundant information on the Web. We propose an innovative approach that aims at pushing further the level of automation of existing wrapper generation systems by leveraging the redundancy of data on the Web. An experimental evaluation of the proposed solution shows a relevant improvement for the precision of the extracted data, without a significant loss in the recall.

A large number of web sites publish pages containing structured information about recognizable concepts, but these data are only partially used by current applications. Although such information is spread across a myriad of sources, the web scale implies a relevant redundancy. We present a domain independent system that exploits the redundancy of information to automatically extract and integrate data from the Web. Our solution concentrates on sources that provide structured data about multiple instances from the same conceptual domain, e.g., financial data, product information. Our proposal is based on an original approach that exploits the mutual dependency between the data extraction and the data integration tasks. Experiments on a sample of 175,000 pages confirm the feasibility and quality of the approach.

A large number of web sites publish pages containing structured information about recognizable concepts, but these data are only partially used by current applications. Although such information is spread across a myriad of sources, the web scale implies a relevant redundancy. We present a domain independent system that exploits the redundancy of information to automatically extract and integrate data from the Web. Our solution concentrates on sources that provide structured data about multiple instances from the same conceptual domain, e.g. financial data, product information. Our proposal is based on an original approach that exploits the mutual dependency between the data extraction and the data integration tasks. Experiments confirmed the quality and the feasibility of the approach.

Several Web sites deliver a large number of pages, each publishing data about one instance of some real world entity, such as an athlete, a stock quote, a book. Even though it is easy for a human reader to recognize these instances, current search engines are unaware of them. Technologies for the Semantic Web aim at achieving this goal; however, so far they have been of little help in this respect, as semantic publishing is very limited.

Data-intensive Web sites are large sites based on a back-end database, with a fairly complex hypertext structure. The paper develops two main contributions: (a) a specific design methodology for data-intensive Web sites, composed of a set of steps and design transformations that lead from a conceptual specification of the domain of interest to the actual implementation of the site; (b) a tool called H omer , conceived to support the site design and implementation process, by allowing the designer to move through the various steps of the methodology, and to automate the generation of the code needed to implement the actual site.Our approach to site design is based on a clear separation between several design activities, namely database design, hypertext design, and presentation design. All these activities are carried on by using high-level models, all subsumed by an extension of the nested relational model; the mappings between the models can be nicely expressed using an extended relational algebra for nested structures. Based on the design artifacts produced during the design process, and on their representation in the algebraic framework, H omer is able to generate all the code needed for the actual generation of the site, in a completely automatic way.

No abstract available.

Large Web sites are becoming repositories of structured information that can benefit from being viewed and queried as relational databases. However, querying these views efficiently requires new techniques. Data usually resides at a remote site and is organized as a set of related HTML documents, with network access being a primary cost factor in query evaluation. This cost can be reduced by exploiting the redundancy often found in site design. We use a simple data model, a subset of the Araneus data model, to describe the structure of a Web site. We augment the model with link and inclusion constraints that capture the redundancies in the site. We map relational views of a site to a navigational algebra and show how to use the constraints to rewrite algebraic expressions, reducing the number of network accesses. We show that similar techniques can be used to maintain materialized views over sets of HTML pages.

We present HOMER, a CASE tool for building and maintaining complex, data-intensive Web sites. In HOMER the processes of creation and maintenance of a Web site are completely based on the adoption of suitable models, to describe the various aspects of the site (content navigation structure, presentation). The development of a site does not require any code writing activity: based on the results of the design process, the system automatically creates programs to implement the site, statically and/or dynamically, as needed; also, the system does not depend on any specific tool or language: it has a modular architecture, which integrates external servers for specific tasks; finally, the system sup- ports site administrators for several maintenance activities, which can involve changes over the site at different levels.

The paper describes the ARANEUS Wel-Base Management System, a system developed at University Roma Tre, which represents a proposal towards the definition of a new kind of data-repository, designed to manage Web data in the database style. We call a Web-Base a collection of data of heterogeneous nature, and more specifically: (i) highly structured data, such as the ones typically stored in relational or object-oriented database systems; (ii) semistructured data, in the Web style. We can simplify by saying that it incorporates both databases and Web sites. A Web-Base Management System (WBMS) is a system for managing such Web-bases

Myocardial perfusion information is crucial in the clinical evaluation of patients with coronary artery disease. A software tool dedicated to the automated analysis of contrast echocardiographic images for the evaluation of myocardial perfusion is presented in this paper. The system recognizes the internal border of the heart during the transit of an ultrasound contrast bolus injection and draws regions of interest. For each region of interest, time intensity curves are derived in order to quantify segmental perfusion.

The authors consider structured Web sites, those sites in which structures are so tight and regular that one can assimilate the site, from the logical viewpoint, to a conventional database. They have argued that, with respect to structured Web servers, it is possible to apply ideas from traditional database techniques, specifically with respect to design, query, and update. They focus on the querying process, which consists of associating a scheme with a server and then using this scheme to pose queries in a high level query language. To describe the scheme, they use a specific data model, called the ARANEUS Data Model (ADM). They call ADM a page oriented model, in the sense that the main construct of the model is that of a page scheme, used to describe the structure of sets of homogeneous pages in the server. ADM schemes are then offered to the user, who can query them using the ULIXES language, whose expressions produce relations as results. These are essentially relational views over Web data and can therefore be queried using any relational query language. It should be noted that the approach inherited some ideas from other proposals for query languages for the Web. However, these approaches are mainly based on a loose notion of structure, and tend to view the Web as a huge collection of unstructured objects, organized as a graph. In contrast, the approach explicitly considers structure, both in the information source (the Web) and in the derived information (the relational views).