In the modern data-driven world, answering queries over heterogeneous and semantically inconsistent data remains a significant challenge. Modern datasets originate from diverse sources, such as relational databases, semi-structured repositories, and unstructured documents, leading to substantial variability in schemas, terminologies, and data formats. Traditional systems, constrained by rigid syntactic matching and strict data binding, struggle to capture critical semantic connections and schema ambiguities, failing to meet the growing demand among data scientists for advanced forms of flexibility and context-awareness in query answering. In parallel, the advent of Large Language Models (LLMs) has introduced new capabilities in natural language interpretation, making them highly promising for addressing such challenges. However, LLMs alone lack the systematic rigor and explainability required for robust query processing and decision-making in high-stakes domains. In this paper, we propose Soft Query Answering (Soft QA), a novel hybrid approach that integrates LLMs as an intermediate semantic layer within the query processing pipeline. Soft QA enhances query answering adaptability and flexibility by injecting semantic understanding through context-aware, schema-informed prompts, and leverages LLMs to semantically link entities, resolve ambiguities, and deliver accurate query results in complex settings. We demonstrate its practical effectiveness through real-world examples, highlighting its ability to resolve semantic mismatches and improve query outcomes without requiring extensive data cleaning or restructuring.

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.

We introduce landmark grammars , a new family of context-free grammars aimed at describing the HTML source code of pages published by large and templated websites and therefore at effectively tackling Web data extraction problems. Indeed, they address the inherent ambiguity of HTML, one of the main challenges of Web data extraction, which, despite over twenty years of research, has been largely neglected by the approaches presented in literature. We then formalize the Smallest Extraction Problem (SEP), an optimization problem for finding the grammar of a family that best describes a set of pages and contextually extract their data. Finally, we present an unsupervised learning algorithm to induce a landmark grammar from a set of pages sharing a common HTML template, and we present an automatic Web data extraction system. The experiments on consolidated benchmarks show that the approach can substantially contribute to improve the state-of-the-art.

Motivated by our experience in building the Enterprise Knowledge Graph of Italian companies for the Central Bank of Italy, in this paper we present an in-depth case analysis of company ownership graphs, graphs having company ownership as a central concept. In particular, we study and introduce three industrially relevant problems related to such graphs: company control, asset eligibility and detection of personal links. We formally characterize the problems and present Vada-Link, a framework based on state-of-the-art approaches for knowledge representation and reasoning. With our methodology and system, we solve the problems at hand in a scalable, model-independent and generalizable way. We illustrate the favourable architectural properties of Vada-Link and give experimental evaluation of the approach.

The definition of mappings between heterogeneous schemas is a critical activity of any database application. Existing tools provide high level interfaces for the discovery of correspondences between elements of schemas, but schema mappings need to be manually specified every time from scratch, even if the scenario at hand is similar to one that has already been addressed. The problem is that schema mappings are precisely defined over a pair of schemas and cannot directly be reused on different scenarios. We tackle this challenge by generalizing schema mappings as meta-mappings: formalisms that describe transformations between generic data structures called meta-schemas. We formally characterize schema mapping reuse and explain how meta-mappings are able to: (i) capture enterprise knowledge from previously defined schema mappings and (ii) use this knowledge to suggest new mappings. We develop techniques to infer meta-mappings from existing mappings, to organize them into a searchable repository, and to leverage the repository to propose to users mappings suitable for their needs. We study effectiveness and efficiency in an extensive evaluation over real-world scenarios and show that our system can infer, store, and search millions of meta-mappings in seconds.

Domain-specific web applications often need to integrate information from schematically heterogeneous sources that share some semantic similarities. These applications often include application widgets—where each widget may address a (potentially small) subset of the local schema. We seek to provide flexible integration where each widget may use its own “global” schema and use its own mapping to each local schema. It is possible for each such “global” schema to be mapped multiple times, in different ways, to a given local schema. Traditional information integration is too rigid to meet these requirements. Here, we define a new integration model that introduces a metamodel of small domain-specific schema fragments—called domain structures—that can be mapped to local schemas. We show how generic, polymorphic widgets can be created by writing queries against domain structures using an extended relational algebra that includes a local type operator to propagate local type names to the domain structures. By bringing the local semantics to the global level we create an integration system with local dominance where important, distinct local schema semantics are available globally.

Data processing is the core of any statistical information system. Statisticians are interested in specifying transformations and manipulations of data at a high level, in terms of entities of statistical models such as time series. We illustrate here an experience at the Bank of Italy where (i) a language, EXL, has been defined for the declarative specification of statistical programs, (ii) an approach for the translation of EXL code into executables in various target systems has been developed, and (iii) a concrete implementation, EXLEngine, has been carried out. The approach leverages on schema mappings as an intermediate specification step, in order to facilitate the translation from EXL towards several target systems.

The recent growth of non-relational databases (often termed as NoSQL) is an interesting phenomenon that has generated both interest and criticism. One of the major drawbacks that is often referred to is the heterogeneity of the languages and interfaces they offer to developers and users.

A runtime approach to model-generic translation of schema and data is proposed.

We discuss the use of a Datalog extension that refers to a data model with inheritance in order to manage the generic dictionary of MIDST, our Model Management proposal for the generation of translation of schemas and databases from a model to another. In comparable scenarios, with structural similarities of predicates of the data model and syntactical and semantical similarities of rules, the use of hierarchies and a sort of polymorphism provide a significant simplification in the definition of complete translations (Datalog programs) and a higher level of reuse in the specification of elementary translations (Datalog rules) thus simplifying the development of such rule based systems.

MIDST is a tool for the translation of schemas and databases from a model to another, in a framework that is flexible and extensible with respect to the family of models. The major novelties with respect to existing proposals consist in the generation of data-level translations and on the customizability of translations (at both schema and data level).

The need to transform, integrate and exchange data is common to many application contexts. In databases, we often use different systems to handle data, with different models, and we therefore need to translate data and their description from one to another. The problem has been considered for decades, but definitive solutions are not yet available. The problem is relevant at the schema level, during the specification or design phase, and at the data level, when we have databases, and we want to translate them into some other system, which may be similar (for example, relational to relational) or completely different (for example, XML to relational or viceversa). In current practice, translation problems are often tackled by means of ad-hoc solutions, for example by writing code for each specific application, but this is clearly very heavy and hard to maintain.

A customizable and extensible tool is proposed to implement ModelGen, the model management operator that translates a schema from one model to another. A wide family of models is handled, by using a metamodel in which models can be succinctly and precisely described. The approach is novel because the tool exposes the dictionary that stores models, schemas, and the rules used to implement translations. In this way, the transformations can be customized and the tool can be easily extended.

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.

We propose a comprehensive framework for the management and the exchange of (semi) structured Web data, described according to a variety of formats and models. We consider various schema definition languages for XML (DTD, XML Schema and XDR) a model for semistructured data (OEM) and a model used to store Web data (the relational model) and show that the primitives adopted by all of them can be classified into a rather limited set of basic types. We then define, building on these basic types, a notion of "meta-formalism" that can be used to describe, in a uniform way, these heterogeneous representations of Web data. In this framework, the translation of schemes and instances between different models are based on the translations of the involved primitives. Complex translations can be then obtained by simply combining a number of predefined operations, which implement standard translations between primitives. Moreover we show that, for translating between any pair of models, it is sufficient to define a number of translations which is linear in the number of models. These results can be used to support a number of involved Web-related activities like: information exchange between different organizations, integration of data coming from heterogeneous information sources, storage of native XML data in a DBMS and publishing of existing structured (relational) data in XML.

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.

We propose new criteria for the classification of systems for database cooperation, based on the nature of the component databases. In fact, the traditional criteria - heterogeneity, distribution, and autonomy - are often constraints for the design process, rather than design parameters. In this case, other features of the component databases should be addressed. We claim that a more useful classification can be based on three new criteria: (a) degree of transparency, (b) complexity of operations, and (c) level of liveliness of data. This leads us to distinguish three main categories of systems: (i) multidatabases, (ii) data warehouses, and (iii) local information systems with external data. For each of these categories, we discuss implementations based on tools offered by currently available technology

The weak instance model is a framework for considering the relations in a database as a whole, regardless of the way attributes are grouped in the individual relations. Queries and updates can be performed for any set of attributes. The management of updates is based on a lattice structure on the set of legal states, and inconsistencies and ambiguities can arise. In the general case, the test for consistency and determinism may involve the whole database. In this paper it is shown how, for the highly significant class of independent schemes, updates can be handled efficiently, considering only the relevant portion of the database.

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

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).

The paper develops EDITOR, a language for manipulating semi-structured documents, such as the ones typically available on the Web. EDITOR programs allow to search and restructure a document. They are based on two simple ideas, taken from text editors: Search” instructions are used to select regions of interest in a document, and “cut & paste” to restructure them. We study the expressive power and the complexity of these programs. We show that they are computationally complete, in the sense that any computable document restructuring can be expressed in EDITOR. We also study the complexity of a safe subclass of programs, showing that it captures exactly the class of polynomial-time restructurings. The language has been implemented in Java, and is used in the ARANEUS project to build database views over Web sites.

MDM is a tool that enables the users to define schemes of different data models and to perform translations of schemes from one model to another. These functionalities can be at the basis of a customizable and integrated CASE environment supporting the analysis and design of information systems. MDM has two main components: the Model Manager and the Schema Manager. The Model Manager supports a specialized user, the model engineer, in the definition of a variety of models, on the basis of a limited set of metaconstructs covering almost all known conceptual models. The Schema Manager allows designers to create and modify schemes over the defined models, and to generate at each time a translation of a scheme into any of the data models currently available. Translations between models are automatically derived, at definition time, by combining a predefined set of elementary transformations, which implement the standard translations between simple combinations of constructs.

The IsaLog model and language are presented. The model has complex objects with classes, relations, and is a hierarchies. The language is strongly types and declarative. The main issue is the definition of the semantics of the language, given in three different ways that are shown to be equivalent: a model-theoretic semantics, a reduction to logic programming with function symbols, and a fixpoint semantics. Each of the semantics presents new aspects with respect to existing proposals because of the interaction of oid-invention with general is a hierarchies. The solutions are based on the explicit Skolem functors, which provide a powerful tool for manipulating object-identifiers.>

The problem of updating databases through interfaces based on the weak instance model is studied, thus extending previous proposals that considered them only from the query point of view. Insertions and deletions of tuples are considered. As a preliminary tool, a lattice on states is defined, based on the information content of the various states. Potential results of an insertion are states that contain at least the information in the original state and that in the new tuple. Sometimes there is no potential result, and in the other cases there may be many of them. We argue that the insertion is deterministic if the state that contains the information common to all the potential results (the greatest lower bound, in the lattice framework) is a potential result itself. Effective characterizations for the various cases exist. A symmetric approach is followed for deletions, with fewer cases, since there are always potential results; determinism is characterized as a consequence.

The weak instance model is a framework to consider the relations in a database as a whole, regardless of the way attributes are grouped in the individual relations. Queries and updates can be performed involving any set of attributes. The management of updates is based on a lattice structure on the set of legal states, and inconsistencies and ambiguities can arise

Database updates have recently received much more attention than in the past. In this trend, a solid foundation is provided to the problem of updating databases through interfaces based on the weak instance model. Insertions and deletions of tuples are considered.

Simple chase join expressions are relational algebra expressions, involving only projection and join operators, defined on the basis of the functional dependencies associated with the database scheme. They are meaningful in the weak instance model, because for certain classes of schemes, including independent schemes, the total projections of the representative instance can be computed by means of unions of simple chase join expressions. We show how unions of simple chase join expressions can be optimized efficiently, without constructing and chasing the corresponding tableaux. We also present efficient algorithms for testing containment and equivalence, and for optimizing individual simple chase join expressions.

A new definition of the weak instance model is presented, which does not consider the missing values as existent though unknown, but just assumes that no information is available about them. It is possible to associate with the new definition logical theories that do not contain universally quantified variables. The new model enjoys various desirable properties of the old weak instance model, with respect to dependency satisfaction, query answering, and associated logical theories.

In the spirit of integrating data base and artificial intelligence techniques, a number of concepts widely used in relational data base theory are introduced in a knowledge representation scheme. A simple network model, which allows the representation of types, is-a relationships and disjointness constraints is considered. The concepts of consistency and redundancy are introduced and characterized by means of implication of constraints and systems of inference rules, and by means of graph theoretic concepts.

We propose a class of database schemes called connection-trap-lree schemes that allows users to retrieve sound and complete information easily and efficiently from the database.We argue that with this class of database schemes, the connection trap problem can be avoided.We present 'some fundamental properties of this class of schemes.We then characterize the class 01 independent and connection-trapfree schemes when an embedded cover of functional dependencies is assumed.

Article Free Access Share on Efficient query answering in the representative instance approach Authors: Paolo Atzeni View Profile , Edward P. F. Chan View Profile Authors Info & Claims PODS '85: Proceedings of the fourth ACM SIGACT-SIGMOD symposium on Principles of database systemsMarch 1985 Pages 181–188https://doi.org/10.1145/325405.325429Published:25 March 1985Publication History 19citation68DownloadsMetricsTotal Citations19Total Downloads68Last 12 Months3Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Many results in relational database theory on the structure of dependencies, query languages, and databases in general have now been established. However, neither (a) the reliance of these results on various assumptions, nor (b) the desirability or reasonableness of these assumptions themselves have been closely examined. These assumptions are nontrivial: examples include the universal relation assumption and the lossless join assumption.The purpose of the present paper is to clarify many of the existing assumptions, and point out weaknesses. This is desirable both to harden the statements of previous results, and to evaluate recent suggestions that certain assumptions (such as the acyclic JD assumption) may be useful for modeling "real world" databases. Specifically, studies are made of assumptions made for (1) universal relations, (2) functional dependency inference, and (3) decomposition theory. We show that:• Some assumptions (such as uniqueness of relationships among attributes) can be more powerful than they appear;• common treatment of FDs is sometimes inappropriate, and for example FD inferences such as {A → B, B → C} |= A → C can be incorrect;• the 'decomposition' approach to design may be hard to justify in real terms; and• Acyclic JDs may have drawbacks in eliminating ambiguity in queries and in modeling real enterprises.It is hoped that this exposition will help clarify some confusing issues in this field, and will lead to a better understanding of which assumptions are reasonable and useful in modeling the "real world".