Nowadays, the popularity of chatbots, such as ChatGPT, has brought research attention to question answering systems, capable to generate natural language answers to user’s natural language queries. However, also in other kinds of systems, flexibility of querying, including but also going beyond the use of natural language, is an important feature. With this consideration in mind the paper presents a critical and comprehensive analysis of recent developments, trends and challenges of Flexible Query Answering Systems (FQASs). Flexible query answering is a multidisciplinary research field that is not limited to question answering in natural language, but comprises other query forms and interaction modalities, which aim to provide powerful means and techniques for better reflecting human preferences and intentions to retrieve relevant information. It adopts methods at the crossroad of several disciplines among which Information Retrieval (IR), databases, knowledge based systems, knowledge and data engineering, Natural Language Processing (NLP) and the semantic web may be mentioned. The analysis principles are inspired by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) framework, characterized by a top-down process, starting with relevant keywords for the topic of interest to retrieve relevant articles from meta-sources And complementing these articles with other relevant articles from seed sources Identified by a bottom-up process. to mine the retrieved publication data a network analysis is performed Which allows to present in a synthetic way intrinsic topics of the selected publications. issues dealt with are related to query answering methods Both model-based and data-driven (the latter based on either machine learning or deep learning) And to their needs for explainability and fairness to deal with big data Notably by taking into account data veracity. conclusions point out trends and challenges to help better shaping the future of the FQAS field.

This paper describes principles and structure for a software system that implements a dialect of natural logic for knowledge bases. Natural logics are formal logics that resemble stylized natural language fragments, and whose reasoning rules reflect common-sense reasoning. Natural logics may be seen as forms of extended syllogistic logic. The paper proposes and describes realization of deductive querying functionalities using a previously specified natural logic dialect called Natura-Log. In focus here is the engineering of an inference engine employing as a key feature relational database operations. Thereby the inference steps are subjected to computation in bulk for scaling-up to large knowledge bases. Accordingly, the system eventually is to be realized as a general-purpose database application package with the database being turned logical knowledge base.

This paper describes the principles of a system applying natural logic as a knowledge base language. Natural logics are regimented fragments of natural language employing high level inference rules. We advocate the use of natural logic for knowledge bases dealing with querying of classes in ontologies and class-relationships such as are common in life-science descriptions. The paper adopts a version of natural logic with recursive restrictive clauses such as relative clauses and adnominal prepositional phrases. It includes passive as well as active voice sentences. We outline a prototype for partial translation of natural language into natural logic, featuring further querying and conceptual path finding in natural logic knowledge bases.

We argue in favour of adopting a form of natural logic for ontology-structured knowledge bases as an alternative to description logic and rule based languages. Natural logic is a form of logic resembling natural language assertions, unlike description logic. This is essential e.g. in life sciences, where the large and evolving knowledge specifications should be directly accessible to domain experts. Moreover, natural logic comes with intuitive inference rules. The considered version of natural logic leans toward the closed world assumption (CWA) unlike the open world assumption with classical negation in description logic. We embed the natural logic in DATALOG clauses which is to take care of the computational inference in connection with querying

A summary is a concise description that reflects the essence of a subject. A text, a collection of text documents, or a query answer can be summarized by simple means such as an automatically generated list of the most frequent words or “advanced” by a meaningful natural language description of the subject. In between these two extremes, conceptual summaries encompass selected concepts derived using background knowledge. We address in this paper an approach where conceptual summaries are provided through a conceptualization as given by an ontology. The ontology guiding the summarization can be a simple taxonomy or a generative domain ontology. A domain ontology can be provided by a preanalysis of a domain corpus and can be used to condense improved summaries that better reflects the conceptualization of a given domain.

In this article, we introduce principles for ontology-based querying of information bases. We consider a framework in which a basis ontology over atomic concepts in combination with a concept language defines a generative ontology. Concepts are assumed to be the basis for an index of the information base, in the sense that these concepts are attached to objects in the information base. Concepts are thus applied to obtain a means for descriptions that generalize classical word-based information base indexing. We discuss how the ontology influences the matching of values, especially how the different relations of the ontology may contribute to overall similarity between concepts. Further, we discuss a set of major properties to improve a given similarity measure's accordance with the semantics of the ontology, and use these properties to guide the choice of function. Finally we implement a prototype search system to evaluate the chosen approach. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 739–761, 2007.

This paper introduces to the main objectives of the OntoQuery project, with the agenda of developing theories and methodologies for content-based retrieval from text databases. Content-based retrieval is obtained through descriptions of database objects and queries derived from natural language analysis and shaped with respect to a domain specific ontology, which in turn is expressed in a formal description language. Querying is performed by comparing descriptions and, during this, reasoning with the domain knowledge expressed in the ontology. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.