Natural Language Query Processing for Model Management

The communication between an MIS and 1ts users would be greatly facilitated if the users could query and instruct the system in a sufficiently large subset of their natural language that the system appears to be conversing in the\u27 1 anguage. In large measure this has been accomplished for MISs that retrieve and display stored data and that perform simple calcul ations (summations, plots, regressions) with the data. A number of natural 1 anguage database query sys-· terns have been developed, a few of which are now commerci ally available. However, little attention has been , pald to the development of natural language interfaces for systems containing decision models. This paper examines the issues that may arise in the development of natural language , query processors for model management systems. In this paper we address four topics. The first ls the state-of-the-art in natural language database query processing. The principal issues here are the parsing of sentences and the resolution of ambiguities. The ambiguities may be those internal to a sentence (such as misspelllngs, ambiguities in the meanings of words, and ambiguities inherent in the syntax of the language in which the query is written), ambiguities resulting from explicit or implicit reference to previous queries (such as the use of pronouns whose referents must be identified), and ambiguities that arise when several flles must be combined to respond to a single query. These issues have been examined in detail and may provide a foundation for natural language model query processing. The second issue is the development of a high-level target language -- a well-structured, user-friendly, machineindependent language into which natural language queries will be translated prior to model execution. A target language for model management, called Mal (Model Query Language), has been designed, and its linguistic properties have been investigated. The language is described, and some exampl es are given. The thi rd issue is the structure of the model query transl ator. The transl ator w111 consist of five components. The Parsing Component receives the query from the user and analyzes it. It identifies the functions to be performed (e.g., optimization, sensitivity analysis), identifies the inputs and outputs of the models to be used, and attempts to resolve ambiguities. The Model Definition Component is used by the model buil der to define the inpu© and outputs of the model s l n the model bank. The Memory r_Aponent contai ns the model definitions, any previous queries (ln case reference, such as pronoun reference, is made to them), and information about possibl e spel 1 ing errors and synonyms. The Model Processing Component executes the model or model s needed to prepare a response, and the Report Writing Component formats the response. The final issue is the possible integration of data management and model management in a way that allows users to enter a natural 1 anguage query that requires access to both databases and model banks. Such an integration may eventual ly 1 ead to the devel opment of systems that provide a comprehensive range of decision support services.

Learning from AI : new trends in database technology

Recently some researchers in the areas of database data modelling and knowledge representations in artificial intelligence have recognized that they share many common goals. In this survey paper we show the relationship between database and artificial intelligence research. We show that there has been a tendency for data models to incorporate more modelling techniques developed for knowledge representations in artificial intelligence as the desire to incorporate more application oriented semantics, user friendliness, and flexibility has increased. Increasing the semantics of the representation is the key to capturing the "reality" of the database environment, increasing user friendliness, and facilitating the support of multiple, possibly conflicting, user views of the information contained in a database

Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems

THE RISE OF AI IN CONTENT MANAGEMENT: REIMAGINING INTELLIGENT WORKFLOWS

As content management systems (CMS) become indispensable for managing digital experiences, AI integration promises to bring new levels of automation and intelligence to streamline workflows. This paper surveys how AI techniques like machine learning, natural language processing, computer vision, and knowledge graphs are transforming CMS capabilities across the content lifecycle. We analyze key use cases like automated metadata tagging, natural language generation, smart recommendations, predictive search, personalized experiences, and conversational interfaces. The benefits include enhanced content discoverability, accelerated creation, improved optimization, simplified governance, and amplified team productivity. However, adoption remains low due to challenges like opaque AI, poor workflow integration, unrealistic expectations, bias risks, and skills gaps. Strategic priorities include starting with focused pilots, evaluating multiple AI approaches, emphasizing transparent and fair AI models, and upskilling teams. Benefits are maximized through hybrid human-AI collaboration vs full automation. While AI integration is maturing, the outlook is cautiously optimistic. Leading CMS platforms are accelerating development of no-code AI tools. But mainstream adoption may take 2-5 years as skills and best practices evolve around transparent and ethical AI. Wise data practices, change management, and participatory design will be key. If implemented thoughtfully, AI can reimagine workflows by expanding human creativity, not replacing it. The future points to creative synergies between empowered users and AI assistants. But pragmatic pilots, continuous improvement, and participatory strategies are necessary to navigate the hype and deliver value. The promise warrants measured experimentation

CREOLE: a Universal Language for Creating, Requesting, Updating and Deleting Resources

In the context of Service-Oriented Computing, applications can be developed following the REST (Representation State Transfer) architectural style. This style corresponds to a resource-oriented model, where resources are manipulated via CRUD (Create, Request, Update, Delete) interfaces. The diversity of CRUD languages due to the absence of a standard leads to composition problems related to adaptation, integration and coordination of services. To overcome these problems, we propose a pivot architecture built around a universal language to manipulate resources, called CREOLE, a CRUD Language for Resource Edition. In this architecture, scripts written in existing CRUD languages, like SQL, are compiled into Creole and then executed over different CRUD interfaces. After stating the requirements for a universal language for manipulating resources, we formally describe the language and informally motivate its definition with respect to the requirements. We then concretely show how the architecture solves adaptation, integration and coordination problems in the case of photo management in Flickr and Picasa, two well-known service-oriented applications. Finally, we propose a roadmap for future work.Comment: In Proceedings FOCLASA 2010, arXiv:1007.499