Stephen W. Liddle

Conceptual-model-based data extraction from multiple-record Web pages

Abstract Electronically available data on the Web is exploding at an ever increasing pace. Much of this data is unstructured, which makes searching hard and traditional database querying impossible. Many Web documents, however, contain an abundance of recognizable constants that together describe the essence of a documents content. For these kinds of data-rich, multiple-record documents (e.g., advertisements, movie reviews, weather reports, travel information, sports summaries, financial statements, obituaries, and many others) we can apply a conceptual-modeling approach to extract and structure data automatically. The approach is based on an ontology – a conceptual model instance – that describes the data of interest, including relationships, lexical appearance, and context keywords. By parsing the ontology, we can automatically produce a database scheme and recognizers for constants and keywords, and then invoke routines to recognize and extract data from unstructured documents and structure it according to the generated database scheme. Experiments show that it is possible to achieve good recall and precision ratios for documents that are rich in recognizable constants and narrow in ontological breadth. Our approach is less labor-intensive than other approaches that manually or semiautomatically generate wrappers, and it is generally insensitive to changes in Web-page format.

Conceptual Modeling-ER 2003

The Semantic Web and the Web service paradigm are currently the most important trends on the way to the next generation of the Web. They promise new opportunities for content and service provision, enabling manifold and flexible new applications and improved support for individual and cooperative tasks. The use of the Web service paradigm in the development of Web applications, that typically couple application databases with user dialogs, is quite obvious. The development of Web applications that can be operated effectively in the Semantic Web context (Semantic Web Applications), however, imposes some challenges. Two main challenges towards extended (conceptual) modeling support are addressed in this talk: 1. In the Semantic Web, Web applications move from a purely human user community towards a mixed user community consisting of humans as well as of software agents; this results into new requirements towards models for Web applications’ user interfaces; 2. Automatic interpretation of content, one of the main building blocks of the Semantic Web, is based on interlinking local models with globally defined interpretation schemes like vocabularies and ontologies; this has to be reflected by the conceptual application domain models of Semantic Web Applications. Conceptual Modeling for Web applications, thus, has to be revisited in the context of the new Web trends looking for adequate Semantic Web Application Models. In Web applications dialog-oriented (in most cases form-based) user interface models are state-of-the art for the interaction with users. The requirement of representing interaction with humans as well as with software agents is best met by a user interface model that describes the dialogs with the system on a conceptual level that can be dynamically translated into a (user) interface language adequate for the respective “user” (human or agent). The upcoming Web standard XForms for the next generation of form-based user interfaces is a good example of such a conceptual user interface model. For the linking of globally defined concepts with local domain model concepts one of the most popular models in the context of the Semantic Web is provided by the Resource Description Framework (RDF). The systematic integration of Uniform Resource Identifiers (URIs) into the model facilitates references to vocabularies and ontologies defined e.g. as RDF Schema or OWL ontology. However, for using RDF in Web applications a coupling between these “semantic” data models and the more traditional data models underlying the application data is necessary. I.-Y. Song et al. (Eds.): ER 2003, LNCS 2813, pp. 1–2, 2003. c

Ontology-based extraction and structuring of information from data-rich unstructured documents

We present a new approach to extracting information from unstructured documents based on an application ontology that describes a domain of interest. Starting with such an ontology, we formulate rules to extract constants and context keywords from unstructured documents. For each unstructured document of interest, we extract its constants and keywords and apply a recognizer to organize extracted constants as attribute values of tuples in a generated database schema. To make our approach general, we fix all the processes and change only the ontological description for a different application domain. In experiments we conducted on two different types of unstructured documents taken from the Web, our approach attained recall ratios in the 80% and 90% range and precision ratios near 98%.

Advances in Conceptual Modeling

Traditionally product data and their evolving definitions, have been handled separately from process data and their evolving definitions. There is little or no overlap between these two views of systems even though product and process data arc inextricably linked over the complete software lifecycle from design to production. The integration of product and process models in an unified data model provides the means by which data could be shared across an enterprise throughout the lifecycle, even while that data continues to evolve. In integrating these domains, an object oriented approach to data modelling has been adopted by the CRISTAL (Cooperating Repositories and an Information System for Tracking Assembly Lifecycles) project. The model that has been developed is description-driven in nature in that it captures multiple layers of product and process definitions and it provides object persistence, flexibility, reusability, schema evolution and versioning of data elements. This paper describes the model that has been developed in CRISTAL and how descriptive meta-objects in that model have their persistence handled. It concludes that adopting a description-driven approach to modelling, aligned with a use of suitable object persistence, can lead to an integration of product and process models which is sufficiently flexible to cope with evolving data definitions. Ke)fwords: Description-Driven systems. Modelling change, schema evolution, versioning

Conceptual Modeling — ER 2000

Model management is a framework for supporting meta-data related applications where models and mappings are manipulated as first class objects using operations such as Match, Merge, ApplyFunction, and Compose. To demonstrate the approach, we show how to use model management in two scenarios related to loading data warehouses. The case study illustrates the value of model management as a methodology for approaching meta-data related problems. It also helps clarify the required semantics of key operations. These detailed scenarios provide evidence that generic model management is useful and, very likely, implementable.

Extracting Data behind Web Forms

A significant and ever-increasing amount of data is accessible only by filling out HTML forms to query an underlying Web data source. While this is most welcome from a user perspective (queries are relatively easy and precise) and from a data management perspective (static pages need not be maintained and databases can be accessed directly), automated agents must face the challenge of obtaining the data behind forms. In principle an agent can obtain all the data behind a form by multiple submissions of the form filled out in all possible ways, but efficiency concerns lead us to consider alternatives. We investigate these alternatives and show that we can estimate the amount of remaining data (if any) after a small number of submissions and that we can heuristically select a reasonably minimal number of submissions to maximize the coverage of the data. Experimental results show that these statistical predictions are appropriate and useful.

Cardinality constraints in semantic data models

Abstract Constraints are central to the notion of a semantic data model. How well a model captures constraints affects its power and viability as a semantic data model. Cardinality constraints are an important subclass of general constraints. In this paper we provide formal definitions for cardinality constraints of several semantic models, as described in the literature. We construct a partial ordering of these constraints that shows the relative power expressed by each cardinality constraints. We discuss our results and offer possible extensions to contemporary cardinality constraint definitions. Our contributions include a collection and formal definition of existing cardinality constraints, a partial ordering of this set, and recommendations for cardinality constraint mechanisms in semantic data models.

A Conceptual-Modeling Approach to Extracting Data from the Web

Electronically available data on the Web is exploding at an ever increasing pace. Much of this data is unstructured, which makes searching hard and traditional database querying impossible. Many Web documents, however, contain an abundance of recognizable constants that together describe the essence of a document’s content. For these kinds of data-rich documents (e.g., advertisements, movie reviews, weather reports, travel information, sports summaries, financial statements, obituaries, and many others) we can apply a conceptual-modeling approach to extract and structure data. The approach is based on an ontology – a conceptual model instance – that describes the data of interest, including relationships, lexical appearance, and context keywords. By parsing the ontology, we can automatically produce a database scheme and recognizers for constants and keywords, and then invoke routines to recognize and extract data from unstructured documents and structure it according to the generated database scheme. Experiments show that it is possible to achieve good recall and precision ratios for documents that are rich in recognizable constants and narrow in ontological breadth.

Conceptual Modeling for E-Business and the Web

Towards Ontology-Based Harmonization of Web Content Standards.- The M*-COMPLEX Approach to Enterprise Modeling, Engineering, and Integration.- Conceptual Design of Electronic Product Catalogs Using Object-Oriented Hypermedia Modeling Techniques.- Generic Linear Business Process Modeling.- Business Modelling Is Not Process Modelling.- Modeling Electronic Workflow Markets.- Building Multi-device, Content-Centric Applications Using WebML and the W3I3 Tool Suite.- Abstraction and Reuse Mechanisms in Web Application Models.- From Web Sites to Web Applications: New Issues for Conceptual Modeling.- Using Webspaces to Model Document Collections on the Web.- Modeling Interactions and Navigation in Web Applications.- A General Methodological Framework for the Development of Web-Based Information Systems.- Managing RDF Metadata for Community Webs.- An Example-Based Environment for Wrapper Generation.- Flexible Category Structure for Supporting WWW Retrieval.

Perspectives in Conceptual Modeling

First International Workshop on Best Practices of UML (BP-UML 2005).- Preface to BP-UML 2005.- Experience Reports and new Applications.- Current Practices in the Use of UML.- An Empirical Study of the Nesting Level of Composite States Within UML Statechart Diagrams.- Utilizing a Multimedia UML Framework for an Image Database Application.- Model Evaluation and Requirements Modeling.- Object Class or Association Class? Testing the User Effect on Cardinality Interpretation.- Organizing and Managing Use Cases.- A Comparative Analysis of Use Case Relationships.- Metamodeling and Model Driven Development.- Applying Transformations to Model Driven Development of Web Applications.- A Precise Approach for the Analysis of the UML Models Consistency.- A UML 2 Profile for Business Process Modelling.- Seventh International Bi-conference Workshop on Agent-Oriented Information Systems (AOIS-2005).- Preface to AOIS 2005.- Invited Talk.- Agent Oriented Data Integration.- Positions in Engineering Agent Oriented Systems.- AOSE and Organic Computing - How Can They Benefit from Each Other? Position Paper.- Modeling Dynamic Engineering Design Processes in PSI.- Agent Oriented Methodologies and Conceptual Modeling.- Preliminary Basis for an Ontology-Based Methodological Approach for Multi-agent Systems.- DDEMAS: A Domain Design Technique for Multi-agent Domain Engineering.- An Agent-Oriented Meta-model for Enterprise Modelling.- Agent Communication and Coordination.- An Approach to Broaden the Semantic Coverage of ACL Speech Acts.- Normative Pragmatics for Agent Communication Languages.- Experimental Comparison of Rational Choice Theory, Norm and Rights Based Multi Agent Systems.- Second International Workshop on Conceptual Modeling for Geographic Information Systems (CoMoGIS 2005).- Preface to CoMoGIS 2005.- Invited Talk.- Map Algebra Extended with Functors for Temporal Data.- Spatial and Spatio-temporal Data Representation.- A Formal Model for Representing Point Trajectories in Two-Dimensional Spaces.- A Logical Approach for Modeling Spatio-temporal Objects and Events.- Conceptual Neighbourhood Diagrams for Representing Moving Objects.- Spatial Relations.- A Refined Line-Line Spatial Relationship Model for Spatial Conflict Detection.- Assessing Topological Consistency for Collapse Operation in Generalization of Spatial Databases.- Spatial Relations for Semantic Similarity Measurement.- Spatial Queries, Analysis and Data Mining.- Approximate Continuous K Nearest Neighbor Queries for Continuous Moving Objects with Pre-defined Paths.- Spatio-temporal Similarity Analysis Between Trajectories on Road Networks.- Using Data Mining for Modeling Personalized Maps.- Data Modeling and Visualisation.- 3D Scene Modeling for Activity Detection.- SAMATS - Edge Highlighting and Intersection Rating Explained.- Applying Semantic Web Technologies for Geodata Integration and Visualization.- Sixth International Workshop on Conceptual Modeling Approaches for e-Business (eCOMO 2005).- Preface to eCOMO 2005.- Bargaining in E-Business Systems.- Conceptual Content Management for Enterprise Web Services.- Verifying Web Services Composition.- Towards Amplifying Business Process Reuse.- First International Workshop on Quality of Information Systems (QoIS 2005).- Preface to QoIS 2005.- Information System Models Quality.- Measuring the Perceived Semantic Quality of Information Models.- Situated Support for Choice of Representation for a Semantic Web Application.- Towards Systematic Model Assessment.- A Fuzzy Based Approach to Measure Completeness of an Entity-Relationship Model.- Quality Driven Processes.- Managing Information Quality in e-Science: A Case Study in Proteomics.- Tool Support and Specification Quality: Experimental Validation of an RE-Tool Evaluation Framework.- Improving Object-Oriented Micro Architectural Design Through Knowledge Systematization.- Tutorials.- Tutorial 1: eduWeaver - The Courseware Modeling Tool.- Tutorial 2: FOOM - Functional and Object Oriented Methodology: An Integrated Approach.- Tutorial 3: Domain Engineering - Using Domain Concepts to Guide Software Design.- Tutorial 4: Reasoning About Web Information Systems.- Tutorial 5: Schema and Data Translation.- Tutorial 6: Modeling and Simulation of Dynamic Engineering Design Processes.- Tutorial 7: Modeling Enterprise Applications.