A Conceptual-Model-Based Computational Alembic for a Web of Knowledge

Lecture Notes in Computer Science

Zitzelberger

2010

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Abstract. The current web is a web of linked pages. Frustrated users search for facts by guessing which keywords or keyword phrases might lead them to pages where they can find facts. Can we make it possible for users to search directly for facts embedded in web pages? Instead of a web of human-readable pages containing machine-inaccessible facts, can the web be a web of machine-accessible facts superimposed over a web of human-readable pages? Ultimately, can the web be a web of knowledge that can provide direct answers to factual questions and support these answers by referencing and highlighting relevant base facts embedded in source pages? Answers to these questions call for distilling knowledge from the web's wealth of heterogeneous digital data into a web of knowledge. But how? Or, even more fundamentally, what, precisely, is this web of knowledge, and what is required to enable it? To answer these questions, we proffer a theoretical foundation for a web of knowledge: We formally define a computational view of knowledge in a way that enables practical construction and use of a web of knowledge.

Section: Resultsmentioning

confidence: 99%

Theoretical Foundations for Enabling a Web of Knowledge

Lecture Notes in Computer Science

Zitzelberger

2010

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“…We envision it as part of a much larger system that automatically, or at least semi-automatically, generates interesting semantic-web content from data-rich web pages [2]. For tables, the larger system (1) generates a conceptual-model instance and represents it as an OWL ontology, (2) converts all recognized instance values into RDF instance data for the OWL ontology, (3) allows the data to be queried with SPARQL or a free-form-query engine, and (4) displays query results both in tabular form and as highlighted data values in the original HTML table processed by the system.…”

Section: Discussion Points and Future Workmentioning

confidence: 99%

Semantically Conceptualizing and Annotating Tables

Lynn

The Semantic Web

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Abstract. Enabling a system to automatically conceptualize and annotate a human-readable table is one way to create interesting semanticweb content. But exactly "how?" is not clear. With conceptualization and annotation in mind, we investigate a semantic-enrichment procedure as a way to turn syntactically observed table layout into semantically coherent ontological concepts, relationships, and constraints. Our semanticenrichment procedure shows how to make use of auxiliary world knowledge to construct rich ontological structures and to populate these ontological structures with instance data. The system uses auxiliary knowledge (1) to recognize concepts and which data values belong to which concepts, (2) to discover relationships among concepts and which datavalue combinations represent relationship instances, and (3) to discover constraints over the concepts and relationships that the data values and data-value combinations should satisfy. Experimental evaluations indicate that the automatic conceptualization and annotation processes perform well, yielding F-measures of 90% for concept recognition, 77% for relationship discovery, and 90% for constraint discovery in web tables selected from the geopolitical domain.

Conceptual Modeling Foundations for a Web of Knowledge

Handbook of Conceptual Modeling

Liddle

Lonsdale

2011

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The semantic web purports to be a web of knowledge that can answer our questions, help us reason about everyday problems as well as scientific endeavors, and service many of our wants and needs. Researchers and others expound various views about exactly what this means. Here we propose an answer with conceptual modeling as its foundation. We define a web of knowledge as a collection of interconnected knowledge bundles superimposed over a web of documents. Knowledge bundles are conceptual model instances augmented with facilities that provide for both extensional and intensional facts, for linking between knowledge bundles yielding a web of data, and for linking to an underlying document collection providing a means of authentication. We formally define both the component parts of these augmented conceptual models and their synergistic interconnections. As for practicalities, we discuss problems regarding the potentially high cost of constructing a web of knowledge and explain how they may be mitigated. We also discuss usage issues and show how untrained users can interact with and gain benefit from a web of knowledge.