Logical foundations of object-oriented and frame-based languages
Journal of the ACM (JACM)
Conceptual-model-based data extraction from multiple-record Web pages
Data & Knowledge Engineering
A survey of table recognition: Models, observations, transformations, and inferences
International Journal on Document Analysis and Recognition
Towards Ontology Generation from Tables
World Wide Web
Transforming arbitrary tables into logical form with TARTAR
Data & Knowledge Engineering
A Conceptual-Model-Based Computational Alembic for a Web of Knowledge
ER '08 Proceedings of the 27th International Conference on Conceptual Modeling
Semantic annotation of data tables using a domain ontology
DS'07 Proceedings of the 10th international conference on Discovery science
An open and scalable framework for enriching ontologies with natural language content
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Converting and annotating quantitative data tables
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
An ontological and terminological resource for n-ary relation annotation in web data tables
OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems - Volume Part II
Theoretical foundations for enabling a web of knowledge
FoIKS'10 Proceedings of the 6th international conference on Foundations of Information and Knowledge Systems
Addressing the long tail in empirical research data management
Proceedings of the 12th International Conference on Knowledge Management and Knowledge Technologies
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Enabling a system to automatically conceptualize and annotate a human-readable table is one way to create interesting semantic-web 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 semantic-enrichment 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 data-value 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.