A Domain Independent Framework for Extracting Linked Semantic Data from Tables

Mulwad, Varish; Finin, Tim; Joshi, Anupam

doi:10.1007/978-3-642-34213-4_2

Cited by 28 publications

(13 citation statements)

References 19 publications

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“…It uses a fix spreadsheet structure, into which ontology information is entered and a fixed import mechanism creates the OWL ontology from the spreadsheet. Another approach to ontology extraction, developed by Mulwad et al [5], uses linked data and probabilistic reasoning to infer a table structure and thus infer a new ontology definition or extension to an existing definition. The approach, however, fixes an interpretation that spreadsheet columns denote classes, data cell values denote literals and properties are inferred relations between columns.…”

Section: Related Workmentioning

confidence: 99%

Declarative Data Exchange

Compton¹,

Ratcliffe

Shu

et al. 2014

Proceedings of the 19th International Database Engineering &Amp; Applications Symposium on - IDEAS '15

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Integrating and accessing data from the many existing formats is a key challenge for the Semantic Web. In particular, representing and understanding non-RDF/OWL data in terms of ontologies is important for reasoning about this data and its relationships to other data, and for extracting or accessing the data itself. This paper presents a declarative method for mapping and translating spreadsheet data to RDF/OWL. First, a declarative mapping language is presented. Then, for this syntax, the paper presents a declarative semantics in terms of matrix operations as abstractions over spreadsheets and the relations therein. The language supports arbitrary search and indexing functions over spreadsheet data and allows the generation of complex associations and aggregations in the RDF/OWL output. Imperative and functional implementations of the mapping and extraction technology are presented.

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Section: Related Workmentioning

confidence: 99%

Declarative Data Exchange

Compton¹,

Ratcliffe

Shu

et al. 2014

Proceedings of the 19th International Database Engineering &Amp; Applications Symposium on - IDEAS '15

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“…Mulwad, Finin, and Joshi (2012)) model tables as a graph, where table cells are nodes of the graphs and the edges linking them are initially weighted by the strength of the semantic relationships between cells as retrieved from KBs or Linked Open Data (Bizer, 2009). This model can be exploited both for the functional/structural analysis and for the interpretation of the table content in terms of a KB.…”

Section: Approaches For the Table Understanding Problemmentioning

confidence: 99%

Table understanding approaches for extracting knowledge from heterogeneous tables

Bonfitto

Casiraghi

Mesiti

2021

WIREs Data Min & Knowl

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Table understanding methods extract, transform, and interpret the information contained in tabular data embedded in documents/files of different formats. Such automatic understanding would allow to exploit tabular information with the aim of accurately answering queries, or integrating heterogeneous repositories of information in a common knowledge base, or exchanging information among different sources. The purpose of this survey is to provide a comprehensive analysis of the research efforts so far devoted to the problem of table understanding and to describe systems that support the transformation of heterogeneous tables into meaningful information. This article is categorized under: Application Areas > Data Mining Software Tools Technologies > Data Preprocessing Technologies > Structure Discovery and Clustering

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“…Our approach builds on the concepts introduced in the TABEL framework [11], [12], [13] for inferring the semantics of information encoded in tables and representing it as RDF Linked Data. Figure 2 presents an overview of our system architecture.…”

Section: Approachmentioning

confidence: 99%

Semantic Interpretation of Structured Log Files

Nimbalkar¹,

Mulwad²,

Puranik³

et al. 2016

2016 IEEE 17th International Conference on Information Reuse and Integration (IRI)

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Data from computer log files record traces of events involving user activity, applications, system software and network traffic. Logs are usually intended for diagnostic and debugging purposes, but their data can be extremely useful in system audits and forensic investigations. Logs created by intrusion detection systems, web servers, anti-virus and anti-malware systems, firewalls and network devices have information that can reconstruct the activities of malware or a malicious agent, help plan for remediation and prevent attacks by revealing probes or intrusions before damage has been done. While existing tools like Splunk can help analyze logs with known schemas, understanding log whose format is unfamiliar or associated with new device or custom application can be challenging. We describe a framework for analyzing logs and automatically generating a semantic description of their schema and content in RDF. The framework begins by normalizing the log into columns and rows using regular expression-based and dictionary-based classifiers. Leveraging our existing work on inferring the semantics of tables, we associate semantic types with columns and, when possible, map them to concepts in general knowledge-bases (e.g. DBpedia) and domain specific ones (e.g., Unified Cybersecurity Ontology). We link cell values to known type instances (e.g., an IP address) and suggest relationships between columns. Converting large and verbose log files into such semantic representations reveals their meaning and supports search, integration and reasoning over the data.

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A Domain Independent Framework for Extracting Linked Semantic Data from Tables

Cited by 28 publications

References 19 publications

Declarative Data Exchange

Declarative Data Exchange

Table understanding approaches for extracting knowledge from heterogeneous tables

Semantic Interpretation of Structured Log Files

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