=Paper=
{{Paper
|id=Vol-2536/om2019_poster4
|storemode=property
|title=Semantic Table Interpretation Using MantisTable
|pdfUrl=https://ceur-ws.org/Vol-2536/om2019_poster4.pdf
|volume=Vol-2536
|authors=Marco Cremaschi,Anisa Rula,Alessandra Siano,Flavio De Paoli
|dblpUrl=https://dblp.org/rec/conf/semweb/CremaschiRSP19
}}
==Semantic Table Interpretation Using MantisTable==
https://ceur-ws.org/Vol-2536/om2019_poster4.pdf
Semantic Table Interpretation using MantisTable
Marco Cremaschi1 , Anisa Rula1,2 , Alessandra Siano1 , and Flavio De Paoli1
1
University of Milano - Bicocca, Italy
{marco.cremaschi,anisa.rula,flavio.depaoli}@unimib.it
a.siano2@campus.unimib.it
2
Univeristy of Bonn, Germany
{rula}@cs.uni-bonn.de
Keywords: Knowledge Graph · Semantic Interpretation · Table Annotation
1 Introduction & Motivation
A vast amount of relevant structured data represented in tables are available on
the Web. However, querying such data is difficult since they are incorporated
in HTML web pages and are not easily query-able. Some approaches started
to propose [4, 2] extraction, annotation and transformation of tabular data into
machine-readable formats. The problem of annotating tables also known as Se-
mantic Table Interpretation (STI) takes a relational table and a Knowledge
Graph (KG) in input, and returns a semantically annotated table in output [1].
In this paper, we propose MantisTable 3 , a web interface and an open source
Semantic Table Interpretation tool that automatically annotates, manages and
makes accessible to humans and machines the semantic of tables. Although STI
contains several steps the key feature of our tool is the involvement of all the
STI steps that run fully automatically.
2 Overview of MantisTable
Figure 1 shows the architecture of MantisTable which is designed to be modular:
View Layer provides a graphic user interface to serve different types of tasks
such as storing and loading tables, exploration of the annotated tables which
allow users to navigate all the executed steps by clicking on each phase and
analyse the result, execution of the STI steps and the editing which allow users
to understand what has been achieved and give them the opportunity to modify
and enhance the results.
Controller Layer creates all the abstraction between the View layer and the
Model layer and implements all the STI steps as follows:
Data Preparation cleans and normalizes values in the table. Transforma-
tions applied to tables include text normalization such as solve acronyms and ab-
breviations by applying regular expressions [3]. Column Analysis assigns types
3
http://mantistable.disco.unimib.it
Copyright 2019 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0).
2 Cremaschi M. et al.
Fig. 1. Architecture of MantisTable tool.
to columns that are named entity (NE-column) or literal column (L-column),
and then identify the subject column (S-column). To identify L-column candi-
dates the tool considers 16 regular expressions that identify several Regextypes.
If the number of occurrences of the most frequent Regextype in a column ex-
ceeds a given threshold, that column is annotated as L-column, otherwise, it is
annotated as NE-column. To detect the S-column, the tool considers the NE-
columns on which applies different statistic features (e.g. % of cells with unique
content). Concept and Datatype Annotation identifies the mappings be-
tween columns headers and semantic elements (concepts or datatypes) in a KG.
First, we perform the entity-linking by searching the KG with the content of
a cell, to get a set of candidate entities and use the DICE similarity measure
for text disambiguation. Second, the abstract and all concepts for each winning
entity are retrieved from DBpedia. For each extracted concept, we count the
occurrences in the abstract. For the Datatype Annotation we consider the L-
columns and for the identification of datatypes, a Regextype is applied on the
content of each column. Predicate Annotation finds relations, in the form
of predicates, between the S-column and the object columns to set the overall
meaning of the table. Further, the entities identified as subjects and objects are
searched in the KG to identify the correct predicate. Entity Linking deals with
mappings between the content of cells and entities in the KG.
Model Layer considers mainly data access for communicating with an appli-
cation’s data sources such as DB connector or DBpedia connector.
References
1. Cremaschi, M., Rula, A., Siano, A., De Paoli, F.: MantisTable: a Tool for Creating
Semantic Annotations on Tabular Data. In: 16th ESWC: Posters and Demos (2019)
2. Pham, M., Alse, S., Knoblock, C.A., Szekely, P.: Semantic labeling: a domain-
independent approach. In: 15th ISWC (2016)
3. Ritze, D., Lehmberg, O., Bizer, C.: Matching HTML Tables to DBpedia. In: WIMS
(2015)
4. Zhang, Z.: Effective and efficient semantic table interpretation using tableminer+.
Semantic Web (2017)