=Paper=
{{Paper
|id=Vol-1388/poster_paper13
|storemode=property
|title=Introducing Distiller: a Lightweight Framework for Knowledge Extraction and Filtering
|pdfUrl=https://ceur-ws.org/Vol-1388/poster_paper13.pdf
|volume=Vol-1388
|dblpUrl=https://dblp.org/rec/conf/um/NartDT15
}}
==Introducing Distiller: a Lightweight Framework for Knowledge Extraction and Filtering==
https://ceur-ws.org/Vol-1388/poster_paper13.pdf
Introducing Distiller: a Lightweight Framework
for Knowledge Extraction and Filtering
Dario De Nart, Dante Degl'Innocenti, Carlo Tasso
Arti cial Intelligence Lab
Department of Mathematics and Computer Science
University of Udine, Italy
fdario.denart,carlo.tassog@uniud.it, dante.deglinnocenti@spes.uniud.it
Abstract. Semantic content analysis is an activity that can greatly sup-
port a broad range of user modelling applications. Several automatic
tools are available, however such systems usually provide little tuning
possibilities and do not support integration with di erent systems. Per-
sonalization applications, on the other hand, are becoming increasingly
multi-lingual and cross-domain. In this paper we present a novel frame-
work for Knowledge Extraction, whose main goal is to support the de-
velopment of new strategies and technologies and to ease the integration
of the existing ones.
1 Introduction
Adaptive Personalization systems can greatly bene t from automatic Knowledge
Extraction (herein KE) from natural language text: for instance machine read-
able data about the textual content of visited Web pages or of user-generated
content can enhance user pro ling activities. Due to the current size of the Web,
however, one cannot expect human experts to annotate such data manually. Sev-
eral tools have been developed over the past years to address this issue. However
we can identify three critical issues in state-of-the-art Knowledge Extraction
systems:
{ Multilinguality : roughly half of the available Web pages include non-English
text 1 . The large majority of Web users are non-English native speakers, and
multilingual personalization is a hot research topic, however KE tools show
a general lack of multilingual non-English support.
{ Knowledge Source Completeness : KE systems mostly rely on a speci c knowl-
edge source (such as DBpedia) acting in a closed-world fashion and assuming
that such knowledge source is complete. This assumption is in contrast with
the open-world assumption of semantic Web technologies and shows o its
limitations when applied to texts such as scienti c papers, where new con-
cepts are often introduced. Therefore a more exible approach open to more
than one external knowledge source seems more appropriate.
1
http://w3techs.com/technologies/overview/content language/all
{ Knowledge Overload : long texts, such as scienti c papers, may include a lot
of named entities, but not all are equally relevant inside the text. State-of-
the-art KE systems currently provide Named Entity Recognition, but do not
lter relevant entities nor include relevance measures.
In this paper we introduce Distiller, a KE framework whose aim is to overcome
these limitations and allow integration of heterogeneous KE technologies.
2 Related Work
Named entity recognition and automatic semantic data generation from natu-
ral language text has already been investigated and several knowledge extraction
systems already exist [4], such as OpenCalais 2 , Apache Stanbol3 , and TagMe [3].
In [8] an ensemble learning strategy to raise the accuracy of the named entity
identi cation process is presented. Several authors in the literature have ad-
dressed the problem of ltering document information by identifying keyphrases
(herein KPs) and a wide range of approaches have been proposed. The authors
of [10] identify four types of KP extraction strategies:
{ Simple Statistical Approaches : mostly unsupervised techniques, considering
word frequency, TF-IDF or word co-occurency [7].
{ Linguistic Approaches : techniques relying on linguistic knowledge to iden-
tify KPs. Proposed methods include lexical analysis, syntactic analysis, and
discourse analysis [5].
{ Machine Learning Approaches : techniques based on machine learning algo-
rithms such as Naive Bayes classi ers and SVM. Systems such as KEA [9]
belong to this category.
{ Other Approaches : other strategies exist which do not t into one of the
above categories, mostly hybrid approaches combining two or more of the
above techniques [1]. Among others, heuristic approaches based on knowledge-
based criteria [6] have been proposed.
3 System Overview
In order to overcome the shortcomings of state-of-the-art KE systems we ex-
tended the approach presented in [2] and formalized it as a framework named
Distiller whose main aim is to support research and prototyping activities by
providing an environment for building testbed systems and integrating existing
systems. The guiding principle of the framework design is that several di er-
ent types of knowledge are involved in the process of KE and should be clearly
separated to design systems able to cope with multilinguality and multi-domain
issues. We consider four main types of knowledge: Statistical, Linguistic, Exter-
nal (i.e. coming from outside the text, like the one extracted from ontologies),
2
http://www.opencalais.com/
3
https://stanbol.apache.org/
and Heuristic knowledge. Linguistic knowledge is language dependant, Heuristic
knowledge is domain dependent, and External knowledge is both domain and
language dependant. At a more practical level, this principle implies that di er-
ent types of knowledge must reside in distinct modules, for instance, statistical
and linguistic analysis must be handled by di erent modules.
Distiller is organized in a series of single-knowledge oriented modules and
its work ow is organized in four phases: Concept Unit Splitting, Annotation,
Candidate Generation, and Filtering, as shown in Figure 1. In the rst phase the
text is split into Concept Units, i.e. logical blocks such as chapters, paragraphs
or sentences. The framework allows the co-existence of concept units of di erent
languages inside a document. The Annotation phase consists in enriching the
text with information such as POS tagging, stems, lemmas, or links to entities
from external knowledge sources (such as DBpedia). This phase introduces new
knowledge in the text, and several di erent annotators can contribute, enriching
the text with di erent kinds of knowledge, but mostly with External knowledge
that may come from heterogeneous sources. Existing KE tools, such as TagMe,
can be integrated in the framework as annotators. The Candidate Generation
phase identi es in the text all the candidate entities and/or concepts of interests
exploiting the annotations provided in the previous step and internally represents
them as KPs with an attached set of annotations. Finally, the Filtering phase
evaluates a relevance score for each candidate concept depending on which it is
returned as output or hidden. The Filtering phase, like the Candidate Generation
one, may exploit di erent types of knowledge embedded in annotations, and
combine them according to the needs of the applications that will eventually use
the extracted knowledge.
Fig. 1: Framework work ow.
Distiller is implemented in Java using the Dependency Injection pattern,
that allows users to easily switch between di erent modules and con gurations.
Default implementations for all the above described modules are provided with
the framework4 .
4
A sample application built with the default modules is showcased at
ailab.uniud.it:8080/distiller
4 Conclusions
With respect to the three issues of KE presented in Section 1, Distiller allows the
development of applications able to overcome such shortcomings. The issue of
multilinguality is eased by the possibility of specifying a wide array of annotators
and to dynamically link them at runtime depending on the text language. The
issue of Knowledge Source Completeness is eased by the possibility of integrat-
ing heterogeneous knowledge sources as di erent annotators and implementing
annotators who generate URIs on the y. The issue of Knowledge Overload, -
nally, is eased by the presence of a ltering phase in which entities are evaluated
with respect to their relevance in the text.
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http://eprints.rclis.org/handle/10760/12305