=Paper=
{{Paper
|id=Vol-100/paper-15
|storemode=property
|title=Recycling an Information Extraction System to Automatically Produce Semantic Annotations for the Web
|pdfUrl=https://ceur-ws.org/Vol-100/Thierry_Poibeau-et-al.pdf
|volume=Vol-100
|dblpUrl=https://dblp.org/rec/conf/ecai/PoibeauAG02
}}
==Recycling an Information Extraction System to Automatically Produce Semantic Annotations for the Web==
https://ceur-ws.org/Vol-100/Thierry_Poibeau-et-al.pdf
Recycling an Information Extraction system to automatically produce
Semantic Annotations for the Web
Thierry Poibeau Alexandre Arcouteil Cyril Grouin
Thales et LIPN INALCO INALCO
Domaine de Corbeville 2, rue de Lille 2, rue de Lille
F-91 404 Orsay, France F-75007 Paris, France, F-75007 Paris, France,
thierry.poibeau@thalesgroup.com alexandre.arcouteil@inalco.fr cgrouin@msh-paris.fr
Abstract
This paper is intended to show how an Information extraction system can be recycled to produce
RDF schemas for the semantic web. We show that this kind of systems has to respect operational
constraints like the fact that the information produced must be highly relevant (high precision,
possibly low recall). We conclude in reconsidering some tasks like Question Answering (Q/A): the
production of explicit structured data on the web will lead a better relevance of information retrieval
engines.
1 Introduction 2 Related work
Information Extraction (IE) is a technology The bases of IE as defined in the introduction
dedicated to the extraction of structured are exposed in (Pazienza, 1997). IE is known
information from texts. This technique is used to have established a now widely accepted
to highlight relevant sequences in the original linguistic architecture based on cascading
text or to fill pre-defined templates (Pazienza automata and domain-specific knowledge
1997). (Appelt et al, 1993). Several papers mentioned
With the development of the semantic web, current limitations of MUC-like systems in
such tools appear to be very interesting to terms of adaptability and studied the resource
automatically extract semantic information development cost (Grishman & Sundheim,
from existing web pages. In this paper, we will 1996). Event’99 was a task intended to
not focus on the analysis of semi-structured evaluate event-level indexing into news stories
documents by means of wrappers. Even if (Hirschman et al., 1999). The idea is “to
HTML is a semi-structured format, most of the minimize the number of event-specific rules”
information available on the web is located to be produced and to favor a light generic
inside unstructured and untagged paragraphs. template, informally called a “templette”
This paper is intended to show how an (Event 99). This paper is based upon the same
Information extraction system can be recycled idea applied to the AFP newswire.
to produce RDF schemas for the semantic web. Different systems tried to extract
We will see that this kind of systems has to information in analyzing the structure of
respect operational constraints like the fact that different kind of texts. For example, (Lacroix
the information produced must be highly et al. 98) presents a system able to extract
relevant (high precision, possibly low recall). information from the structure of HTML
We conclude in reconsidering some tasks like pages. This kind of application will increase
Question Answering (Q/A): the production of with the development of more structured
explicit structured data on the web will lead a document (like XML documents). Wrapper
better relevance of information retrieval factories go one step beyond, in connecting
engines. together distant pieces of texts and in
extracting information from poorly structured
documents (Sahuguet and Azavant 98).
To address the problem of portability, a state toolbox Intex to design dictionaries
recent research effort focused on using and automata (Silberztein 1993).
machine learning throughout the IE process
(Muslea, 1999). A first trend was to directly
apply machine learning methods to replace IE
components. For example, statistical methods
have been successfully applied to the named-
entity task. Among others, (Bikel et al., 1997)
learns names using a variant of hidden Markov
models. However, a 90% success rate is
reached at the cost of tagging manually about
half a million words. (Cucchiarelli & Velardi,
1999) propose a more interesting approach:
they adopt a hybrid approach mixing a core
generic system extended with some learning Figure 2: The named entity recognizer
mechanisms. Their system is able to learn new
proper names by generalizing the data
extracted by the basic rule-based system. We
• The third stage performs text
will adopt a very similar approach, except the categorization from “semantic
fact that we want to stay in a symbolic signatures” automatically produced from
framework, mainly for readability reasons. a rough semantic analysis of the text. We
use an external industrial system
3 Inf ormation extraction system implementing a vector space model to
The architecture consists in a multi-agent categorize texts (the Intuition™ system
platform. Each agent performs a precise from the French company Sinequa, cf.
subtask of the information extraction Salton (1988)).
process. A supervisor controls the overall • The fourth stage extracts specific
process and the information flow. The information (most of time, specific
overall architecture is presented in (Poibeau, relationships between named entities). It
2001). can be for example the number of
The system can be divided into five parts: victims of a terrorist event. This step is
information extraction from the structure of achieved in applying a grammar of
the text, the module for named entity transducers (extraction patterns) over the
recognition (location, dates, etc), semantic text.
filters, modules for the extraction of specific
domain-dependent information and modules • The next stage links all these information
for the filling of a result template. together to produce one or several result
• Some information is extracted from the template(s) that present(s) a synthetic view
structure of the text. Given that the AFP of the information extracted from the text.
newswire is formatted, some wrappers The template corresponding to the text is
automatically extract information about chosen among the set of all templates,
the location and the date of the event. according to the identified category of the
This non-linguistic extraction increases
the quality of the result by providing text (registered by the system at the third
100% good results. It is also accurate analysis step). A specific template is
when one thinks of the current produced only if some main slots are filled
development of structured text (HTML, (the system distinguished among
XML) via the web and other corporate obligatory and optional slots).
networks.
• The second stage is concerned with the Partial templates produced by different
recognition of relevant information by sentences are merged to produce only one
means of a linguistic analysis. This stage template per text. This merging is done under
allows the recognition of various named constraints on what can be unified or not. The
entities (person names, organizations,
locations and dates) of the text. New results are then stored in a database, which
kinds of named entities can be defined exhibit knowledge extracted from the corpus.
according to a new domain (for
examples, gene names to analyze a
genome database). We use the finite-
4 Application overview : know ledge 5 Semantic annotations and other
extraction f rom various domains outputs
Various applications have been developed The system currently produces various kinds of
using the above architecture, to ensure its output, for example:
genericity. They concerned different domains:
− XML(/HTML) tagged texts for named
− Event-based extraction and indexing of the entity highlighting in texts.
AFP newswire. This multi-domain
extraction system is currently running in
real time, on the AFP newswire. About 15
templates have been defined that cover
about 30% of the stories. From the
remaining 70%, the system only extract
surface information, especially thanks to
the wrappers. The performances are
between .55 and .85 P&R, if we do not take
into account the date and location slots that
are filled by means of wrappers. New
extraction templates are defined to prove
system scalability.
− Event-based extraction from financial news Figure 3: A news story from FirstInvest annotated
stories (FirstInvest, a French financial with XML tags
website). This application is very close to
the previous one.
− Extraction of gene interactions from the − Event database for the analysis of the AFP
genomics database Flybase. This kind of newswire. A new template is produce for
bases are structured by gene description, each new event.
but researchers want to find relations
among genes. In this context, the IE engine
is intended to automatically produce a
knowledge base about gene interaction,
from the analysis of free texts.
− Customer Request Management application
(extracting information from emails relating
software problems). This last case poses the
problem of email analysis and management.
The language used in such texts is not as
correct as it can be in news stories. Specific
grammar and orthographic relaxations must
be applied to achieve relevant results. Figure 4: Event-base AFP indexing: each text fills a
specific template, given its topic
The last three applications concern texts from
the Internet. FirstInvest is an electronic
financial newswire available on the Web.
− A dynamic knowledge base for gene
Flybase, like other electronic databases in
interaction (a query-able knowledge base
genomics, is a collection of public data freely
made of Prolog-like terms)
available for researchers. The CRM application
concerns a currently very popular area, which
activation(1.28,Dfd)
is also related to Knowledge Management. activation(5-HT1A,C)
activation(ac,E)
activation(Ac13E,G)
activation(Dfd,1.28)
interaction(2R-F,mys)
interaction(2R-L,mys)
Figure 5: A part of the knowledge base generated
from the analysis of Flybase
The range of performance is generally located
possible to semi-automatically adapt the
system so that precision is very high. This
point is crucial to produce high quality data for
lower recall than classical IE tools (recall
we often see a precision above .95 with a recall
of .15, which is not a problem as such since
genomics databases are highly redundant. Of
course, an effort is made to produce data with
6 Information Extraction and RDF
Expressing structured information using the
RDF syntax should provide interoperability
two kinds of information should be produced
− a structure analysis inferring a RDF schema
statement;
schema.
1.1 Structure analysis
An IE system isolates semantic groups from
which a RDF schema is built. As an example,
in the case of Flybase, the IE system identifies 7 Structured data
interactions between genes, the list of genes According to the RDF schema, the gene and
and the list of interactions. A RDF schema interaction description in Flybase is
defines an object class and the relations that represented ina new RDF file. Let’s take the
could exist between several objects from these example of two genes, abd-A and trx:
classes. In our case, these classes are the class
“gene” and the class “interaction”:
+
Then, the system defines statements about The interaction between the two genes is
existing constraints on the classes and the represented as follows:
properties, according to the syntactic analysis
of the document:
P&R is the harmonic means of recall and precision.
This metric is classical to measure the performance of
filtering and extraction systems.
http://www.itl.nist.gov/div894/894.01/
Lacroix Z., Sahuguet A. and Chandrasekar R.
1998. Information Extraction and Database
Techniques: A User-Oriented Approach to
Querying the Web. Conference on Advanced
8 Conclusion Information Systems Engineering, 1998.
In this paper we have shown that a versatile IE
Muslea I. 1999. Extraction patterns for
system is very appropriate to automatically
Information Extraction tasks: a survey,
analyze unstructured texts from the web and
AAAI’99 (available at: http://www.isi.edu/
produce semantic annotations. Some
~muslea/RISE/ML4IE/)
researches still need to be done to produce
more robust IE tools that will be able to deal Pazienza M. T. ed. 1997. Information
with various kind of texts. We have proposed extraction (a multidisciplinary approach to an
some methods (Poibeau, 2001), but large emerging information technology), Springer
experiments still need to be done. In particular, Verlag (Lecture Notes in Computer Science),
it is necessary to mix NLP approach with Heidelberg, Germany.
wrappers to make good use of semi-structured
Poibeau T. 2001. “Deriving a multi-domain
texts.
information extraction system from a rough
These systems should change a bit the face
ontology”. In Proceeding of the 17th
of the Web. Given that more and more
International Conference on Artificial
structured and semantically annotated data will
Intelligence, Seattle, USA. pp. 1264–1270.
be available, Question Answering systems
should give more accurate answers to user Sahuguet A. and Azavant F. (1998) W4F: a
requests, for example. In this sense, IE systems WysiWyg Web Wrapper Factory, Technical
allow to really extract and structure the report from the Penn Database Research
semantic of the Web. Group, University of Pennsylvania.
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