The PIA Project: Learning to Semantically Annotate Texts
from an Ontology and XML-Instance Data
Nigel Collier

Koichi Takeuchi

Keita Tsuji

National Institute ofInformatics (NII)
National Center of Sciences,2-1-2 Hitotsubashi
Chiyoda-ku, Tokyo 101-8430,
Japan

National Institute ofInformatics (NII)
National Center of Sciences,2-1-2 Hitotsubashi
Chiyoda-ku, Tokyo 101-8430,
Japan

National Institute ofInformatics (NII)
National Center of Sciences,2-1-2 Hitotsubashi
Chiyoda-ku, Tokyo 101-8430,
Japan

collier@nii.ac.jp

koichi@nii.ac.jp

keita@nii.ac.jp

ABSTRACTThe development of the XML and RDF(S) standards offer
a positive environment for machine learning to enable theautomatic XML-annotation of texts that can encourage theextension of Semantic Web applications. After reviewing
the current limitations of information extraction technology,
specifically its lack of portability to new domains, we introduce the PIA project for automatically XML-annotating
domain-based texts using example XML texts and an ontology for supervised training.

1.

INTRODUCTIONPIA aims to develop a domain and language portable information extraction (IE) system. In contrast to other Webbased technologies such as information retrieval (IR) which
are characterized by strong portability, no such system as
yet exists for IE. We consider that the main factors whichhave prevented this are: (1) A focus within the IE community on general news-based IE, exemplified by systems
that resulted from the message understanding conferences(MUCs) [6], and, (2) Despite recent moves towards machinelearning for low level IE tasks such as named entity recognition there is still a strong reliance on large lexical resources
such as term lists, and an emphasis on hand-built rules andpatterns. The problem we see with this direction is thatit promotes the development of rather inflexible IE systems
that cannot easily be ported to new domains without substantial efforts to customize the system with domain-specificknowledge resources, e.g. the collection of domain dictionaries, writing domain-specific rules etc. Perhaps the greatest
problem is that since there is no prior understanding between the IE system developer and the domain knowledge
provider about the encoding of the knowledge that will beused to train the IE system, there is no guarantee that thetype of knowledge that the system needs will be available in
the new domain.
While the MUCs have made great advances in promoting
the formalisation of IE tasks and evaluation, the MUC-styleof IE technology provides a relatively sterile semantic environment. Semantics is limited to the border between syntax and semantics that occurs at the word and term level
but largely ignores higher-level relations between the classes
themselves and class-relations are ‘forced’ into disjoint relations as far as possible. The markup of text, while conforming to SGML, makes no use of an explicit ontology andrelatively little use of meta-data.
Recent IE projects have looked beyond news to the molecular biology domain, e.g. [4] [5]. Some projects have implicitly incorporated simple taxonomies (is-a hierarchy) into
the annotation guidelines for domain experts. To the bestof our knowledge these projects still largely ignore explicit
properties of classes and class relations that could be contained in the ontology and their potential contribution toautomatic annotation. There seems to be great potential inthis technical domain for incorporating ontologies into thelearning model since a large amount of research has takenplace on their development for gene-product databases suchas SwissProt [1].
We believe then that with the advent of standards for theannotation of semantic content such as XML [3] for document structure, RDF [7] for defining objects and their relations, and RDFS [8] for defining the object model for describing RDF, that sources of domain knowledge will become
widely available in electronic form and that these resources
can and should be used for supervised training of a portable
IE system which we call PIA-Core. Crucially these sourcesof knowledge will be available in a predictable format allowing PIA-Core to be rapidly deployed in a new domain.

2.
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Semantic Web Workshop (SWWS) 2001 Stanford University, USA
Copyright by the authors.

PIA-CORE

We consider the W3C standardization process of XMLand RDF(S) to offer a positive environment for machinelearning of expert knowledge. Although ontologies in RDF
are likely to emerge primarily as a result of (human) expert introspection we cannot expect that XML-instances ofthe defined concepts, such as technical terms, proper nouns,
quantity and time expressions and their relations, will beannotated by experts for every document due to the highcost. This is one of the bottlenecks in the extension of Semantic Web [2] applications to the majority of documentsthat can be viewed on the Web and Intranets today. Whatis missing in the current focus on formalisation is a consideration about how the actual instantiation of the concepts
defined in the ontologies will take place.
Actual semantic annotation of terminology and relations
involves considerable time by domain experts and for thisreason we believe it is worth investing in machine learning
as a way to reliably replicate the capabilities of experts. Thisis the goal of PIA-Core. The scenario is that experts willdevelop a domain model in RDFS and a relatively small setof example annotated texts using an integrated XML andontology editor. From this knowledge PIA will learn how
to automatically XML-annotate unseen texts in the same
domain. By focussing on domain-based learning we hope tomake use of the ontology as a valuable knowledge resource
and also to reduce the problems of ambiguity that developersof general IE systems must face.
In combination with robust domain-independent natural
language processing (NLP) modules such as part of speechtaggers, chunkers and shallow parsers, as well as generallinguistic resources such as thesauri, PIA-Core will be used
to XML-annotate texts that are consistent with those in
the training set. We hope that PIA-Core can provide rapid
acquisition of domain-knowledge and provide functionalitythat can be used at the heart of an IE system or within XMLtagging tools for computer-aided annotation. This can thenserve as the basis for the deployment of ‘smart’ applications
providing intelligent services that we hope to see emerge
on the 2nd generation World Wide Web (Web), i.e. the‘Semantic Web’. The application we want to apply PIACore to is domain-based question-answering (e.g. [9]) in
English and Japanese.

3.

DISCUSSION
Some of the key questions that we need to consider are:
• How should we integrate the ontology into a statisticallybased machine learning (ML) model? For example,
how should we make use of concepts that appear in theontology but don’t appear in the training set? How canstatistical evidence from sub-classes be shared throughontological relations to help overcome data-sparsenessproblems?
• XML is in some respects quite limited in its ability torepresent complex object structure and relations as it
is designed to encode serialization. We need to explore
the limits of this representation for practical annotation of terminology and relations.
• Ontologies change over time - they may be revised,expanded or incorporated into other (shared) ontologies. How do we update the knowledge base that wasextracted from the training set based on the original
ontology? Should the model be retrained every timea change is made to the ontology? How can changes
in the ontology be reflected automatically at the text
markup level? E.g. the introduction of a new subclass.
• How will the issue of multi-linguality affect the designof ontologies in RDF for PIA?

4.

CONCLUSION

We briefly presented a critical analysis of the current status of IE research and proposed a new project called PIA
based on domain-based learning through XML-annotatedtexts and domain models described in RDF. We also considered some of the key research issues. From now we intend
to implement PIA-Core and apply it to the task of question
answering in technical domains such as molecular-biology.

5.

ADDITIONAL AUTHORS

Gareth Jones (Department of Computer Science, University of Exeter, UK, email: gareth@dcs.exeter.ac.uk),
Jun’ichi Fukumoto (Department of Computer Science, Ritsumeikan University, Japan, email: fukumoto@cs.ritsumei.ac.jp), Norihiro Ogata (Faculty of Language and Culture, Osaka University, Japan, email: ogata@lang.osaka-u.ac.jp), Chikashi Nobata (CRL, Japan, email: nova@crl.go.jp), Kinji Ono (National Institute of Informatics, Japan,email: ono@nii.ac.jp).

6.

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