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<Paper uid="W06-0508">
  <Title>Sydney, July 2006. c(c)2006 Association for Computational Linguistics A hybrid approach for extracting semantic relations from texts</Title>
  <Section position="7" start_page="62" end_page="63" type="concl">
    <SectionTitle>
4 Conclusions and future work
</SectionTitle>
    <Paragraph position="0"> We presented a hybrid approach for the extraction of semantic relations from text. It was de-KMi awarded PS4M for Semantic Web Research Professor Enrico Motta and Dr John Domingue of the Knowledge Media Institute have received a set of recordbreaking awards totalling PS4m from the European Commission's Framework 6 Information Society Technologies (IST) programme. This is the largest ever combined award obtained by KMi associated with a single funding programme.</Paragraph>
    <Paragraph position="1"> The awards include three Integrated Projects (IPs) and three Specific Targeted Research Projects (STREPs) and they consolidate KMi's position as one of the leading international research centers in semantic technologies. Specifically Professor Motta has been awarded: a.. PS1.55M for the project NeOn: Lifecycle Support for Networked Ontologies b.. PS565K for XMEDIA: Knowledge Sharing and Reuse across Media and c.. PS391K for OK: Openknowledge - Open, coordinated knowledge sharing architecture. ...</Paragraph>
    <Paragraph position="2">  signed mainly to enrich the annotations produced by a semantic web portal, but can be used for other domains and applications, such as ontology population and development. Currently we are concluding the integration of the several modules composing our architecture. We will then carry experiments with our corpus of newsletters in order to evaluate the approach. Subsequently, we will incorporate the architecture to a semantic web portal and accomplish an extrinsic evaluation in the context of that application. Since the approach uses deep linguistic processing and corpus-based strategies not requiring any manual annotation, we expect it will accurately discover most of the relevant relations in the text.</Paragraph>
  </Section>
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