I. Fahmi

Mapping metadata for SWHi: aligning schemas with library metadata for a historical ontology

What are the possibilities of Semantic Web technologies for organizations which traditionally have lots of structured data, such as metadata, available? A library is such a particular organization. We mapped a digital librarys descriptive (bibliographic) metadata for a large historical document collection encoded in MARC21 to a historical ontology using an out-of-the-box ontology, existing topic hierarchies on the World Wide Web and other resources. We also created and explored useful relations for such an ontology. We show that mapping the metadata to an ontology adds information and makes the existing information more easily accessible for users. The paper discusses various issues that arose during the mapping process. The result of mapping metadata to RDF/OWL is a populated ontology, ready to be deployed.

SWHi System Description: A Case Study in Information Retrieval, Inference, and Visualization in the Semantic Web

Search engines have become the most popular tools for finding information on the Internet. A real-world Semantic Web application can benefit from this by combining its features with some features from search engines. In this paper, we describe methods for indexing and searching a populated ontology by using an information retrieval tool; its results are enriched with inference. For visualization purposes, all of the retrieved ontology instances are clustered based on their classes; and the clusters are linked using instance properties. The approach is illustrated using our SWHi (Semantic Web for History) prototype as a case study.

Relation Extraction for Open and Closed Domain Question Answering

One of the most accurate methods in Question Answering (QA) uses off-line information extraction to find answers for frequently asked questions. It requires automatic extraction from text of all relation instances for relations that users frequently ask for. In this chapter, two methods are presented for learning relation instances for relations relevant in a closed and open domain (medical) QA system. Both methods try to learn automatic dependency paths that typically connect two arguments of a given relation. The first (lightly supervised) method starts from a seed list of argument instances, and extracts dependency paths from all sentences in which a seed pair occurs. This method works well for large text collections and for seeds which are easily identified, such as named entities, and is well-suited for open domain QA. A second experiment concentrates on medical relation extraction for the question answering module of the IMIX system. The IMIX corpus is relatively small and relation instances may contain complex noun phrases that do not occur frequently in the exact same form in the corpus. In this case, learning from annotated data is necessary. Dependency patterns enriched with semantic concept labels are shown to give accurate results for relations that are relevant for a medical QA system. Both methods improve the performance of the Dutch QA system Joost.

Using syntactic knowledge for QA

We describe the system of the University of Groningen for the monolingual Dutch and multilingual English to Dutch QA tasks. First, we give a brief outline of the architecture of our QA-system, which makes heavy use of syntactic information. Next, we describe the modules that were improved or developed especially for the CLEF tasks, among others incorporation of syntactic knowledge in IR, incorporation of lexical equivalences and coreference resolution, and a baseline multilingual (English to Dutch) QA system, which uses a combination of Systran and Wikipedia (for term recognition and translation) for question translation. For non-list questions, 31% (20%) of the highest ranked answers returned by the monolingual (multilingual) system were correct.

Automatic extraction of medical term variants from multilingual parallel translations

The extraction of terms and their variants is an important issue in various applications of natural language processing (NLP), such as question answering and information retrieval. This chapter discusses a method to automatically extract medical terms and their variants from a multilingual corpus of parallel translations. As a first step terms are extracted using a pattern-based approach. In order to determine what terms are variants of each other the distributional method used calculates semantic similarity between terms on the basis of translations of these terms in multiple languages. Word alignment techniques were used in combination with phrase extraction techniques from phrase-based machine translation to extract phrases and their translations from a medical parallel corpus. The approach provides a promising strategy for the extraction of term variants using straightforward and fully-automatic techniques. Moreover, the approach is independent of domain and language and can thus be applied to various domains and various languages for which parallel multilingual corpora exist.