A Method for Semi-Automatic Ontology Acquisition from a Corporate Intranet
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1 A Method for Semi-Automatic Ontology Acquisition from a Corporate Intranet Joerg-Uwe Kietz, Alexander Maedche, Raphael Volz Swisslife Information Systems Research Lab, Zuerich, Switzerland fkietz, volzg@swisslife.ch AIFB, Univ. Karlsruhe, D Karlsruhe, Germany maedche@aifb.uni-karlsruhe.de Abstract This paper describes our actual and ongoing work in supporting semiautomatic ontology acquisition from a corporate intranet of an insurance company. A comprehensive architecture and a system for semi-automatic ontology acquisition supports processing semi-structured information (e.g. contained in dictionaries) and natural language documents and including existing core ontologies (e.g. GermaNet, WordNet). We present a method for acquiring a applicationtailored domain ontology from given heterogeneous intranet sources. 1 Introduction Ontologies have proved their usefulness in different applications scenarios, such as intelligent information integration, knowledge-based systems, natural language processing. The role of ontologies is to capture domain knowledge in a generic way and provide a commonly agreed upon understanding of a domain. The common vocabulary of an ontology, defining the meaning of terms and relations, is usually organized in a taxonomy. An ontology usually contains modeling primitives such as concepts, generic relations between concepts, and axioms. In this paper we describe our actual and ongoing work in supporting semi-automatic ontology acquisition from a corporate intranet of an insurance company. Corporate intranets have been developed in the last years to large information resources containing useful information, such as dictionaries, instruction catalogues, collection of interesting publications of the domain, internal information, etc. However, the information stored in corporate intranets is only accessible and searcheable using standard information retrieval techniques. This is due to that the information expressed in natural language is not machine processable. Nevertheless, a lot of useful concepts and conceptual structures of the domain and the company terminology is contained on the intranet. Our approach underlies different heterogeneous sources: First, a generic core ontology has been taken as a top level structure for our domain-specific goal ontology. It has been transformed into our ontology representation formalism. Second, a dictionary containing the most important 1500 corporate terms described with natural language definitions served as an input for the ontology construction. Third, we accessed around 1000 intranet documents containing business reports and general company information.
2 2 Architecture The general architecture of the core approach for semi-automatic ontology learning from natural language has been described in [5]. The main components of the system are a Text & Processing Management component, a Text Processing Server, a component containing different algorithms for ontology learning and pruning coming with a multistrategy learning result set, and the Ontology Engineering System OntoEdit [10]. semi-structured information, e.g. domain-specific dictionaries natural language texts Ontology WordNet feed feed Select core ontology GermaNet Text & Processing Management Built core ontology (XML tagged) text &selected algorithms uses Ontology Learning & Ontology Pruning Algorithms selected text & preprocessing method XMLtagged text Multi - Strategy Learning Result Set Stemming Text Processing Server POS tagging chunk parsing Information Extraction... uses Domain Ontology interacts domain lexicon models edits Proposes new Conceptual structures (concepts, isa relations, generic relations) OntoEdit Ontology Modeling Environment uses Lexical DB Figure1. Architecture of the Ontology Learning Approach The Text & Processing component supports efficient handling and processing of the different input sources (semi-structured information, natural language documents, core ontologies), the definition of linguistic processing steps, the application of several algorithms. The Text Processing Server (SMES) is an information extraction system including shallow parsing mechanisms at different processing levels. It is based on the core systems SMES (Saarbrücken Message Extraction System), a shallow text processor for German (cf. [8]). SMES is a system that performs syntactic analysis on natural language documents. In general, the Text Processing Server is organized in modules, such as a tokenizer, morphological and lexical processing, and chunk parsing that use lexical resources to produce mixed syntactic/semantic information. The results of text processing are stored in annotations using XML-tagged text.
3 The Ontology Learning & Pruning Component includes a pattern definition and matching module, several term extraction mechanisms based on term frequency/inverted document frequency [9], and an algorithm for discovering conceptual relations. A Multi-Strategy Learning Result Set is used to support the complex task of ontology learning: It is possible to combine results from different learning methods, that have been applied to different sources. As described by [6] multi-strategy learning architectures support balancing between advantages and disadvantages of different learning methods. The Ontology Engineering System OntoEdit supports the ontology engineer in semiautomatically adding newly discovered structures to the ontology. 1 In addition to core capabilities for structuring the ontology, the engineering environment provides some additional features for the purpose of documentation, maintenance, and ontology exchange. OntoEdit internally stores ontologies using an XML serialization of the ontology model. 3 The Acquisition Process Based on the architecture described above we developed an ontology acquisition methodology depicted in Figure 2. The acquisition cycle starts with selecting a core ontology (cf. subsection 3.1). In our case we decided to adopt the GermaNet lexical semantic network described in [2] and transformed it to our internal ontology representation. In general, core or top level ontologies like GermaNet, do not contain domain-specific concepts. First, domain-specific concepts are acquired from the sources and are embedded into the concept taxonomy. In our case as described in subsection 3.2 we used a domain-specific dictionary. Additionally, the documents have been linguistically processed and frequency-based term extraction mechanisms for concept acquisition have been applied. Second, the ontology is focused or pruned to the application domain. This step is further explained in subsection 3.3. Third, domain-specific conceptual relations are extracted from natural language documents (cf. subsection 3.4). The resulting domain-specific ontology can be further refined and improved repeating the acquisition cycle. Our cyclic approach supports the evolving nature of ontologies and natural language in general. We have to emphasize that the acquisition process described in the following is a semi-automatic process with necessary human intervention. 3.1 Transforming GermaNet GermaNet is the german counterpart to the well known WordNet. It builds a lexical semantic network for german words, where 3 different types of word classes are distinguished: nouns, verbs and adjectives. Words are grouped into sets of synomyms so called synsets. These sets are converted to concepts. Some semantic relations between synsets are converted to conceptual relations, especially the hypernym-hyponym 1 A comprehensive description of the ontology engineering system OntoEdit and the underlying methodology is given in [10].
4 Figure2. Semi-Automatic Ontology Acquisition Process relations are used to construct the concept taxonomy. We have to emphasize that other generic top-level ontologies like CyC, Dahlgren s or Sowa s ontology could also be used to start the domain ontology acquisition process. 3.2 Extracting domain-specific concepts and a concept hierarchy As already mentioned we used a dictionary of corporate terms to acquire domain specific concepts. Dictionary entries are considered as concepts. The entires are aligned into the taxonomy using their natural language descriptions. The descriptions are processed by the Text Processing Server. Several heuristic patterns for extracting taxonomic relations similar to [3] are used to acquire the taxonomic alignment. Additionally, all phrases have been extracted from the intranet documents using linguistic processing techniques. Frequent phrases which tend to be concepts have been detected using statistical techniques. 3.3 Domain-specific Pruning By now the generic core ontology has been extended with domain-specific components. In order to prune concepts that are domain-unspecific, concept frequencies are determined from the intranet corpus. By cumulating the frequencies of sub concepts within their super concepts, concept frequencies are propagated through the taxonomy. The frequencies are compared to concept frequencies acquired from generic corpora. Concepts that are less frequent than an user specified minimum are deleted from the ontology (see figure 3).
5 1. Given: ontology, corpus, mininimum frequency 2. Determine concept frequencies from corpus (a) Count concept frequencies in corpus (b) Propagate frequencies to super concepts (c) Compare to concept frequencies from generic corpora 3. Remove concepts that do not support minimum frequencies (a) Remove concept relations and restrictions from ontology (b) Remove concept from ontology Figure3. Focusing on the domain 3.4 Acquisition of Conceptual Relations We used two methods to learn conceptual relations. First, we used a statistical approach based on association rules as described in [4]. The algorithm uses the background knowledge from the concept taxonomy in order to propose relations at the appropriate level of abstraction. For instance, the linguistic processing may find that the word insurance agreement frequently co-occurs with each of the words policyholder and insurance salesman. From this statistical linguistic data our approach derives correlations at the conceptual level, viz. between the concept InsuranceAgreement and the concepts, PolicyHolder and InsuranceSalesman. The discovery algorithm determines support and confidence measures for the relationships between these three pairs, as well as for relationships at higher levels of abstraction, such as between InsuranceAgreement and Person. In a final step, the algorithm determines the level of abstraction most suited to describe the conceptual relationships by pruning appearingly less adequate ones. Here, the relation between InsuranceAgreement and Person may be proposed for inclusion in the ontology. Second, we used heuristic patterns defined as regular expressions to extract nontaxonomic conceptual relations from text as used in [7] for the acquisition of hyponyms. In our current work we have defined some patterns on top of the phrase-level processed documents, such as NP have NP. 4 Conclusions & Further Work In this paper we have described our recent and ongoing work in semi-automatic ontology acquisition from a corporate intranet. Based on our comprehensive architecture a new approach for supporting the overall process of engineering ontologies from text is described. It is mainly based on a given core ontology, which is extended with domain specific concepts. The resulting ontology is pruned and restricted to a specific application using a corpus-based mechanism for ontology pruning. On top of the ontology two approaches supporting the difficult task of determining non-taxonomic conceptual relationships are applied. In the future much work remains to be done. First, several techniques for evaluating the acquired ontology have to be developed. In our scenario we will apply ontology cross comparison techniques such as described in [4]. Additionally, applying the ontology on top of the intranet documents (e.g. a information retrieval scenario, a semantic
6 document annotation scenario such as described in [1]) will allow us an applicationspecific evaluation of the ontology using standard measures such as precision and recall. Second, our approach for multi-strategy learning is still in an early stage. We will have to elaborate how the results of different learning algorithms will have to be assessed and combined in the multi-strategy learning set. Nevertheless, an approach combing different resources on which different techniques are applied, seems promising for supporting the complex task of ontology learning from text. References 1. M. Erdmann, A. Maedche, H.-P. Schnurr, and Steffen Staab. From manual to semi-automatic semantic annotation: About ontology-based text annotation tools. In P. Buitelaar & K. Hasida (eds). Proceedings of the COLING 2000 Workshop on Semantic Annotation and Intelligent Content, Luxembourg, August B. Hamp and H. Feldweg. Germanet - a lexical-semantic net for german. In Proceedings of ACL workshop Automatic Information Extraction and Building of Lexical Semantic Resources for NLP Applications, Madrid., M.A. Hearst. Automatic acquisition of hyponyms from large text corpora. In Proceedings of the 14th International Conference on Computational Linguistics. Nantes, France, A. Maedche and S. Staab. Discovering conceptual relations from text. In Proceedings of ECAI IOS Press, Amsterdam, A. Maedche and S. Staab. Semi-automatic engineering of ontologies from text. In Proceedings of the 12th Internal Conference on Software and Knowledge Engineering. Chicago, USA. KSI, R. Michalski and K. Kaufmann. Data mining and knowledge discovery: A review of issues and multistrategy approach. In Machine Learning and Data Mining Methods and Applications. John Wiley, England, E. Morin. Automatic acquisition of semantic relations between terms from technical corpora. In Proc. of the Fifth International Congress on Terminology and Knowledge Engineering - TKE 99, G. Neumann, R. Backofen, J. Baur, M. Becker, and C. Braun. An information extraction core system for real world german text processing. In ANLP 97 Proceedings of the Conference on Applied Natural Language Processing, pages , Washington, USA, G. Salton. Automatic Text Processing. Addison-Wesley, S. Staab and A. Maedche. Ontology engineering beyond the modeling of concepts and relations. In Proceedings of the ECAI 2000 Workshop on Application of Ontologies and Problem-Solving Methods, 2000.
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