Towards a workbench for acquisition of domain knowledge from natural language

Mikheev, Andrei; Finch, Steven

doi:10.3115/976973.977001

Cited by 17 publications

(10 citation statements)

References 3 publications

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“…Approaches to the identification of domain-specific keywords -the terminology of the domain -generally rely on extensive prior linguistic knowledge and linguistic extraction techniques [9], [10], [11], [12], [13]: use of part-of-speech (POS) taggers predominates. Our treatment differs from these approaches in taking an initially statistical approach, which is suitable for subsequent augmentation using linguistic techniques.…”

Section: A Methods For Extracting Ontologymentioning

confidence: 99%

Automatic Ontology Extraction from Unstructured Texts

Ahmad

Gillam

2005

Lecture Notes in Computer Science

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Abstract. Construction of the ontology of a specific domain currently relies on the intuition of a knowledge engineer, and the typical output is a thesaurus of terms, each of which is expected to denote a concept. Ontological 'engineers' tend to hand-craft these thesauri on an ad-hoc basis and on a relatively smallscale. Workers in the specific domain create their own special language, and one device for this creation is the repetition of select keywords for consolidating or rejecting one or more concepts. A more scalable, systematic and automatic approach to ontology construction is possible through the automatic identification of these keywords. An approach for the study and extraction of keywords is outlined where a corpus of randomly collected unstructured, i.e. not containing any kind of mark-up, texts in a specific domain is analysed with reference to the lexical preferences of the workers in the domain. An approximation about the role of frequently used single words within multiword expressions leads us to the creation of a semantic network. The network can be asserted into a terminology database or knowledge representation formalism, and the relationship between the nodes of the network helps in the visualisation of, and automatic inference over, the frequently used words denoting important concepts in the domain. We illustrate our approach with a case study using corpora from three time periods on the emergence and consolidation of nuclear physics. The text-based approach appears to be less subjective and more suitable for introspection, and is perhaps useful in ontology evolution.

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Section: A Methods For Extracting Ontologymentioning

confidence: 99%

Automatic Ontology Extraction from Unstructured Texts

Ahmad

Gillam

2005

Lecture Notes in Computer Science

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“…We think, as others ( [30], [2], [22]), that domain modeling would benefit from a close interaction between linguistic methods or tools and computer-aided knowledge engineering methods or tools. Ontology, terminology, and lexical semantics aim to describe the world through the words of the language, in all language's generality for lexical semantics, restricted to a technical domain for terminology and for ontology as we have defined it.…”

Section: Conclusion About the Experimentsmentioning

confidence: 99%

“…KAWB (Knowledge Acquisition WorkBench) [22] is an example of advanced integration of linguistic tools. It is a semi-implemented tool to acquire semantic features of a domain from large text corpora.…”

Section: A Computational Linguistics Based Knowledge Engineering Toolmentioning

confidence: 99%

TERMINAE: A Linguistics-Based Tool for the Building of a Domain Ontology

Biebow

Szulman

1999

Knowledge Acquisition, Modeling and Management

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Abstract. The purpose of TERMINAE is to help building an ontology, both from scratch and from texts, without control by any task. Requirements have been defined for a methodology on the basis of real experiments. TERMINAE fulfills these requirements, involving theoretical bases from linguistics and knowledge representation. Its strong points are integration of a terminological approach and an ontology management, precise definition of concept types reflecting modeling choices, and traceability facilities. This paper presents briefly the experiments leading to the requirements, and focuses on the tool and its underlying methodology. Practical and theoretical basis of TERMINAEIn what follows, we use the term "domain ontology", with the meaning of "ontology" in [33]: " An ontology is a hierarchically structured set of terms for describing a domain that can be used as a skeletal foundation for a knowledge base ". This definition seems to be totally compatible with that of [16]: " An ontology is a logical theory accounted for the intended meaning of a formal vocabulary, i.e. its ontological commitment to a particular conceptualization of the world ". We use also the same distinction between top-level ontology, domain ontology, task ontology and application ontology as in [16]. We agree too with Guarino's definition of a knowledge base as being obtained by specialization of an ontology to a particular state of the world.In TERMINAE, the computer-aided tool presented in this paper, we often use the term "knowledge base" for "ontology of a generic knowledge base" because what we speak about is always state-independent, and the term "ontology management" is not yet widely used. TERMINAE is used as an "ontology management" tool, even if its representation language allows the description of facts by individual concepts.

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“…inclusion" and reportedly use term banks to identify term inclusion (Mikheev and Finch 1995). Other authors use WordNet to discover semantic relations between terms, although they are unconvincing with regard to term identification (Navigli, Velardi and Gangemi 2003).…”

Section: A Terminological Perspective On Ontologymentioning

confidence: 99%

“…Typically, approaches to ontology "learning", and here consideration is constrained to extraction from free text, are based on syntactic parsing (Maedche and Volz 2001, Maedche and Staab 2003, Faure and Nédellec 1998, Mikheev and Finch 1995. Some authors augment such an approach using TFIDF 1 , word clustering using simple stemming, and coded linguistic relationships (Maedche and Staab 2003).…”

Section: A Terminological Perspective On Ontologymentioning

confidence: 99%

Terminology and the construction of ontology

Gillam¹,

Tariq²,

Ahmad³

2005

TERM

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Abstract. This paper discusses a method for extracting conceptual hierarchies from arbitrary domain-specific collections of text. These hierarchies can form a basis for a concept-oriented terminology collection, and hence may be used as the basis for developing knowledge-based systems via ontology editors. This reference to ontology is explored in the context of collections of terms. The method presented uses both statistical and linguistic techniques. The result of such an extraction may be useful in information retrieval, knowledge management, or in the discipline of terminology science itself.

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Towards a workbench for acquisition of domain knowledge from natural language

Cited by 17 publications

References 3 publications

Automatic Ontology Extraction from Unstructured Texts

Automatic Ontology Extraction from Unstructured Texts

TERMINAE: A Linguistics-Based Tool for the Building of a Domain Ontology

Terminology and the construction of ontology

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