ATOLL—A framework for the automatic induction of ontology lexica

Abstract

There is a range of large knowledge bases, such as Freebase and DBpedia, as well as linked data sets available on the web, but they typically lack lexical information stating how the properties and classes they comprise are realized lexically. Often only one label is attached, if at all, thus lacking rich linguistic information, e.g. about morphological forms, syntactic arguments or possible lexical variants and paraphrases. While ontology lexicon models like lemon allow for defining such linguistic information with respect to a given ontology, the cost involved in creating and maintaining such lexica is substantial, requiring a high manual effort. Towards lowering this effort we present ATOLL, a framework for the automatic induction of ontology lexica, based both on existing labels and dependency paths extracted from a text corpus. We instantiate ATOLL with respect to DBpedia as dataset and Wikipedia as corresponding corpus, and evaluate it by comparing the automatically generated lexicon with a manually constructed one. Our results clearly corroborate that our approach shows a high potential to be applied in a semi-automatic fashion in which a lexicon engineer can validate, reject or refine the automatically generated lexical entries, thus having a clear potential to contributing to the reduction the overall cost of creating ontology lexica.

Learning Embeddings to lexicalise RDF Properties

“…4 http://wiki.dbpedia.org/ 5 https://github.com/ag-sc/matoll DBlexipedia e : a lexicon 6 automatically inferred from Wikipedia using the method described in (Walter et al, 2013;Walter et al, 2014a;Walter et al, 2014b) (c.f. section 2).…”

“…For instance, given the property name HASADRESS, this approach will produce the candidate lexicalisation "the address of S is O" where S and O are place-holders for the lexicalisations of the subject and object entity in the input RDF triple. (Walter et al, 2013;Walter et al, 2014a;Walter et al, 2014b) describes an approach for inducing a lexicon mapping DBPedia properties to possible lexicalisations. The approach combines a label-based and a pattern-based method.…”

Applying Semantic Parsing to Question Answering Over Linked Data: Addressing the Lexical Gap

“…However, for large domains the required manual effort is not always feasible. Therefore we ran M-ATOLL [12,13], a system that automatically extracts lexicalizations for ontology elements from a text corpus, on the predicates used in the training dataset. It managed to find 10 of the required 54 lexical items.…”

Automatic Acquisition of Adjective Lexicalizations of Restriction Classes: a Machine Learning Approach

“…In this paper, we have extended our ontology lexicalization approach M-ATOLL [26,27] to adjectives. This makes it more extensive than other existing ontology lexicalization approaches, such as BOA [8] and WRPA [25], which do not consider adjective lexicalizations.…”