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Documents authored by Ell, Basil

Document
DOI: 10.4230/OASIcs.LDK.2021.31
Towards Scope Detection in Textual Requirements

Authors: Ole Magnus Holter and Basil Ell

Published in: OASIcs, Volume 93, 3rd Conference on Language, Data and Knowledge (LDK 2021)

Abstract
Requirements are an integral part of industry operation and projects. Not only do requirements dictate industrial operations, but they are used in legally binding contracts between supplier and purchaser. Some companies even have requirements as their core business. Most requirements are found in textual documents, this brings a couple of challenges such as ambiguity, scalability, maintenance, and finding relevant and related requirements. Having the requirements in a machine-readable format would be a solution to these challenges, however, existing requirements need to be transformed into machine-readable requirements using NLP technology. Using state-of-the-art NLP methods based on end-to-end neural modelling on such documents is not trivial because the language is technical and domain-specific and training data is not available. In this paper, we focus on one step in that direction, namely scope detection of textual requirements using weak supervision and a simple classifier based on BERT general domain word embeddings and show that using openly available data, it is possible to get promising results on domain-specific requirements documents.
Cite as

Ole Magnus Holter and Basil Ell. Towards Scope Detection in Textual Requirements. In 3rd Conference on Language, Data and Knowledge (LDK 2021). Open Access Series in Informatics (OASIcs), Volume 93, pp. 31:1-31:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{holter_et_al:OASIcs.LDK.2021.31,
  author =	{Holter, Ole Magnus and Ell, Basil},
  title =	{{Towards Scope Detection in Textual Requirements}},
  booktitle =	{3rd Conference on Language, Data and Knowledge (LDK 2021)},
  pages =	{31:1--31:15},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-199-3},
  ISSN =	{2190-6807},
  year =	{2021},
  volume =	{93},
  editor =	{Gromann, Dagmar and S\'{e}rasset, Gilles and Declerck, Thierry and McCrae, John P. and Gracia, Jorge and Bosque-Gil, Julia and Bobillo, Fernando and Heinisch, Barbara},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.LDK.2021.31},
  URN =		{urn:nbn:de:0030-drops-145674},
  doi =		{10.4230/OASIcs.LDK.2021.31},
  annote =	{Keywords: Scope Detection, Textual requirements, NLP}
}
Document
DOI: 10.4230/OASIcs.LDK.2021.33
Bridging the Gap Between Ontology and Lexicon via Class-Specific Association Rules Mined from a Loosely-Parallel Text-Data Corpus

Authors: Basil Ell, Mohammad Fazleh Elahi, and Philipp Cimiano

Published in: OASIcs, Volume 93, 3rd Conference on Language, Data and Knowledge (LDK 2021)

Abstract
There is a well-known lexical gap between content expressed in the form of natural language (NL) texts and content stored in an RDF knowledge base (KB). For tasks such as Information Extraction (IE), this gap needs to be bridged from NL to KB, so that facts extracted from text can be represented in RDF and can then be added to an RDF KB. For tasks such as Natural Language Generation, this gap needs to be bridged from KB to NL, so that facts stored in an RDF KB can be verbalized and read by humans. In this paper we propose LexExMachina, a new methodology that induces correspondences between lexical elements and KB elements by mining class-specific association rules. As an example of such an association rule, consider the rule that predicts that if the text about a person contains the token "Greek", then this person has the relation nationality to the entity Greece. Another rule predicts that if the text about a settlement contains the token "Greek", then this settlement has the relation country to the entity Greece. Such a rule can help in question answering, as it maps an adjective to the relevant KB terms, and it can help in information extraction from text. We propose and empirically investigate a set of 20 types of class-specific association rules together with different interestingness measures to rank them. We apply our method on a loosely-parallel text-data corpus that consists of data from DBpedia and texts from Wikipedia, and evaluate and provide empirical evidence for the utility of the rules for Question Answering.
Cite as

Basil Ell, Mohammad Fazleh Elahi, and Philipp Cimiano. Bridging the Gap Between Ontology and Lexicon via Class-Specific Association Rules Mined from a Loosely-Parallel Text-Data Corpus. In 3rd Conference on Language, Data and Knowledge (LDK 2021). Open Access Series in Informatics (OASIcs), Volume 93, pp. 33:1-33:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{ell_et_al:OASIcs.LDK.2021.33,
  author =	{Ell, Basil and Elahi, Mohammad Fazleh and Cimiano, Philipp},
  title =	{{Bridging the Gap Between Ontology and Lexicon via Class-Specific Association Rules Mined from a Loosely-Parallel Text-Data Corpus}},
  booktitle =	{3rd Conference on Language, Data and Knowledge (LDK 2021)},
  pages =	{33:1--33:21},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-199-3},
  ISSN =	{2190-6807},
  year =	{2021},
  volume =	{93},
  editor =	{Gromann, Dagmar and S\'{e}rasset, Gilles and Declerck, Thierry and McCrae, John P. and Gracia, Jorge and Bosque-Gil, Julia and Bobillo, Fernando and Heinisch, Barbara},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.LDK.2021.33},
  URN =		{urn:nbn:de:0030-drops-145691},
  doi =		{10.4230/OASIcs.LDK.2021.33},
  annote =	{Keywords: Ontology, Lexicon, Association Rules, Pattern Mining}
}
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