4,101 research outputs found
Hypotheses, evidence and relationships: The HypER approach for representing scientific knowledge claims
Biological knowledge is increasingly represented as a collection of (entity-relationship-entity) triplets. These are queried, mined, appended to papers, and published. However, this representation ignores the argumentation contained within a paper and the relationships between hypotheses, claims and evidence put forth in the article. In this paper, we propose an alternate view of the research article as a network of 'hypotheses and evidence'. Our knowledge representation focuses on scientific discourse as a rhetorical activity, which leads to a different direction in the development of tools and processes for modeling this discourse. We propose to extract knowledge from the article to allow the construction of a system where a specific scientific claim is connected, through trails of meaningful relationships, to experimental evidence. We discuss some current efforts and future plans in this area
Accelerating Innovation Through Analogy Mining
The availability of large idea repositories (e.g., the U.S. patent database)
could significantly accelerate innovation and discovery by providing people
with inspiration from solutions to analogous problems. However, finding useful
analogies in these large, messy, real-world repositories remains a persistent
challenge for either human or automated methods. Previous approaches include
costly hand-created databases that have high relational structure (e.g.,
predicate calculus representations) but are very sparse. Simpler
machine-learning/information-retrieval similarity metrics can scale to large,
natural-language datasets, but struggle to account for structural similarity,
which is central to analogy. In this paper we explore the viability and value
of learning simpler structural representations, specifically, "problem
schemas", which specify the purpose of a product and the mechanisms by which it
achieves that purpose. Our approach combines crowdsourcing and recurrent neural
networks to extract purpose and mechanism vector representations from product
descriptions. We demonstrate that these learned vectors allow us to find
analogies with higher precision and recall than traditional
information-retrieval methods. In an ideation experiment, analogies retrieved
by our models significantly increased people's likelihood of generating
creative ideas compared to analogies retrieved by traditional methods. Our
results suggest a promising approach to enabling computational analogy at scale
is to learn and leverage weaker structural representations.Comment: KDD 201
Balancing SoNaR: IPR versus Processing Issues in a 500-Million-Word Written Dutch Reference Corpus
In The Low Countries, a major reference corpus for written Dutch is beingbuilt. We discuss the interplay between data acquisition and data processingduring the creation of the SoNaR Corpus. Based on developments in traditionalcorpus compiling and new web harvesting approaches, SoNaR is designed tocontain 500 million words, balanced over 36 text types including bothtraditional and new media texts. Beside its balanced design, every text sampleincluded in SoNaR will have its IPR issues settled to the largest extentpossible. This data collection task presents many challenges because everydecision taken on the level of text acquisition has ramifications for the levelof processing and the general usability of the corpus. As far as thetraditional text types are concerned, each text brings its own processingrequirements and issues. For new media texts - SMS, chat - the problem is evenmore complex, issues such as anonimity, recognizability and citation right, allpresent problems that have to be tackled. The solutions actually lead to thecreation of two corpora: a gigaword SoNaR, IPR-cleared for research purposes,and the smaller - of commissioned size - more privacy compliant SoNaR,IPR-cleared for commercial purposes as well
An Automated Framework for the Extraction of Semantic Legal Metadata from Legal Texts
Semantic legal metadata provides information that helps with understanding
and interpreting legal provisions. Such metadata is therefore important for the
systematic analysis of legal requirements. However, manually enhancing a large
legal corpus with semantic metadata is prohibitively expensive. Our work is
motivated by two observations: (1) the existing requirements engineering (RE)
literature does not provide a harmonized view on the semantic metadata types
that are useful for legal requirements analysis; (2) automated support for the
extraction of semantic legal metadata is scarce, and it does not exploit the
full potential of artificial intelligence technologies, notably natural
language processing (NLP) and machine learning (ML). Our objective is to take
steps toward overcoming these limitations. To do so, we review and reconcile
the semantic legal metadata types proposed in the RE literature. Subsequently,
we devise an automated extraction approach for the identified metadata types
using NLP and ML. We evaluate our approach through two case studies over the
Luxembourgish legislation. Our results indicate a high accuracy in the
generation of metadata annotations. In particular, in the two case studies, we
were able to obtain precision scores of 97.2% and 82.4% and recall scores of
94.9% and 92.4%
Ontology population for open-source intelligence: A GATE-based solution
Open-Source INTelligence is intelligence based on publicly available sources such as news sites, blogs, forums, etc. The Web is the primary source of information, but once data are crawled, they need to be interpreted and structured. Ontologies may play a crucial role in this process, but because of the vast amount of documents available, automatic mechanisms for their population are needed, starting from the crawled text. This paper presents an approach for the automatic population of predefined ontologies with data extracted from text and discusses the design and realization of a pipeline based on the General Architecture for Text Engineering system, which is interesting for both researchers and practitioners in the field. Some experimental results that are encouraging in terms of extracted correct instances of the ontology are also reported. Furthermore, the paper also describes an alternative approach and provides additional experiments for one of the phases of our pipeline, which requires the use of predefined dictionaries for relevant entities. Through such a variant, the manual workload required in this phase was reduced, still obtaining promising results
Towards a Semantic-based Approach for Modeling Regulatory Documents in Building Industry
Regulations in the Building Industry are becoming increasingly complex and
involve more than one technical area. They cover products, components and
project implementation. They also play an important role to ensure the quality
of a building, and to minimize its environmental impact. In this paper, we are
particularly interested in the modeling of the regulatory constraints derived
from the Technical Guides issued by CSTB and used to validate Technical
Assessments. We first describe our approach for modeling regulatory constraints
in the SBVR language, and formalizing them in the SPARQL language. Second, we
describe how we model the processes of compliance checking described in the
CSTB Technical Guides. Third, we show how we implement these processes to
assist industrials in drafting Technical Documents in order to acquire a
Technical Assessment; a compliance report is automatically generated to explain
the compliance or noncompliance of this Technical Documents
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