22,230 research outputs found
A Factoid Question Answering System for Vietnamese
In this paper, we describe the development of an end-to-end factoid question
answering system for the Vietnamese language. This system combines both
statistical models and ontology-based methods in a chain of processing modules
to provide high-quality mappings from natural language text to entities. We
present the challenges in the development of such an intelligent user interface
for an isolating language like Vietnamese and show that techniques developed
for inflectional languages cannot be applied "as is". Our question answering
system can answer a wide range of general knowledge questions with promising
accuracy on a test set.Comment: In the proceedings of the HQA'18 workshop, The Web Conference
Companion, Lyon, Franc
PFTijah: text search in an XML database system
This paper introduces the PFTijah system, a text search system that is integrated with an XML/XQuery database management system. We present examples of its use, we explain some of the system internals, and discuss plans for future work. PFTijah is part of the open source release of MonetDB/XQuery
Towards an Intelligent Database System Founded on the SP Theory of Computing and Cognition
The SP theory of computing and cognition, described in previous publications,
is an attractive model for intelligent databases because it provides a simple
but versatile format for different kinds of knowledge, it has capabilities in
artificial intelligence, and it can also function like established database
models when that is required.
This paper describes how the SP model can emulate other models used in
database applications and compares the SP model with those other models. The
artificial intelligence capabilities of the SP model are reviewed and its
relationship with other artificial intelligence systems is described. Also
considered are ways in which current prototypes may be translated into an
'industrial strength' working system
Embedding-based Scientific Literature Discovery in a Text Editor Application
Each claim in a research paper requires all relevant prior knowledge to be
discovered, assimilated, and appropriately cited. However, despite the
availability of powerful search engines and sophisticated text editing
software, discovering relevant papers and integrating the knowledge into a
manuscript remain complex tasks associated with high cognitive load. To define
comprehensive search queries requires strong motivation from authors,
irrespective of their familiarity with the research field. Moreover, switching
between independent applications for literature discovery, bibliography
management, reading papers, and writing text burdens authors further and
interrupts their creative process. Here, we present a web application that
combines text editing and literature discovery in an interactive user
interface. The application is equipped with a search engine that couples
Boolean keyword filtering with nearest neighbor search over text embeddings,
providing a discovery experience tuned to an author's manuscript and his
interests. Our application aims to take a step towards more enjoyable and
effortless academic writing.
The demo of the application (https://SciEditorDemo2020.herokuapp.com/) and a
short video tutorial (https://youtu.be/pkdVU60IcRc) are available online
Utilising semantic technologies for intelligent indexing and retrieval of digital images
The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return inaccurate matches as they in principle rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this paper we present a semantically-enabled image annotation and retrieval engine that is designed to satisfy the requirements of the commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as the exploitation of lexical databases for explicit semantic-based query expansion
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