175,862 research outputs found
Knowledge Representation of Requirements Documents Using Natural Language Processing
Complex systems such as automotive software systems are usually broken down into subsystems that are specified and developed in isolation and afterwards integrated to provide the functionality of the desired system. This results in a large number of requirements documents for each subsystem written by different people and in different departments. Requirements engineers are challenged by comprehending the concepts mentioned in a requirement because coherent information is spread over several requirements documents. In this paper, we describe a natural language processing pipeline that we developed to transform a set of heterogeneous natural language requirements into a knowledge representation graph. The graph provides an orthogonal view onto the concepts and relations written in the requirements. We provide a first validation of the approach by applying it to two requirements documents including more than 7,000 requirements from industrial systems. We conclude the paper by stating open challenges and potential application of the knowledge representation graph
Using NLP tools in the specification phase
The software quality control is one of the main topics in the Software
Engineering area. To put the effort in the quality control during the
specification phase leads us to detect possible mistakes in an early
steps and, easily, to correct them before the design and implementation
steps start. In this framework the goal of SAREL system, a
knowledge-based system, is twofold. On one hand, to help software
engineers in the creation of quality Software Requirements
Specifications. On the other hand, to analyze the correspondence between
two different conceptual representations associated with two different
Software Requirements Specification documents.
For the first goal, a set of NLP and Knowledge management tools is
applied to obtain a conceptual representation that can be validated and
managed by the software engineer.
For the second goal we have established some correspondence measures in
order to get a comparison between two conceptual representations. This
information will be useful during the interaction.Postprint (published version
The Synonym management process in SAREL
The specification phase is one of the most important and least supported
parts of the software development process. The SAREL system has been
conceived as a knowledge-based tool to improve the specification phase.
The purpose of SAREL (Assistance System for Writing Software
Specifications in Natural Language) is to assist engineers in the
creation of software specifications written in Natural Language (NL).
These documents are divided into several parts. We can distinguish the
Introduction and the Overall Description as parts that should be used in
the Knowledge Base construction. The information contained in the
Specific Requirements Section corresponds to the information represented
in the Requirements Base. In order to obtain high-quality software
requirements specification the writing norms that define the linguistic
restrictions required and the software engineering constraints related
to the quality factors have been taken into account. One of the controls
performed is the lexical analysis that verifies the words belong to the
application domain lexicon which consists of the Required and the
Extended lexicon. In this sense a synonym management process is needed
in order to get a quality software specification. The aim of this paper
is to present the synonym management process performed during the
Knowledge Base construction. Such process makes use of the Spanish
Wordnet developed inside the Eurowordnet project. This process generates
both the Required lexicon and the Extended lexicon that will be used
during the Requirements Base construction.Postprint (published version
Multilingual manager: a new strategic role in organizations
Today?s knowledge management (KM) systems seldom account for language management and, especially, multilingual information processing. Document management is one of the strongest components of KM systems. If these systems do not include a multilingual knowledge management policy, intranet searches, excessive document space occupancy and redundant information slow down what are the most effective processes in a single language environment. In this paper, we model information flow from the sources of knowledge to the persons/systems searching for specific information. Within this framework, we focus on the importance of multilingual information processing, which is a hugely complex component of modern organizations
Multi modal multi-semantic image retrieval
PhDThe rapid growth in the volume of visual information, e.g. image, and video can
overwhelm users’ ability to find and access the specific visual information of interest
to them. In recent years, ontology knowledge-based (KB) image information retrieval
techniques have been adopted into in order to attempt to extract knowledge from these
images, enhancing the retrieval performance. A KB framework is presented to
promote semi-automatic annotation and semantic image retrieval using multimodal
cues (visual features and text captions). In addition, a hierarchical structure for the KB
allows metadata to be shared that supports multi-semantics (polysemy) for concepts.
The framework builds up an effective knowledge base pertaining to a domain specific
image collection, e.g. sports, and is able to disambiguate and assign high level
semantics to ‘unannotated’ images.
Local feature analysis of visual content, namely using Scale Invariant Feature
Transform (SIFT) descriptors, have been deployed in the ‘Bag of Visual Words’
model (BVW) as an effective method to represent visual content information and to
enhance its classification and retrieval. Local features are more useful than global
features, e.g. colour, shape or texture, as they are invariant to image scale, orientation
and camera angle. An innovative approach is proposed for the representation,
annotation and retrieval of visual content using a hybrid technique based upon the use
of an unstructured visual word and upon a (structured) hierarchical ontology KB
model. The structural model facilitates the disambiguation of unstructured visual
words and a more effective classification of visual content, compared to a vector
space model, through exploiting local conceptual structures and their relationships.
The key contributions of this framework in using local features for image
representation include: first, a method to generate visual words using the semantic
local adaptive clustering (SLAC) algorithm which takes term weight and spatial
locations of keypoints into account. Consequently, the semantic information is
preserved. Second a technique is used to detect the domain specific ‘non-informative
visual words’ which are ineffective at representing the content of visual data and
degrade its categorisation ability. Third, a method to combine an ontology model with
xi
a visual word model to resolve synonym (visual heterogeneity) and polysemy
problems, is proposed. The experimental results show that this approach can discover
semantically meaningful visual content descriptions and recognise specific events,
e.g., sports events, depicted in images efficiently.
Since discovering the semantics of an image is an extremely challenging problem, one
promising approach to enhance visual content interpretation is to use any associated
textual information that accompanies an image, as a cue to predict the meaning of an
image, by transforming this textual information into a structured annotation for an
image e.g. using XML, RDF, OWL or MPEG-7. Although, text and image are distinct
types of information representation and modality, there are some strong, invariant,
implicit, connections between images and any accompanying text information.
Semantic analysis of image captions can be used by image retrieval systems to
retrieve selected images more precisely. To do this, a Natural Language Processing
(NLP) is exploited firstly in order to extract concepts from image captions. Next, an
ontology-based knowledge model is deployed in order to resolve natural language
ambiguities. To deal with the accompanying text information, two methods to extract
knowledge from textual information have been proposed. First, metadata can be
extracted automatically from text captions and restructured with respect to a semantic
model. Second, the use of LSI in relation to a domain-specific ontology-based
knowledge model enables the combined framework to tolerate ambiguities and
variations (incompleteness) of metadata. The use of the ontology-based knowledge
model allows the system to find indirectly relevant concepts in image captions and
thus leverage these to represent the semantics of images at a higher level.
Experimental results show that the proposed framework significantly enhances image
retrieval and leads to narrowing of the semantic gap between lower level machinederived
and higher level human-understandable conceptualisation
Natural Language Processing for Information Retrieval and Knowledge Discovery
Natural Language Processing (NLP) is a powerful technology for the vital tasks of information retrieval (IR) and knowledge discovery (KD) which, in turn, feed the visualization systems of the present and future and enable knowledge workers to focus more of their time on the vital tasks of analysis and prediction.published or submitted for publicatio
Knowledge Extraction from Natural Language Requirements into a Semantic Relation Graph
Knowledge extraction and representation aims to identify information and to transform it into a machine-readable format. Knowledge representations support Information Retrieval tasks such as searching for single statements, documents, or metadata.
Requirements specifications of complex systems such as automotive software systems are usually divided into different subsystem specifications. Nevertheless, there are semantic relations between individual documents of the separated subsystems, which have to be considered in further processes (e.g. dependencies). If requirements engineers or other developers are not aware of these relations, this can lead to inconsistencies or malfunctions of the overall system. Therefore, there is a strong need for tool support in order to detects semantic relations in a set of large natural language requirements specifications.
In this work we present a knowledge extraction approach based on an explicit knowledge representation of the content of natural language requirements as a semantic relation graph. Our approach is fully automated and includes an NLP pipeline to transform unrestricted natural language requirements into a graph. We split the natural language into different parts and relate them to each other based on their semantic relation. In addition to semantic relations, other relationships can also be included in the graph. We envision to use a semantic search algorithm like spreading activation to allow users to search different semantic relations in the graph
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