2,770 research outputs found
Enterprise engineering using semantic technologies
Modern Enterprises are facing unprecedented challenges in every aspect of their businesses: from marketing research, invention of products, prototyping, production, sales to billing. Innovation is the key to enhancing enterprise performances and knowledge is the main driving force in creating innovation. The identification and effective management of valuable knowledge, however, remains an illusive topic. Knowledge management (KM) techniques, such as enterprise process modelling, have long been recognised for their value and practiced as part of normal business. There are plentiful of KM techniques. However, what is still lacking is a holistic KM approach that enables one to fully connect KM efforts with existing business knowledge and practices already in IT systems, such as organisational memories. To address this problem, we present an integrated three-dimensional KM approach that supports innovative semantics technologies. Its automated formal methods allow us to tap into modern business practices and capitalise on existing knowledge. It closes the knowledge management cycle with user feedback loops. Since we are making use of reliable existing knowledge and methods, new knowledge can be extracted with less effort comparing with another method where new information has to be created from scratch
Analogy Mining for Specific Design Needs
Finding analogical inspirations in distant domains is a powerful way of
solving problems. However, as the number of inspirations that could be matched
and the dimensions on which that matching could occur grow, it becomes
challenging for designers to find inspirations relevant to their needs.
Furthermore, designers are often interested in exploring specific aspects of a
product-- for example, one designer might be interested in improving the
brewing capability of an outdoor coffee maker, while another might wish to
optimize for portability. In this paper we introduce a novel system for
targeting analogical search for specific needs. Specifically, we contribute a
novel analogical search engine for expressing and abstracting specific design
needs that returns more distant yet relevant inspirations than alternate
approaches
Natural Language Processing in-and-for Design Research
We review the scholarly contributions that utilise Natural Language
Processing (NLP) methods to support the design process. Using a heuristic
approach, we collected 223 articles published in 32 journals and within the
period 1991-present. We present state-of-the-art NLP in-and-for design research
by reviewing these articles according to the type of natural language text
sources: internal reports, design concepts, discourse transcripts, technical
publications, consumer opinions, and others. Upon summarizing and identifying
the gaps in these contributions, we utilise an existing design innovation
framework to identify the applications that are currently being supported by
NLP. We then propose a few methodological and theoretical directions for future
NLP in-and-for design research
Ontologies on the semantic web
As an informational technology, the World Wide Web has enjoyed spectacular success. In just ten years it has transformed the way information is produced, stored, and shared in arenas as diverse as shopping, family photo albums, and high-level academic research. The “Semantic Web” was touted by its developers as equally revolutionary but has not yet achieved anything like the Web’s exponential uptake. This 17 000 word survey article explores why this might be so, from a perspective that bridges both philosophy and IT
Flexible RDF data extraction from Wiktionary - Leveraging the power of community build linguistic wikis
We present a declarative approach implemented in a comprehensive opensource
framework (based on DBpedia) to extract lexical-semantic resources (an ontology about language use) from Wiktionary. The data currently includes language, part of speech, senses, definitions, synonyms, taxonomies (hyponyms, hyperonyms, synonyms, antonyms) and translations for each lexical word. Main focus is on flexibility to the loose schema and configurability towards differing language-editions ofWiktionary. This is achieved by a declarative mediator/wrapper approach. The goal is, to allow the addition of languages just by configuration without the need of programming, thus enabling the swift and resource-conserving adaptation of wrappers by domain experts. The extracted data is as fine granular as the source data in Wiktionary and additionally follows the lemon model. It enables use cases like disambiguation or machine translation. By offering a linked data service, we hope to extend DBpedia’s central role in the LOD infrastructure to the world of Open Linguistics.
Towards Populating Generalizable Engineering Design Knowledge
Aiming to populate generalizable engineering design knowledge, we propose a
method to extract facts of the form head entity :: relationship :: tail entity
from sentences found in patent documents. These facts could be combined within
and across patent documents to form knowledge graphs that serve as schemes for
representing as well as storing design knowledge. Existing methods in
engineering design literature often utilise a set of predefined relationships
to populate triples that are statistical approximations rather than facts. In
our method, we train a tagger to identify both entities and relationships from
a sentence. Given a pair of entities thus identified, we train another tagger
to identify the relationship tokens that specifically denote the relationship
between the pair. For training these taggers, we manually construct a dataset
of 44,227 sentences and corresponding facts. We also compare the performance of
the method against typically recommended approaches, wherein, we predict the
edges among tokens by pairing the tokens independently and as part of a graph.
We apply our method to sentences found in patents related to fan systems and
build a domain knowledge base. Upon providing an overview of the knowledge
base, we search for solutions relevant to some key issues prevailing in fan
systems. We organize the responses into knowledge graphs and hold a comparative
discussion against the opinions from ChatGPT
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A narrative in three acts: Using combinations of image schemas to model events
Image schemas have been proposed as conceptual building blocks corresponding to the hypothesised most fundamental embodied experiences. We formally investigate how combinations of image schemas (or 'image schematic profiles') can model essential aspects of events, and discuss benefits for artificial intelligence and cognitive systems research, in particular concerning the role of such basic events in concept formation. More specifically, as exemplary illustrations and proof of concept the image schemas Object, Contact, and Path are combined to form the events Blockage, Bouncing, and Caused-Movement. Additionally, an outline of a proposed conceptual hierarchy of levels of modelling for image schemas and similar cognitive theories is given
The evolution of grounded spatial language
This book presents groundbreaking robotic experiments on how and why spatial language evolves. It provides detailed explanations of the origins of spatial conceptualization strategies, spatial categories, landmark systems and spatial grammar by tracing the interplay of environmental conditions, communicative and cognitive pressures. The experiments discussed in this book go far beyond previous approaches in grounded language evolution. For the first time, agents can evolve not only particular lexical systems but also evolve complex conceptualization strategies underlying the emergence of category systems and compositional semantics. Moreover, many issues in cognitive science, ranging from perception and conceptualization to language processing, had to be dealt with to instantiate these experiments, so that this book contributes not only to the study of language evolution but to the investigation of the cognitive bases of spatial language as well
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