With this work, we introduce a novel method for the unsupervised learning of conceptual hierarchies, or concept maps as they are sometimes called, which is aimed specifically for use with literary texts, as such disti...With this work, we introduce a novel method for the unsupervised learning of conceptual hierarchies, or concept maps as they are sometimes called, which is aimed specifically for use with literary texts, as such distinguishing itself from the majority of research literature on the topic which is primarily focused on building ontologies from a vast array of different types of data sources, both structured and unstructured, to support various forms of AI, in particular, the Semantic Web as envisioned by Tim Berners-Lee. We first elaborate on mutually informing disciplines of philosophy and computer science, or more specifically the relationship between metaphysics, epistemology, ontology, computing and AI, followed by a technically in-depth discussion of DEBRA, our dependency tree based concept hierarchy constructor, which as its name alludes to, constructs a conceptual map in the form of a directed graph which illustrates the concepts, their respective relations, and the implied ontological structure of the concepts as encoded in the text, decoded with standard Python NLP libraries such as spaCy and NLTK. With this work we hope to both augment the Knowledge Representation literature with opportunities for intellectual advancement in AI with more intuitive, less analytical, and well-known forms of knowledge representation from the cognitive science community, as well as open up new areas of research between Computer Science and the Humanities with respect to the application of the latest in NLP tools and techniques upon literature of cultural significance, shedding light on existing methods of computation with respect to documents in semantic space that effectively allows for, at the very least, the comparison and evolution of texts through time, using vector space math.展开更多
In order to reduce the costs of the ontology construction, a general ontology learning framework （GOLF） is developed. The key technologies of the GOLF including domain concepts extraction and semantic relationships ...In order to reduce the costs of the ontology construction, a general ontology learning framework （GOLF） is developed. The key technologies of the GOLF including domain concepts extraction and semantic relationships between concepts and taxonomy automatic construction are proposed. At the same time ontology evaluation methods are also discussed. The experimental results show that this method produces better performance and it is applicable across different domains. By integrating several machine learning algorithms, this method suffers less ambiguity and can identify domain concepts and relations more accurately. By using generalized corpus WordNet and HowNet, this method is applicable across different domains. In addition, by obtaining source documents from the web on demand, the GOLF can produce up-to-date ontologies.展开更多
In order to construct Chinese ontology easily, an automated ontology learning technology for Chinese documents based on singular value decomposition (SVD) and conceptual clustering is proposed . First the system extra...In order to construct Chinese ontology easily, an automated ontology learning technology for Chinese documents based on singular value decomposition (SVD) and conceptual clustering is proposed . First the system extracts concepts from a set of domain-specific documents by using SVD technology, and then acquires subsumption relationships between the concepts by means of hierarchical conceptual clustering method. The system thus yields domain-related concept hierarchy.展开更多
Ontologies of research areas are important tools for characterizing,exploring,and analyzing the research landscape.Some fields of research are comprehensively described by large-scale taxonomies,e.g.,MeSH in Biology a...Ontologies of research areas are important tools for characterizing,exploring,and analyzing the research landscape.Some fields of research are comprehensively described by large-scale taxonomies,e.g.,MeSH in Biology and PhySH in Physics.Conversely,current Computer Science taxonomies are coarse-grained and tend to evolve slowly.For instance,the ACM classification scheme contains only about 2K research topics and the last version dates back to 2012.In this paper,we introduce the Computer Science Ontology(CSO),a large-scale,automatically generated ontology of research areas,which includes about 14K topics and 162K semantic relationships.It was created by applying the Klink-2 algorithm on a very large data set of 16M scientific articles.CSO presents two main advantages over the alternatives:i)it includes a very large number of topics that do not appear in other classifications,and ii)it can be updated automatically by running Klink-2 on recent corpora of publications.CSO powers several tools adopted by the editorial team at Springer Nature and has been used to enable a variety of solutions,such as classifying research publications,detecting research communities,and predicting research trends.To facilitate the uptake of CSO,we have also released the CSO Classifier,a tool for automatically classifying research papers,and the CSO Portal,a Web application that enables users to download,explore,and provide granular feedback on CSO.Users can use the portal to navigate and visualize sections of the ontology,rate topics and relationships,and suggest missing ones.The portal will support the publication of and access to regular new releases of CSO,with the aim of providing a comprehensive resource to the various research communities engaged with scholarly data.展开更多
文摘With this work, we introduce a novel method for the unsupervised learning of conceptual hierarchies, or concept maps as they are sometimes called, which is aimed specifically for use with literary texts, as such distinguishing itself from the majority of research literature on the topic which is primarily focused on building ontologies from a vast array of different types of data sources, both structured and unstructured, to support various forms of AI, in particular, the Semantic Web as envisioned by Tim Berners-Lee. We first elaborate on mutually informing disciplines of philosophy and computer science, or more specifically the relationship between metaphysics, epistemology, ontology, computing and AI, followed by a technically in-depth discussion of DEBRA, our dependency tree based concept hierarchy constructor, which as its name alludes to, constructs a conceptual map in the form of a directed graph which illustrates the concepts, their respective relations, and the implied ontological structure of the concepts as encoded in the text, decoded with standard Python NLP libraries such as spaCy and NLTK. With this work we hope to both augment the Knowledge Representation literature with opportunities for intellectual advancement in AI with more intuitive, less analytical, and well-known forms of knowledge representation from the cognitive science community, as well as open up new areas of research between Computer Science and the Humanities with respect to the application of the latest in NLP tools and techniques upon literature of cultural significance, shedding light on existing methods of computation with respect to documents in semantic space that effectively allows for, at the very least, the comparison and evolution of texts through time, using vector space math.
基金The National Basic Research Program of China(973Program)(No.2003CB317000),the Natural Science Foundation of Zhejiang Province (No.Y105625).
文摘In order to reduce the costs of the ontology construction, a general ontology learning framework （GOLF） is developed. The key technologies of the GOLF including domain concepts extraction and semantic relationships between concepts and taxonomy automatic construction are proposed. At the same time ontology evaluation methods are also discussed. The experimental results show that this method produces better performance and it is applicable across different domains. By integrating several machine learning algorithms, this method suffers less ambiguity and can identify domain concepts and relations more accurately. By using generalized corpus WordNet and HowNet, this method is applicable across different domains. In addition, by obtaining source documents from the web on demand, the GOLF can produce up-to-date ontologies.
文摘In order to construct Chinese ontology easily, an automated ontology learning technology for Chinese documents based on singular value decomposition (SVD) and conceptual clustering is proposed . First the system extracts concepts from a set of domain-specific documents by using SVD technology, and then acquires subsumption relationships between the concepts by means of hierarchical conceptual clustering method. The system thus yields domain-related concept hierarchy.
文摘Ontologies of research areas are important tools for characterizing,exploring,and analyzing the research landscape.Some fields of research are comprehensively described by large-scale taxonomies,e.g.,MeSH in Biology and PhySH in Physics.Conversely,current Computer Science taxonomies are coarse-grained and tend to evolve slowly.For instance,the ACM classification scheme contains only about 2K research topics and the last version dates back to 2012.In this paper,we introduce the Computer Science Ontology(CSO),a large-scale,automatically generated ontology of research areas,which includes about 14K topics and 162K semantic relationships.It was created by applying the Klink-2 algorithm on a very large data set of 16M scientific articles.CSO presents two main advantages over the alternatives:i)it includes a very large number of topics that do not appear in other classifications,and ii)it can be updated automatically by running Klink-2 on recent corpora of publications.CSO powers several tools adopted by the editorial team at Springer Nature and has been used to enable a variety of solutions,such as classifying research publications,detecting research communities,and predicting research trends.To facilitate the uptake of CSO,we have also released the CSO Classifier,a tool for automatically classifying research papers,and the CSO Portal,a Web application that enables users to download,explore,and provide granular feedback on CSO.Users can use the portal to navigate and visualize sections of the ontology,rate topics and relationships,and suggest missing ones.The portal will support the publication of and access to regular new releases of CSO,with the aim of providing a comprehensive resource to the various research communities engaged with scholarly data.
