随着信息技术的迅猛发展,大语言模型(Large Language Models,LLMs)在近年来取得了突破性进展,成为推动知识组织与应用领域变革的重要力量。大语言模型凭借其强大的自然语言处理能力,不仅在文本生成、语义理解、知识抽取等方面表现出色,...随着信息技术的迅猛发展,大语言模型(Large Language Models,LLMs)在近年来取得了突破性进展,成为推动知识组织与应用领域变革的重要力量。大语言模型凭借其强大的自然语言处理能力,不仅在文本生成、语义理解、知识抽取等方面表现出色,还为知识组织的自动化和智能化提供了新的可能性。这一技术的进步,极大地提升了知识管理的效率,推动了人机交互式知识组织的发展,为图书情报领域带来了前所未有的机遇与挑战。展开更多
To revise stratified web ontology language(OWL)ontologies,the kernel revision operator is extended by defining novel conflict stratification and the incision function based on integer linear programming(ILP).The ILP-b...To revise stratified web ontology language(OWL)ontologies,the kernel revision operator is extended by defining novel conflict stratification and the incision function based on integer linear programming(ILP).The ILP-based model considers an optimization problem of minimizing a linear objective function which is suitable for selecting the minimal number of axioms to remove when revising ontologies.Based on the incision function,a revision algorithm is proposed to apply ILP to all minimal incoherence-preserving subsets(MIPS).Although this algorithm can often find a minimal number of axioms to remove,it is very time-consuming to compute MIPS.Thus,an adapted revision algorithm to deal with unsatisfiable concepts individually is also given.Experimental results reveal that the proposed ILP-based revision algorithm is much more efficient than the commonly used algorithm based on the hitting set tree.In addition,the adapted algorithm can achieve higher efficiency,while it may delete more axioms.展开更多
Ontology diagnosis, a well-known approach for handling inconsistencies in a description logic （DL） based ontology, computes a diagnosis of the ontology, i.e., a minimal subset of axioms in the ontology whose removal...Ontology diagnosis, a well-known approach for handling inconsistencies in a description logic （DL） based ontology, computes a diagnosis of the ontology, i.e., a minimal subset of axioms in the ontology whose removal restores consistency. However, ontology diagnosis is computationally hard, especially computing a minimum cost diagnosis （MCD） which is a diagnosis such that the sum of the removal costs attached to its axioms is minimized. This paper addresses this problem by finding data tractable DLs for computing an MCD which allow computing an MCD in time polynomial in the size of the ABox of a given ontology. ABox decomposition is used to find a sufficient and necessary condition to identify data tractable DLs for computing an MCD under the unique name assumption （UNA） among all fragments of that are at least as expressive as without inverse roles. The most expressive, data tractable DL identified is without inverse roles or qualified existential restrictions.展开更多
Description logic programs （DLP） are an expressive but tractable subset of OWL. This paper ana-lyzes the important under-researched problem of learning DLP from uncertain data. Current studies have rarely explored t...Description logic programs （DLP） are an expressive but tractable subset of OWL. This paper ana-lyzes the important under-researched problem of learning DLP from uncertain data. Current studies have rarely explored the plentiful uncertain data populating the semantic web. This algorithm handles uncertain data in an inductive logic programming framework by modifying the performance evaluation criteria. A pseudo-log-likelihood based measure is used to evaluate the performance of different literals under uncer-tainties. Experiments on two datasets demonstrate that the approach is able to automatically learn a rule-set from uncertain data with acceptable accuracy.展开更多
文摘随着信息技术的迅猛发展,大语言模型(Large Language Models,LLMs)在近年来取得了突破性进展,成为推动知识组织与应用领域变革的重要力量。大语言模型凭借其强大的自然语言处理能力,不仅在文本生成、语义理解、知识抽取等方面表现出色,还为知识组织的自动化和智能化提供了新的可能性。这一技术的进步,极大地提升了知识管理的效率,推动了人机交互式知识组织的发展,为图书情报领域带来了前所未有的机遇与挑战。
基金The National Natural Science Foundation of China(No.61602259,U1736204)Research Foundation for Advanced Talents of Nanjing University of Posts and Telecommunications(No.NY216022)the National Key Research and Development Program of China(No.2018YFC0830200).
文摘To revise stratified web ontology language(OWL)ontologies,the kernel revision operator is extended by defining novel conflict stratification and the incision function based on integer linear programming(ILP).The ILP-based model considers an optimization problem of minimizing a linear objective function which is suitable for selecting the minimal number of axioms to remove when revising ontologies.Based on the incision function,a revision algorithm is proposed to apply ILP to all minimal incoherence-preserving subsets(MIPS).Although this algorithm can often find a minimal number of axioms to remove,it is very time-consuming to compute MIPS.Thus,an adapted revision algorithm to deal with unsatisfiable concepts individually is also given.Experimental results reveal that the proposed ILP-based revision algorithm is much more efficient than the commonly used algorithm based on the hitting set tree.In addition,the adapted algorithm can achieve higher efficiency,while it may delete more axioms.
基金Supported by the National Natural Science Foundation of China(Nos.61005043 and 60970045)
文摘Ontology diagnosis, a well-known approach for handling inconsistencies in a description logic （DL） based ontology, computes a diagnosis of the ontology, i.e., a minimal subset of axioms in the ontology whose removal restores consistency. However, ontology diagnosis is computationally hard, especially computing a minimum cost diagnosis （MCD） which is a diagnosis such that the sum of the removal costs attached to its axioms is minimized. This paper addresses this problem by finding data tractable DLs for computing an MCD which allow computing an MCD in time polynomial in the size of the ABox of a given ontology. ABox decomposition is used to find a sufficient and necessary condition to identify data tractable DLs for computing an MCD under the unique name assumption （UNA） among all fragments of that are at least as expressive as without inverse roles. The most expressive, data tractable DL identified is without inverse roles or qualified existential restrictions.
基金Supported by the National Natural Science Foundation of China(Nos.60773107,60873153,and 60803061)
文摘Description logic programs （DLP） are an expressive but tractable subset of OWL. This paper ana-lyzes the important under-researched problem of learning DLP from uncertain data. Current studies have rarely explored the plentiful uncertain data populating the semantic web. This algorithm handles uncertain data in an inductive logic programming framework by modifying the performance evaluation criteria. A pseudo-log-likelihood based measure is used to evaluate the performance of different literals under uncer-tainties. Experiments on two datasets demonstrate that the approach is able to automatically learn a rule-set from uncertain data with acceptable accuracy.
