基于语义构建个人知识网络相关技术研究
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摘要
本文主要从三个方面切入来对基于语义的构建个人知识网络过程中涉及到的技术进行分析和提出改进方法。分别为知识单元的发现，知识单元的搜索和排序，知识单元的推荐。
在知识单元的发现方面，我们提出动态关联主题模型来随着时间的变化来对知识单元进行分析，其模型将高维度的观察空间中的词组集映射到低维度的主题潜在空间来提高空间利用率的同时缩小了用户目标空间范围，分解为词组集合的知识单元来源于会议或者期刊论文集等知识库。主题和关系的动态由临时先验概率分布获得，以此来通过关系潜在空间构建层次结构，所有的变量包括词组、主题和相互关系在不同的时间段动态存在，我们提出的模型是非参数化的可以表现更为高效的收敛速度。动态关联主体模型对于发现特定主题中的词组概率分布并预测主题和相互关系的走势方面表现出色。降低的主题空间对提高知识单元的聚类也有很大帮助。
在知识单元的搜索和排序方面，我们提出基于语义上下文对知识单元的协作搜索，通过对语义上下文来进行概念标注用户本体和知识领域本体，并表明此方法可以明显提高搜索和排序的质量，并将用户的搜索习惯考虑在我们的方法中来构建用户与用户之间的语义关系和分析兴趣相似度，并提出根据用户的属性分为标记知识单元的用户和查询知识单元的用户来进行聚类，通过此来平衡处理时间和空间利用。
在知识单元的推荐方面，我们提出将领域知识本体集成到用户使用挖掘和推荐过程中，这样的集成方式增加了对领域知识细节的解释能力。领域本体结合用户提供的标签来提供top-n推荐，并且提出基于语义的序列模式挖掘算法，来减少执行时间和降低内存使用。提出包含语义信息的马尔科夫转换概率矩阵来对知识单元进行预测。
It has been seen that the rapid development or World Wide Web has broughtdramatic explosion of information. In the meanwhile, as the amount of informationgrows day by day which is mostly essential for highly effectively management ofinformation. As a matter of fact, by taking intelligent computational algorithms todiscover new and useful information and knowledge especially in the field ofinformation retrieval and data mining has been widely focused on and as a hot topic tobe deeply researched.
This thesis mainly concentrates on the issues that would occur in the process ofconstructing personal knowledge network that covers the discovery, searching,ranking and recommendation for knowledge based on specific domain. The source ofknowledge more derives from textual form of data, audio form of data, video form ordata where we more focus on textual form. The research issues we work on can beconcerned as text mining, also be including lots of interesting and more challengedproblems and applications which is one branch of information retrieval field.Generally, it means that discover useful patterns, structures and other valuableinformation from unconstructed natural language text. For knowledge discovery, topicdiscovery process is much more concerned about. After the emergence of latentsemantic analysis approach, topic analysis has become one popular hot spot byscholars in computer and statistics fields. The simple idea behind the topic analysis isto deal with the collections of topic instead of the ones of knowledge units. Each topiccontains the terms which form the uncertain possibility distribution. So we cantransform the dimension of large scale of collections of terms into lower dimension ofrepresentative collections of topics. Dynamic topic correlation model has been proposed to analyze the topics of knowledge units over time. The model is inspired byhierarchical Gaussian process latent variable model. It makes high dimensionality ofobserved space of terms to become lower dimension of latent space of topics. Theaforementioned condition is to suppose that there is no exchangeability between terms.And all variants exist dynamically at different time points. This non-parameter modelshows faster convergence rate than others. The posterior inference distributionbetween the topic and correlation exist in dynamic topic correlation model is helpfulfor discovering the dynamic changing between the frequency changing among termsin certain topic. And to predict the trend in the topic and the relations reside in whichlows the dimension of topic space and improves the classification performance ofknowledge units.
In personal knowledge network where users can build their own information base,build the relationship with each other and the store the personal preference intoindividual profile. Users go interaction with each other in a collaborative way in theknowledge network. Personal basic information, behaviour preference and coorelatedsimilar user information will be denoted as concepts. The reason of relations amongconcepts described by ontologies is for improving the semantic relation in users. Theontologies are in further divided into perosnal ontology and knowledge domainontology. We more refer to the current existing ontology base when it comes toknowledge domain ontology. Perosnal ontologies are constructed and annotatedmainly by knowledge experts and knowlege workers. As a result, the enrichingsemantic information of users have already been existed or derived. The informationcan be collaborated to improve the users’ online experience. We also need onecontext-aware data management mechanism to support user-centric data analysis. Wedenote the goal and challenge exist in collaborative knowledge network and proposecollaboratively searching based on knowledge which includes scores on knowledgeunits. The method of scoring comes from the collaborated knowledge network.Besides, we describe the top-k processing algorithm and consider how to balancebetween the query time and space using. The procedure we take is to apply the tag on knowledge units to make further improvement about the efficiency of searching andranking.
For the recommendation of knowledge units, we propose the semanticallyrecommended method which integrates the domain ontology and usage mining. Ithighly increases the efficiency of searching process and saving time of knowledgeunits in knowledge network. By modeling users' latent interests (mine users usagedaily log, calculate the how the portion that interested knowledge units take inknowledge units collection) and making recommendation for next target knowledgeunits which is for saving users' time. Semantic recommendation includes the semanticdistance combing semantic information to enrich the usage log. At the same time,semantic distance matrix works with transit possibility matrix coming from Markovmodel. The semantic sequence pattern mining combines with Markov model into theprocess of recommendation. At last, we propose the vector space model to constructknowledge units possibility and correlation matrix combines with the tags usersprovide to produce the top-n recommendation.
在知识单元的发现方面，我们提出动态关联主题模型来随着时间的变化来对知识单元进行分析，其模型将高维度的观察空间中的词组集映射到低维度的主题潜在空间来提高空间利用率的同时缩小了用户目标空间范围，分解为词组集合的知识单元来源于会议或者期刊论文集等知识库。主题和关系的动态由临时先验概率分布获得，以此来通过关系潜在空间构建层次结构，所有的变量包括词组、主题和相互关系在不同的时间段动态存在，我们提出的模型是非参数化的可以表现更为高效的收敛速度。动态关联主体模型对于发现特定主题中的词组概率分布并预测主题和相互关系的走势方面表现出色。降低的主题空间对提高知识单元的聚类也有很大帮助。
在知识单元的搜索和排序方面，我们提出基于语义上下文对知识单元的协作搜索，通过对语义上下文来进行概念标注用户本体和知识领域本体，并表明此方法可以明显提高搜索和排序的质量，并将用户的搜索习惯考虑在我们的方法中来构建用户与用户之间的语义关系和分析兴趣相似度，并提出根据用户的属性分为标记知识单元的用户和查询知识单元的用户来进行聚类，通过此来平衡处理时间和空间利用。
在知识单元的推荐方面，我们提出将领域知识本体集成到用户使用挖掘和推荐过程中，这样的集成方式增加了对领域知识细节的解释能力。领域本体结合用户提供的标签来提供top-n推荐，并且提出基于语义的序列模式挖掘算法，来减少执行时间和降低内存使用。提出包含语义信息的马尔科夫转换概率矩阵来对知识单元进行预测。
It has been seen that the rapid development or World Wide Web has broughtdramatic explosion of information. In the meanwhile, as the amount of informationgrows day by day which is mostly essential for highly effectively management ofinformation. As a matter of fact, by taking intelligent computational algorithms todiscover new and useful information and knowledge especially in the field ofinformation retrieval and data mining has been widely focused on and as a hot topic tobe deeply researched.
This thesis mainly concentrates on the issues that would occur in the process ofconstructing personal knowledge network that covers the discovery, searching,ranking and recommendation for knowledge based on specific domain. The source ofknowledge more derives from textual form of data, audio form of data, video form ordata where we more focus on textual form. The research issues we work on can beconcerned as text mining, also be including lots of interesting and more challengedproblems and applications which is one branch of information retrieval field.Generally, it means that discover useful patterns, structures and other valuableinformation from unconstructed natural language text. For knowledge discovery, topicdiscovery process is much more concerned about. After the emergence of latentsemantic analysis approach, topic analysis has become one popular hot spot byscholars in computer and statistics fields. The simple idea behind the topic analysis isto deal with the collections of topic instead of the ones of knowledge units. Each topiccontains the terms which form the uncertain possibility distribution. So we cantransform the dimension of large scale of collections of terms into lower dimension ofrepresentative collections of topics. Dynamic topic correlation model has been proposed to analyze the topics of knowledge units over time. The model is inspired byhierarchical Gaussian process latent variable model. It makes high dimensionality ofobserved space of terms to become lower dimension of latent space of topics. Theaforementioned condition is to suppose that there is no exchangeability between terms.And all variants exist dynamically at different time points. This non-parameter modelshows faster convergence rate than others. The posterior inference distributionbetween the topic and correlation exist in dynamic topic correlation model is helpfulfor discovering the dynamic changing between the frequency changing among termsin certain topic. And to predict the trend in the topic and the relations reside in whichlows the dimension of topic space and improves the classification performance ofknowledge units.
In personal knowledge network where users can build their own information base,build the relationship with each other and the store the personal preference intoindividual profile. Users go interaction with each other in a collaborative way in theknowledge network. Personal basic information, behaviour preference and coorelatedsimilar user information will be denoted as concepts. The reason of relations amongconcepts described by ontologies is for improving the semantic relation in users. Theontologies are in further divided into perosnal ontology and knowledge domainontology. We more refer to the current existing ontology base when it comes toknowledge domain ontology. Perosnal ontologies are constructed and annotatedmainly by knowledge experts and knowlege workers. As a result, the enrichingsemantic information of users have already been existed or derived. The informationcan be collaborated to improve the users’ online experience. We also need onecontext-aware data management mechanism to support user-centric data analysis. Wedenote the goal and challenge exist in collaborative knowledge network and proposecollaboratively searching based on knowledge which includes scores on knowledgeunits. The method of scoring comes from the collaborated knowledge network.Besides, we describe the top-k processing algorithm and consider how to balancebetween the query time and space using. The procedure we take is to apply the tag on knowledge units to make further improvement about the efficiency of searching andranking.
For the recommendation of knowledge units, we propose the semanticallyrecommended method which integrates the domain ontology and usage mining. Ithighly increases the efficiency of searching process and saving time of knowledgeunits in knowledge network. By modeling users' latent interests (mine users usagedaily log, calculate the how the portion that interested knowledge units take inknowledge units collection) and making recommendation for next target knowledgeunits which is for saving users' time. Semantic recommendation includes the semanticdistance combing semantic information to enrich the usage log. At the same time,semantic distance matrix works with transit possibility matrix coming from Markovmodel. The semantic sequence pattern mining combines with Markov model into theprocess of recommendation. At last, we propose the vector space model to constructknowledge units possibility and correlation matrix combines with the tags usersprovide to produce the top-n recommendation.
引文
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