In order to let software programs gain full benefit from semi-structured Web sources, wrapper programs must be built to provide a "machine readable" view over them. A significant problem of this approach is that, since Web sources are autonomous, they may experience changes that invalidate the current wrapper. In this paper, we address this problem by introducing novel heuristics and algorithms for automatically maintaining wrappers. In our approach, the system collects some query results during normal wrapper operation and, when the source changes, it uses them as input to generate a set of labeled examples for the source which can then be used to induce a new wrapper. Our experiments show that the proposed techniques show high accuracy for a wide range of real world Web data extraction problems.
{"title":"Automatically generating labeled examples for Web wrapper maintenance","authors":"J. Raposo, A. Pan, M. Álvarez, Justo Hidalgo","doi":"10.1109/WI.2005.40","DOIUrl":"https://doi.org/10.1109/WI.2005.40","url":null,"abstract":"In order to let software programs gain full benefit from semi-structured Web sources, wrapper programs must be built to provide a \"machine readable\" view over them. A significant problem of this approach is that, since Web sources are autonomous, they may experience changes that invalidate the current wrapper. In this paper, we address this problem by introducing novel heuristics and algorithms for automatically maintaining wrappers. In our approach, the system collects some query results during normal wrapper operation and, when the source changes, it uses them as input to generate a set of labeled examples for the source which can then be used to induce a new wrapper. Our experiments show that the proposed techniques show high accuracy for a wide range of real world Web data extraction problems.","PeriodicalId":213856,"journal":{"name":"The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05)","volume":"08 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124448335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Schema matching plays a key role in data integration, data warehouse and e-business. This paper introduces a schema matching method SMDD based on neural network. By analyzing the characteristics of data distribution, it automatically fulfills the task of schema matching. It can be used independently or as a supplement of other schema matching methods. SMDD can improve the accuracy of schema matching from the point of view of data contents.
{"title":"Schema matching using neural network","authors":"You Li, Dongbo Liu, Weiming Zhang","doi":"10.1109/WI.2005.129","DOIUrl":"https://doi.org/10.1109/WI.2005.129","url":null,"abstract":"Schema matching plays a key role in data integration, data warehouse and e-business. This paper introduces a schema matching method SMDD based on neural network. By analyzing the characteristics of data distribution, it automatically fulfills the task of schema matching. It can be used independently or as a supplement of other schema matching methods. SMDD can improve the accuracy of schema matching from the point of view of data contents.","PeriodicalId":213856,"journal":{"name":"The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05)","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121930629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Intrusion detection on the Internet is a heated research field in computer science, where much work has been done during the past two decades. In this paper, we build a network-based intrusion detection system using Adaboost, a prevailing machine learning algorithm. The experiments demonstrate that our system can achieve an especially low false positive rate while keeping a preferable detection rate, and its computational complexity is extremely low, which is a very attractive property in practice.
{"title":"Network-based intrusion detection using Adaboost algorithm","authors":"Wei Hu, Weiming Hu","doi":"10.1109/WI.2005.107","DOIUrl":"https://doi.org/10.1109/WI.2005.107","url":null,"abstract":"Intrusion detection on the Internet is a heated research field in computer science, where much work has been done during the past two decades. In this paper, we build a network-based intrusion detection system using Adaboost, a prevailing machine learning algorithm. The experiments demonstrate that our system can achieve an especially low false positive rate while keeping a preferable detection rate, and its computational complexity is extremely low, which is a very attractive property in practice.","PeriodicalId":213856,"journal":{"name":"The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123472910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We put forward a hypothesis that there exist common meaningful structures among ontologies whose domains are analogous to each other The initial motivation of our hypothesis is to make full use of the structural information in existing ontologies, in order to benefit the domain of ontology. To verify the hypothesis we give a precise definition of the candidate of the common meaningful structure called MICISO (maximum isomorphic common induced sub-ontology). Based on the hypothesis and the definition we present a novel data mining problem called MICISO mining, whose aim is learning from ontologies to find out MICISOs and further recommend the common meaningful structures. We also provide an algorithm for MICISO mining, based on which we have developed a practical tool for mining and checking such structures. With the tool, the algorithm is implemented with quite a few pairs of existing ontologies, and the interesting meaningful results support our hypothesis. Thus we consider that the hypothesis is preliminarily verified. We suppose that our work sparks a novel promising thinking for the domain of ontology -to study existing ontologies for useful things.
我们提出了领域相似的本体之间存在共同意义结构的假设，提出这一假设的初衷是为了充分利用现有本体中的结构信息，从而使本体领域受益。为了验证这一假设，我们给出了MICISO (maximum isomorphic common induced sub-ontology，最大同构公共诱导子本体)候选对象的精确定义。在此假设和定义的基础上，我们提出了一种新的数据挖掘问题，称为MICISO挖掘，其目的是从本体中学习，发现MICISO，并进一步推荐共同的有意义结构。我们还提供了一种MICISO挖掘算法，并在此基础上开发了一种实用的挖掘和检查此类结构的工具。使用该工具，该算法使用了相当多对现有本体来实现，并且有趣的有意义的结果支持了我们的假设。因此，我们认为该假设得到了初步验证。我们认为我们的工作为本体论领域激发了一种新的有前途的思维——研究现有的本体论以寻找有用的东西。
{"title":"Learning from ontologies for common meaningful structures","authors":"Liu Yang, Guojie Li, Zhongzhi Shi","doi":"10.1109/WI.2005.90","DOIUrl":"https://doi.org/10.1109/WI.2005.90","url":null,"abstract":"We put forward a hypothesis that there exist common meaningful structures among ontologies whose domains are analogous to each other The initial motivation of our hypothesis is to make full use of the structural information in existing ontologies, in order to benefit the domain of ontology. To verify the hypothesis we give a precise definition of the candidate of the common meaningful structure called MICISO (maximum isomorphic common induced sub-ontology). Based on the hypothesis and the definition we present a novel data mining problem called MICISO mining, whose aim is learning from ontologies to find out MICISOs and further recommend the common meaningful structures. We also provide an algorithm for MICISO mining, based on which we have developed a practical tool for mining and checking such structures. With the tool, the algorithm is implemented with quite a few pairs of existing ontologies, and the interesting meaningful results support our hypothesis. Thus we consider that the hypothesis is preliminarily verified. We suppose that our work sparks a novel promising thinking for the domain of ontology -to study existing ontologies for useful things.","PeriodicalId":213856,"journal":{"name":"The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129888337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper addresses the distributed resource optimization issue in heterogeneous Web environments, where both resource nodes and service requests may be heterogeneous. Specifically, this paper presents an agent-based mechanism, where agents are employed to carry service requests. Agents are equipped with three behaviors, namely, least-loaded move, less-loaded move, and random move, to search for appropriate resource nodes. Every time, agents probabilistically choose a behavior to perform. As a whole, the multiagent system can accomplish the objective of load balancing and resource optimization. Through experiments on a computing platform, called SSADRO, we validate the effectiveness of the proposed mechanism. As compared to our previously proposed load balancing mechanism in Liu, Jin and Wang, (2005), the one in this paper can address dynamic load balancing in heterogeneous environments.
{"title":"Resource optimization in heterogeneous Web environments","authors":"Xiaolong Jin, Jiming Liu","doi":"10.1109/WI.2005.128","DOIUrl":"https://doi.org/10.1109/WI.2005.128","url":null,"abstract":"This paper addresses the distributed resource optimization issue in heterogeneous Web environments, where both resource nodes and service requests may be heterogeneous. Specifically, this paper presents an agent-based mechanism, where agents are employed to carry service requests. Agents are equipped with three behaviors, namely, least-loaded move, less-loaded move, and random move, to search for appropriate resource nodes. Every time, agents probabilistically choose a behavior to perform. As a whole, the multiagent system can accomplish the objective of load balancing and resource optimization. Through experiments on a computing platform, called SSADRO, we validate the effectiveness of the proposed mechanism. As compared to our previously proposed load balancing mechanism in Liu, Jin and Wang, (2005), the one in this paper can address dynamic load balancing in heterogeneous environments.","PeriodicalId":213856,"journal":{"name":"The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130964294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The quality of discovery patterns is crucial for building satisfactory systems of Web text mining. It is no doubt that we can find numerous frequent patterns from Web documents. However, there are many meaningless frequent patterns. This paper presents a novel method to improve the quality of discovered patterns. It generalizes discovered patterns into interesting topics in order to acquire the necessary useful information. The experimental results also verify the proposed method is promising.
{"title":"Mining interesting topics for Web information gathering and Web personalization","authors":"Yuefeng Li, Ben Murphy, N. Zhong","doi":"10.1109/WI.2005.98","DOIUrl":"https://doi.org/10.1109/WI.2005.98","url":null,"abstract":"The quality of discovery patterns is crucial for building satisfactory systems of Web text mining. It is no doubt that we can find numerous frequent patterns from Web documents. However, there are many meaningless frequent patterns. This paper presents a novel method to improve the quality of discovered patterns. It generalizes discovered patterns into interesting topics in order to acquire the necessary useful information. The experimental results also verify the proposed method is promising.","PeriodicalId":213856,"journal":{"name":"The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132182041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Domain ontology can help in information retrieval from documents. But ontology is a pre-defined structure with crisp concept descriptions and inter-concept relations. However, due to the dynamic nature of the document repository, ontology should be upgradeable with information extracted through text mining of documents in the domain. This also necessitates that concepts, their descriptions and inter-concept relations should be associated with a degree of fuzziness that will indicate the support for the extracted knowledge according to the currently available resources. Supports may be revised with more knowledge coming in future. This approach preserves the basic structured knowledge format for storing domain knowledge, but at the same time allows for update of information. In this paper, we have proposed a mechanism which initiates text mining with a set of ontological concepts, and thereafter extracts fuzzy relations through text mining. Membership values of relations are functions of frequency of co-occurrence of concepts and relations. We have worked on the GENIA corpus and shown how fuzzy relations can be further used for guided information extraction from MEDLINE documents.
{"title":"Biological ontology enhancement with fuzzy relations: a text-mining framework","authors":"M. Abulaish, Lipika Dey","doi":"10.1109/WI.2005.43","DOIUrl":"https://doi.org/10.1109/WI.2005.43","url":null,"abstract":"Domain ontology can help in information retrieval from documents. But ontology is a pre-defined structure with crisp concept descriptions and inter-concept relations. However, due to the dynamic nature of the document repository, ontology should be upgradeable with information extracted through text mining of documents in the domain. This also necessitates that concepts, their descriptions and inter-concept relations should be associated with a degree of fuzziness that will indicate the support for the extracted knowledge according to the currently available resources. Supports may be revised with more knowledge coming in future. This approach preserves the basic structured knowledge format for storing domain knowledge, but at the same time allows for update of information. In this paper, we have proposed a mechanism which initiates text mining with a set of ontological concepts, and thereafter extracts fuzzy relations through text mining. Membership values of relations are functions of frequency of co-occurrence of concepts and relations. We have worked on the GENIA corpus and shown how fuzzy relations can be further used for guided information extraction from MEDLINE documents.","PeriodicalId":213856,"journal":{"name":"The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133414859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The most well known search techniques are perhaps the PageRank and HITS algorithms. In this paper, we argue that these algorithms miss an important dimension, the temporal dimension. Quality pages in the past may not be quality pages now or in the future. These techniques favor older pages because these pages have many in-links accumulated over time. New pages, which may be of high quality, have few or no in-links and are left behind. Research publication search has the same problem. If we use the PageRank or HITS algorithm, those older or classic papers are ranked high due to the large number of citations that they received in the past. This paper studies the temporal dimension of search in the context of research publication. A number of methods are proposed to deal with the problem based on analyzing the behavior history and the source of each publication. These methods are evaluated empirically. Our results show that they are highly effective.
{"title":"Adding the temporal dimension to search - a case study in publication search","authors":"Philip S. Yu, Xin Li, B. Liu","doi":"10.1109/WI.2005.21","DOIUrl":"https://doi.org/10.1109/WI.2005.21","url":null,"abstract":"The most well known search techniques are perhaps the PageRank and HITS algorithms. In this paper, we argue that these algorithms miss an important dimension, the temporal dimension. Quality pages in the past may not be quality pages now or in the future. These techniques favor older pages because these pages have many in-links accumulated over time. New pages, which may be of high quality, have few or no in-links and are left behind. Research publication search has the same problem. If we use the PageRank or HITS algorithm, those older or classic papers are ranked high due to the large number of citations that they received in the past. This paper studies the temporal dimension of search in the context of research publication. A number of methods are proposed to deal with the problem based on analyzing the behavior history and the source of each publication. These methods are evaluated empirically. Our results show that they are highly effective.","PeriodicalId":213856,"journal":{"name":"The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130105144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Stolz, Maximilian Viermetz, Michal Skubacz, R. Neuneier
For the evaluation of Web sites, a multitude of metrics are available. Apart from general statistical measures, success metrics reflect the degree to which a Web site achieves its defined objectives. Particularly metrics for e-commerce sites based on transaction analysis are commonly available and well understood. In contrast to transaction based sites, the success of Web sites geared toward information delivery is harder to quantify since there is no direct feedback of user intent. User feedback is only directly available on transactional Web sites. We introduce a metric to measure the success of an information driven Web site in meeting its objective to deliver the desired information in a timely and usable fashion. We propose to assign a value to each click based on the type of transition, duration and semantic distance. These values are then combined into a scoring model describing the success of a Web site in meeting its objectives. The resulting metric is introduced as the GPI and its applicability shown on a large corporate Web site.
{"title":"Guidance performance indicator - Web metrics for information driven Web sites","authors":"C. Stolz, Maximilian Viermetz, Michal Skubacz, R. Neuneier","doi":"10.1109/WI.2005.69","DOIUrl":"https://doi.org/10.1109/WI.2005.69","url":null,"abstract":"For the evaluation of Web sites, a multitude of metrics are available. Apart from general statistical measures, success metrics reflect the degree to which a Web site achieves its defined objectives. Particularly metrics for e-commerce sites based on transaction analysis are commonly available and well understood. In contrast to transaction based sites, the success of Web sites geared toward information delivery is harder to quantify since there is no direct feedback of user intent. User feedback is only directly available on transactional Web sites. We introduce a metric to measure the success of an information driven Web site in meeting its objective to deliver the desired information in a timely and usable fashion. We propose to assign a value to each click based on the type of transition, duration and semantic distance. These values are then combined into a scoring model describing the success of a Web site in meeting its objectives. The resulting metric is introduced as the GPI and its applicability shown on a large corporate Web site.","PeriodicalId":213856,"journal":{"name":"The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05)","volume":"124 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114017740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a weighted-binary-sequential method to predict the status of customer patronage for the next day. Most of the research using association rules to mine sequential data focus on the algorithms and computing efficiency of pattern or rule generation. But few of them consider the time value of the sequential data. It is desirable to weight recent observations more heavily than remote observations in the analysis of time-series data. In this paper, we address a time-weighted concept on association algorithm to mine the binary-time-series data. The weighted binary sequence algorithm gives more weight on the recent data in finding the longest frequent patterns from binary-time-series data. There are two weighting methods; dynamic-length weighting and fixed-length weighting. Both algorithms are compared to the un-weighted algorithm to show how time value influences the prediction accuracy. Some performance results with a real-life Web site application given in this paper show that time-weighted sequential algorithms are generally superior to un-weighted sequential algorithm.
{"title":"Binary prediction based on weighted sequential mining method","authors":"Shuchuan Lo","doi":"10.1109/WI.2005.42","DOIUrl":"https://doi.org/10.1109/WI.2005.42","url":null,"abstract":"This paper presents a weighted-binary-sequential method to predict the status of customer patronage for the next day. Most of the research using association rules to mine sequential data focus on the algorithms and computing efficiency of pattern or rule generation. But few of them consider the time value of the sequential data. It is desirable to weight recent observations more heavily than remote observations in the analysis of time-series data. In this paper, we address a time-weighted concept on association algorithm to mine the binary-time-series data. The weighted binary sequence algorithm gives more weight on the recent data in finding the longest frequent patterns from binary-time-series data. There are two weighting methods; dynamic-length weighting and fixed-length weighting. Both algorithms are compared to the un-weighted algorithm to show how time value influences the prediction accuracy. Some performance results with a real-life Web site application given in this paper show that time-weighted sequential algorithms are generally superior to un-weighted sequential algorithm.","PeriodicalId":213856,"journal":{"name":"The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114217741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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