It is important to help researchers find valuable scientific papers from a large literature collection containing information of authors, papers and venues. Graph-based algorithms have been proposed to rank papers based on networks formed by citation and co-author relationships. This paper proposes a new graph-based ranking framework MutualRank that integrates mutual reinforcement relationships among networks of papers, researchers and venues to achieve a more synthetic, accurate and fair ranking result than previous graph-based methods. MutualRank leverages the network structure information among papers, authors, and their venues available from a literature collection dataset and sets up a unified mutual reinforcement model that involves both intra- and inter-network information for ranking papers, authors and venues simultaneously. To evaluate, we collect a set of recommended papers from websites of graduate-level computational linguistics courses of 15 top universities as the benchmark and apply different methods to estimate paper importance. The results show that MutualRank greatly outperforms the competitors including Pag-eRank, HITS and CoRank in ranking papers as well as researchers. The experimental results also demonstrate that venues ranked by MutualRank are reasonable.
{"title":"Towards an effective and unbiased ranking of scientific literature through mutual reinforcement","authors":"Xiaorui Jiang, Xiaoping Sun, H. Zhuge","doi":"10.1145/2396761.2396853","DOIUrl":"https://doi.org/10.1145/2396761.2396853","url":null,"abstract":"It is important to help researchers find valuable scientific papers from a large literature collection containing information of authors, papers and venues. Graph-based algorithms have been proposed to rank papers based on networks formed by citation and co-author relationships. This paper proposes a new graph-based ranking framework MutualRank that integrates mutual reinforcement relationships among networks of papers, researchers and venues to achieve a more synthetic, accurate and fair ranking result than previous graph-based methods. MutualRank leverages the network structure information among papers, authors, and their venues available from a literature collection dataset and sets up a unified mutual reinforcement model that involves both intra- and inter-network information for ranking papers, authors and venues simultaneously. To evaluate, we collect a set of recommended papers from websites of graduate-level computational linguistics courses of 15 top universities as the benchmark and apply different methods to estimate paper importance. The results show that MutualRank greatly outperforms the competitors including Pag-eRank, HITS and CoRank in ranking papers as well as researchers. The experimental results also demonstrate that venues ranked by MutualRank are reasonable.","PeriodicalId":313414,"journal":{"name":"Proceedings of the 21st ACM international conference on Information and knowledge management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128898838","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}
Location-based social networks (LBSNs) have attracted an increasing number of users in recent years. The availability of geographical and social information of online LBSNs provides an unprecedented opportunity to study the human movement from their socio-spatial behavior, enabling a variety of location-based services. Previous work on LBSNs reported limited improvements from using the social network information for location prediction; as users can check-in at new places, traditional work on location prediction that relies on mining a user's historical trajectories is not designed for this "cold start" problem of predicting new check-ins. In this paper, we propose to utilize the social network information for solving the "cold start" location prediction problem, with a geo-social correlation model to capture social correlations on LBSNs considering social networks and geographical distance. The experimental results on a real-world LBSN demonstrate that our approach properly models the social correlations of a user's new check-ins by considering various correlation strengths and correlation measures.
{"title":"gSCorr: modeling geo-social correlations for new check-ins on location-based social networks","authors":"Huiji Gao, Jiliang Tang, Huan Liu","doi":"10.1145/2396761.2398477","DOIUrl":"https://doi.org/10.1145/2396761.2398477","url":null,"abstract":"Location-based social networks (LBSNs) have attracted an increasing number of users in recent years. The availability of geographical and social information of online LBSNs provides an unprecedented opportunity to study the human movement from their socio-spatial behavior, enabling a variety of location-based services. Previous work on LBSNs reported limited improvements from using the social network information for location prediction; as users can check-in at new places, traditional work on location prediction that relies on mining a user's historical trajectories is not designed for this \"cold start\" problem of predicting new check-ins. In this paper, we propose to utilize the social network information for solving the \"cold start\" location prediction problem, with a geo-social correlation model to capture social correlations on LBSNs considering social networks and geographical distance. The experimental results on a real-world LBSN demonstrate that our approach properly models the social correlations of a user's new check-ins by considering various correlation strengths and correlation measures.","PeriodicalId":313414,"journal":{"name":"Proceedings of the 21st ACM international conference on Information and knowledge management","volume":"151 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134475644","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}
To integrate Linked Open Data, which originates from various and heterogeneous sources, the use of well-defined ontologies is essential. However, oftentimes the utilization of these ontologies by data publishers differs from the intended application envisioned by ontology engineers. This may lead to unspecified properties being used ad-hoc as predicates in RDF triples or it may result in infrequent usage of specified properties. These mismatches impede the goals and propagation of the Web of Data as data consumers face difficulties when trying to discover and integrate domain-specific information. In this work, we identify and classify common misusage patterns by employing frequency analysis and rule mining. Based on this analysis, we introduce an algorithm to propose suggestions for a data-driven ontology re-engineering workflow, which we evaluate on two large-scale RDF datasets.
{"title":"Reconciling ontologies and the web of data","authors":"Ziawasch Abedjan, Johannes Lorey, Felix Naumann","doi":"10.1145/2396761.2398467","DOIUrl":"https://doi.org/10.1145/2396761.2398467","url":null,"abstract":"To integrate Linked Open Data, which originates from various and heterogeneous sources, the use of well-defined ontologies is essential. However, oftentimes the utilization of these ontologies by data publishers differs from the intended application envisioned by ontology engineers. This may lead to unspecified properties being used ad-hoc as predicates in RDF triples or it may result in infrequent usage of specified properties. These mismatches impede the goals and propagation of the Web of Data as data consumers face difficulties when trying to discover and integrate domain-specific information. In this work, we identify and classify common misusage patterns by employing frequency analysis and rule mining. Based on this analysis, we introduce an algorithm to propose suggestions for a data-driven ontology re-engineering workflow, which we evaluate on two large-scale RDF datasets.","PeriodicalId":313414,"journal":{"name":"Proceedings of the 21st ACM international conference on Information and knowledge management","volume":"294 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115327174","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}
Finding reverse nearest neighbors (RNNs) is an important operation in spatial databases. The problem of evaluating RNN queries has already received considerable attention due to its importance in many real-world applications, such as resource allocation and disaster response. While RNN query processing has been extensively studied in Euclidean space, no work ever studies this problem on land surfaces. However, practical applications of RNN queries involve terrain surfaces that constrain object movements, which rendering the existing algorithms inapplicable. In this paper, we investigate the evaluation of two types of RNN queries on land surfaces: monochromatic RNN (MRNN) queries and bichromatic RNN (BRNN) queries. On a land surface, the distance between two points is calculated as the length of the shortest path along the surface. However, the computational cost of the state-of-the-art shortest path algorithm on a land surface is quadratic to the size of the surface model, which is usually quite huge. As a result, surface RNN query processing is a challenging problem. Leveraging some newly-discovered properties of Voronoi cell approximation structures, we make use of standard index structures such as an R-tree to design efficient algorithms that accelerate the evaluation of MRNN and BRNN queries on land surfaces. Our proposed algorithms are able to localize query evaluation by accessing just a small fraction of the surface data near the query point, which helps avoid shortest path evaluation on a large surface. Extensive experiments are conducted on large real-world datasets to demonstrate the efficiency of our algorithms.
{"title":"Monochromatic and bichromatic reverse nearest neighbor queries on land surfaces","authors":"D. Yan, Zhou Zhao, Wilfred Ng","doi":"10.1145/2396761.2396880","DOIUrl":"https://doi.org/10.1145/2396761.2396880","url":null,"abstract":"Finding reverse nearest neighbors (RNNs) is an important operation in spatial databases. The problem of evaluating RNN queries has already received considerable attention due to its importance in many real-world applications, such as resource allocation and disaster response. While RNN query processing has been extensively studied in Euclidean space, no work ever studies this problem on land surfaces. However, practical applications of RNN queries involve terrain surfaces that constrain object movements, which rendering the existing algorithms inapplicable. In this paper, we investigate the evaluation of two types of RNN queries on land surfaces: monochromatic RNN (MRNN) queries and bichromatic RNN (BRNN) queries. On a land surface, the distance between two points is calculated as the length of the shortest path along the surface. However, the computational cost of the state-of-the-art shortest path algorithm on a land surface is quadratic to the size of the surface model, which is usually quite huge. As a result, surface RNN query processing is a challenging problem. Leveraging some newly-discovered properties of Voronoi cell approximation structures, we make use of standard index structures such as an R-tree to design efficient algorithms that accelerate the evaluation of MRNN and BRNN queries on land surfaces. Our proposed algorithms are able to localize query evaluation by accessing just a small fraction of the surface data near the query point, which helps avoid shortest path evaluation on a large surface. Extensive experiments are conducted on large real-world datasets to demonstrate the efficiency of our algorithms.","PeriodicalId":313414,"journal":{"name":"Proceedings of the 21st ACM international conference on Information and knowledge management","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115422426","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}
Anastasios Arvanitis, Antonios Deligiannakis, Y. Vassiliou
The rapid growth of social web has contributed vast amounts of user preference data. Analyzing this data and its relationships with products could have several practical applications, such as personalized advertising, market segmentation, product feature promotion etc. In this work we develop novel algorithms for efficiently processing two important classes of queries involving user preferences, i.e. potential customers identification and product positioning. With regards to the first problem, we formulate product attractiveness based on the notion of reverse skyline queries. We then present a new algorithm, termed as RSA, that significantly reduces the I/O cost, as well as the computation cost, when compared to the state-of-the-art reverse skyline algorithm, while at the same time being able to quickly report the first results. Several real-world applications require processing of a large number of queries, in order to identify the product characteristics that maximize the number of potential customers. Motivated by this problem, we also develop a batched extension of our RSA algorithm that significantly improves upon processing multiple queries individually, by grouping contiguous candidates, exploiting I/O commonalities and enabling shared processing. Our experimental study using both real and synthetic data sets demonstrates the superiority of our proposed algorithms for the studied classes of queries.
{"title":"Efficient influence-based processing of market research queries","authors":"Anastasios Arvanitis, Antonios Deligiannakis, Y. Vassiliou","doi":"10.1145/2396761.2398420","DOIUrl":"https://doi.org/10.1145/2396761.2398420","url":null,"abstract":"The rapid growth of social web has contributed vast amounts of user preference data. Analyzing this data and its relationships with products could have several practical applications, such as personalized advertising, market segmentation, product feature promotion etc. In this work we develop novel algorithms for efficiently processing two important classes of queries involving user preferences, i.e. potential customers identification and product positioning. With regards to the first problem, we formulate product attractiveness based on the notion of reverse skyline queries. We then present a new algorithm, termed as RSA, that significantly reduces the I/O cost, as well as the computation cost, when compared to the state-of-the-art reverse skyline algorithm, while at the same time being able to quickly report the first results. Several real-world applications require processing of a large number of queries, in order to identify the product characteristics that maximize the number of potential customers. Motivated by this problem, we also develop a batched extension of our RSA algorithm that significantly improves upon processing multiple queries individually, by grouping contiguous candidates, exploiting I/O commonalities and enabling shared processing. Our experimental study using both real and synthetic data sets demonstrates the superiority of our proposed algorithms for the studied classes of queries.","PeriodicalId":313414,"journal":{"name":"Proceedings of the 21st ACM international conference on Information and knowledge management","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115550944","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}
Matteo Riondato, Justin A. DeBrabant, Rodrigo Fonseca, E. Upfal
Frequent Itemsets and Association Rules Mining (FIM) is a key task in knowledge discovery from data. As the dataset grows, the cost of solving this task is dominated by the component that depends on the number of transactions in the dataset. We address this issue by proposing PARMA, a parallel algorithm for the MapReduce framework, which scales well with the size of the dataset (as number of transactions) while minimizing data replication and communication cost. PARMA cuts down the dataset-size-dependent part of the cost by using a random sampling approach to FIM. Each machine mines a small random sample of the dataset, of size independent from the dataset size. The results from each machine are then filtered and aggregated to produce a single output collection. The output will be a very close approximation of the collection of Frequent Itemsets (FI's) or Association Rules (AR's) with their frequencies and confidence levels. The quality of the output is probabilistically guaranteed by our analysis to be within the user-specified accuracy and error probability parameters. The sizes of the random samples are independent from the size of the dataset, as is the number of samples. They depend on the user-chosen accuracy and error probability parameters and on the parallel computational model. We implemented PARMA in Hadoop MapReduce and show experimentally that it runs faster than previously introduced FIM algorithms for the same platform, while 1) scaling almost linearly, and 2) offering even higher accuracy and confidence than what is guaranteed by the analysis.
{"title":"PARMA: a parallel randomized algorithm for approximate association rules mining in MapReduce","authors":"Matteo Riondato, Justin A. DeBrabant, Rodrigo Fonseca, E. Upfal","doi":"10.1145/2396761.2396776","DOIUrl":"https://doi.org/10.1145/2396761.2396776","url":null,"abstract":"Frequent Itemsets and Association Rules Mining (FIM) is a key task in knowledge discovery from data. As the dataset grows, the cost of solving this task is dominated by the component that depends on the number of transactions in the dataset. We address this issue by proposing PARMA, a parallel algorithm for the MapReduce framework, which scales well with the size of the dataset (as number of transactions) while minimizing data replication and communication cost. PARMA cuts down the dataset-size-dependent part of the cost by using a random sampling approach to FIM. Each machine mines a small random sample of the dataset, of size independent from the dataset size. The results from each machine are then filtered and aggregated to produce a single output collection. The output will be a very close approximation of the collection of Frequent Itemsets (FI's) or Association Rules (AR's) with their frequencies and confidence levels. The quality of the output is probabilistically guaranteed by our analysis to be within the user-specified accuracy and error probability parameters. The sizes of the random samples are independent from the size of the dataset, as is the number of samples. They depend on the user-chosen accuracy and error probability parameters and on the parallel computational model. We implemented PARMA in Hadoop MapReduce and show experimentally that it runs faster than previously introduced FIM algorithms for the same platform, while 1) scaling almost linearly, and 2) offering even higher accuracy and confidence than what is guaranteed by the analysis.","PeriodicalId":313414,"journal":{"name":"Proceedings of the 21st ACM international conference on Information and knowledge management","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115625673","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}
Nattiya Kanhabua, Sara Romano, Avare Stewart, W. Nejdl
Microblogging services, such as Twitter, are gaining interests as a means of sharing information in social networks. Numerous works have shown the potential of using Twitter posts (or tweets) in order to infer the existence and magnitude of real-world events. In the medical domain, there has been a surge in detecting public health related tweets for early warning so that a rapid response from health authorities can take place. In this paper, we present a temporal analytics tool for supporting a comparative, temporal analysis of disease outbreaks between Twitter and official sources, such as, World Health Organization (WHO) and ProMED-mail. We automatically extract and aggregate outbreak events from official outbreak reports, producing time series data. Our tool can support a correlation analysis and an understanding of the temporal developments of outbreak mentions in Twitter, based on comparisons with official sources.
{"title":"Supporting temporal analytics for health-related events in microblogs","authors":"Nattiya Kanhabua, Sara Romano, Avare Stewart, W. Nejdl","doi":"10.1145/2396761.2398726","DOIUrl":"https://doi.org/10.1145/2396761.2398726","url":null,"abstract":"Microblogging services, such as Twitter, are gaining interests as a means of sharing information in social networks. Numerous works have shown the potential of using Twitter posts (or tweets) in order to infer the existence and magnitude of real-world events. In the medical domain, there has been a surge in detecting public health related tweets for early warning so that a rapid response from health authorities can take place. In this paper, we present a temporal analytics tool for supporting a comparative, temporal analysis of disease outbreaks between Twitter and official sources, such as, World Health Organization (WHO) and ProMED-mail. We automatically extract and aggregate outbreak events from official outbreak reports, producing time series data. Our tool can support a correlation analysis and an understanding of the temporal developments of outbreak mentions in Twitter, based on comparisons with official sources.","PeriodicalId":313414,"journal":{"name":"Proceedings of the 21st ACM international conference on Information and knowledge management","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124273851","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}
Guang Xiang, Bin Fan, Ling Wang, Jason I. Hong, C. Rosé
In this paper, we propose a novel semi-supervised approach for detecting profanity-related offensive content in Twitter. Our approach exploits linguistic regularities in profane language via statistical topic modeling on a huge Twitter corpus, and detects offensive tweets using automatically these generated features. Our approach performs competitively with a variety of machine learning (ML) algorithms. For instance, our approach achieves a true positive rate (TP) of 75.1% over 4029 testing tweets using Logistic Regression, significantly outperforming the popular keyword matching baseline, which has a TP of 69.7%, while keeping the false positive rate (FP) at the same level as the baseline at about 3.77%. Our approach provides an alternative to large scale hand annotation efforts required by fully supervised learning approaches.
{"title":"Detecting offensive tweets via topical feature discovery over a large scale twitter corpus","authors":"Guang Xiang, Bin Fan, Ling Wang, Jason I. Hong, C. Rosé","doi":"10.1145/2396761.2398556","DOIUrl":"https://doi.org/10.1145/2396761.2398556","url":null,"abstract":"In this paper, we propose a novel semi-supervised approach for detecting profanity-related offensive content in Twitter. Our approach exploits linguistic regularities in profane language via statistical topic modeling on a huge Twitter corpus, and detects offensive tweets using automatically these generated features. Our approach performs competitively with a variety of machine learning (ML) algorithms. For instance, our approach achieves a true positive rate (TP) of 75.1% over 4029 testing tweets using Logistic Regression, significantly outperforming the popular keyword matching baseline, which has a TP of 69.7%, while keeping the false positive rate (FP) at the same level as the baseline at about 3.77%. Our approach provides an alternative to large scale hand annotation efforts required by fully supervised learning approaches.","PeriodicalId":313414,"journal":{"name":"Proceedings of the 21st ACM international conference on Information and knowledge management","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116966269","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}
Along with the increasing popularity of social web sites, users rely more on the trustworthiness information for many online activities among users. However, such social network data often suffers from severe data sparsity and are not able to provide users with enough information. Therefore, trust prediction has emerged as an important topic in social network research. Traditional approaches explore the topology of trust graph. Previous research in sociology and our life experience suggest that people who are in the same social circle often exhibit similar behavior and tastes. Such ancillary information, is often accessible and therefore could potentially help the trust prediction. In this paper, we address the link prediction problem by aggregating heterogeneous social networks and propose a novel joint manifold factorization (JMF) method. Our new joint learning model explores the user group level similarity between correlated graphs and simultaneously learns the individual graph structure, therefore the shared structures and patterns from multiple social networks can be utilized to enhance the prediction tasks. As a result, we not only improve the trust prediction in the target graph, but also facilitate other information retrieval tasks in the auxiliary graphs. To optimize the objective function, we break down the proposed objective function into several manageable sub-problems, then further establish the theoretical convergence with the aid of auxiliary function. Extensive experiments were conducted on real world data sets and all empirical results demonstrated the effectiveness of our method.
{"title":"Trust prediction via aggregating heterogeneous social networks","authors":"Jin Huang, F. Nie, Heng Huang, Yi-Cheng Tu","doi":"10.1145/2396761.2398515","DOIUrl":"https://doi.org/10.1145/2396761.2398515","url":null,"abstract":"Along with the increasing popularity of social web sites, users rely more on the trustworthiness information for many online activities among users. However, such social network data often suffers from severe data sparsity and are not able to provide users with enough information. Therefore, trust prediction has emerged as an important topic in social network research. Traditional approaches explore the topology of trust graph. Previous research in sociology and our life experience suggest that people who are in the same social circle often exhibit similar behavior and tastes. Such ancillary information, is often accessible and therefore could potentially help the trust prediction. In this paper, we address the link prediction problem by aggregating heterogeneous social networks and propose a novel joint manifold factorization (JMF) method. Our new joint learning model explores the user group level similarity between correlated graphs and simultaneously learns the individual graph structure, therefore the shared structures and patterns from multiple social networks can be utilized to enhance the prediction tasks. As a result, we not only improve the trust prediction in the target graph, but also facilitate other information retrieval tasks in the auxiliary graphs. To optimize the objective function, we break down the proposed objective function into several manageable sub-problems, then further establish the theoretical convergence with the aid of auxiliary function. Extensive experiments were conducted on real world data sets and all empirical results demonstrated the effectiveness of our method.","PeriodicalId":313414,"journal":{"name":"Proceedings of the 21st ACM international conference on Information and knowledge management","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116978839","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}
Content slicing addresses the need of adaptive systems to reuse open corpus material by converting it into re-composable information objects. However this conversion is highly dependent upon the ability to correctly fragment pages into structurally sound atomic pieces. A recently suggested approach to fragmentation, which relies on densitometric page representation, claims to achieve high accuracy and time performance. Although it has been well received within the research community, a full evaluation of this approach and identification of strengths and weaknesses across a range of characteristics hasn't been performed. This paper proposes an independent evaluation of the approach with respect to granularity control, accuracy, time performance, content diversity and linguistic dependency. Moreover, this paper also provides a significant contribution to address important weaknesses discovered during the analysis, in order to improve the suitability and impact of the original algorithm within the context of content slicing.
{"title":"An evaluation and enhancement of densitometric fragmentation for content slicing reuse","authors":"Killian Levacher, S. Lawless, V. Wade","doi":"10.1145/2396761.2398652","DOIUrl":"https://doi.org/10.1145/2396761.2398652","url":null,"abstract":"Content slicing addresses the need of adaptive systems to reuse open corpus material by converting it into re-composable information objects. However this conversion is highly dependent upon the ability to correctly fragment pages into structurally sound atomic pieces. A recently suggested approach to fragmentation, which relies on densitometric page representation, claims to achieve high accuracy and time performance. Although it has been well received within the research community, a full evaluation of this approach and identification of strengths and weaknesses across a range of characteristics hasn't been performed. This paper proposes an independent evaluation of the approach with respect to granularity control, accuracy, time performance, content diversity and linguistic dependency. Moreover, this paper also provides a significant contribution to address important weaknesses discovered during the analysis, in order to improve the suitability and impact of the original algorithm within the context of content slicing.","PeriodicalId":313414,"journal":{"name":"Proceedings of the 21st ACM international conference on Information and knowledge management","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116989937","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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