H. Huang, Yunjun Gao, Lu Chen, Rui Li, K. Chiew, Qinming He
Browse with either web directories or social bookmarks is an important complementation to search by keywords in web information retrieval. To improve users' browse experiences and facilitate the web directory construction, in this paper, we propose a novel browse system called Social Web Directory (SWD for short) by integrating web directories and social bookmarks. In SWD, (1) web pages are automatically categorized to a hierarchical structure to be retrieved efficiently, and (2) the popular web pages, hottest tags, and expert users in each category are ranked to help users find information more conveniently. Extensive experimental results demonstrate the effectiveness of our SWD system.
在网络信息检索中，利用网络目录或社交书签进行浏览是对关键词搜索的重要补充。为了提高用户的浏览体验，方便网络目录的构建，本文提出了一种将网络目录与社交书签相结合的新型浏览系统Social web directory(简称SWD)。在SWD中，(1)自动将网页分类为一个层次结构，以便高效地检索;(2)对每个类别中的热门网页、最热标签和专家用户进行排名，以帮助用户更方便地查找信息。大量的实验结果证明了我们的SWD系统的有效性。
{"title":"Browse with a social web directory","authors":"H. Huang, Yunjun Gao, Lu Chen, Rui Li, K. Chiew, Qinming He","doi":"10.1145/2484028.2484141","DOIUrl":"https://doi.org/10.1145/2484028.2484141","url":null,"abstract":"Browse with either web directories or social bookmarks is an important complementation to search by keywords in web information retrieval. To improve users' browse experiences and facilitate the web directory construction, in this paper, we propose a novel browse system called Social Web Directory (SWD for short) by integrating web directories and social bookmarks. In SWD, (1) web pages are automatically categorized to a hierarchical structure to be retrieved efficiently, and (2) the popular web pages, hottest tags, and expert users in each category are ranked to help users find information more conveniently. Extensive experimental results demonstrate the effectiveness of our SWD system.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122223130","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}
Shengxian Wan, Yanyan Lan, J. Guo, Chaosheng Fan, Xueqi Cheng
It is well recognized that users rely on social media (e.g. Twitter or Digg) to fulfill two common needs (i.e. social need and informational need) that is to keep in touch with their friends in the real world and to have access to information they are interested in. Traditional friend recommendation methods in social media mainly focus on a user's social need, but seldom address their informational need (i.e. suggesting friends that can provide information one may be interested in but have not been able to obtain so far). In this paper, we propose to recommend friends according to the informational utility, which stands for the degree to which a friend satisfies the target user's unfulfilled informational need, called informational friend recommendation. In order to capture users' informational need, we view a post in social media as an item and utilize collaborative filtering techniques to predict the rating for each post. The candidate friends are then ranked according to their informational utility for recommendation. In addition, we also show how to further consider diversity in such recommendations. Experiments on benchmark datasets demonstrate that our approach can significantly outperform the traditional friend recommendation methods under informational evaluation measures.
{"title":"Informational friend recommendation in social media","authors":"Shengxian Wan, Yanyan Lan, J. Guo, Chaosheng Fan, Xueqi Cheng","doi":"10.1145/2484028.2484179","DOIUrl":"https://doi.org/10.1145/2484028.2484179","url":null,"abstract":"It is well recognized that users rely on social media (e.g. Twitter or Digg) to fulfill two common needs (i.e. social need and informational need) that is to keep in touch with their friends in the real world and to have access to information they are interested in. Traditional friend recommendation methods in social media mainly focus on a user's social need, but seldom address their informational need (i.e. suggesting friends that can provide information one may be interested in but have not been able to obtain so far). In this paper, we propose to recommend friends according to the informational utility, which stands for the degree to which a friend satisfies the target user's unfulfilled informational need, called informational friend recommendation. In order to capture users' informational need, we view a post in social media as an item and utilize collaborative filtering techniques to predict the rating for each post. The candidate friends are then ranked according to their informational utility for recommendation. In addition, we also show how to further consider diversity in such recommendations. Experiments on benchmark datasets demonstrate that our approach can significantly outperform the traditional friend recommendation methods under informational evaluation measures.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131202271","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}
Dmitry Lagun, Avneesh Sud, Ryen W. White, P. Bailey, Georg Buscher
Modern search engines make extensive use of people's contextual information to finesse result rankings. Using a searcher's location provides an especially strong signal for adjusting results for certain classes of queries where people may have clear preference for local results, without explicitly specifying the location in the query direct-ly. However, if the location estimate is inaccurate or searchers want to obtain many results from a particular location, they have limited control on the location focus in the search results returned. In this paper we describe a user study that examines the effect of offering searchers more control over how local preferences are gathered and used. We studied providing users with functionality to offer explicit relevance feedback (ERF) adjacent to results automatically identi-fied as location-dependent (i.e., more from this location). They can use this functionality to indicate whether they are interested in a particular search result and desire more results from that result's location. We compared the ERF system against a baseline (NoERF) that used the same underlying mechanisms to retrieve and rank results, but did not offer ERF support. User performance was as-sessed across 12 experimental participants over 12 location-sensitive topics, in a fully counter-balanced design. We found that participants interacted with ERF frequently, and there were signs that ERF has the potential to improve success rates and lead to more efficient searching for location-sensitive search tasks than NoERF.
{"title":"Explicit feedback in local search tasks","authors":"Dmitry Lagun, Avneesh Sud, Ryen W. White, P. Bailey, Georg Buscher","doi":"10.1145/2484028.2484123","DOIUrl":"https://doi.org/10.1145/2484028.2484123","url":null,"abstract":"Modern search engines make extensive use of people's contextual information to finesse result rankings. Using a searcher's location provides an especially strong signal for adjusting results for certain classes of queries where people may have clear preference for local results, without explicitly specifying the location in the query direct-ly. However, if the location estimate is inaccurate or searchers want to obtain many results from a particular location, they have limited control on the location focus in the search results returned. In this paper we describe a user study that examines the effect of offering searchers more control over how local preferences are gathered and used. We studied providing users with functionality to offer explicit relevance feedback (ERF) adjacent to results automatically identi-fied as location-dependent (i.e., more from this location). They can use this functionality to indicate whether they are interested in a particular search result and desire more results from that result's location. We compared the ERF system against a baseline (NoERF) that used the same underlying mechanisms to retrieve and rank results, but did not offer ERF support. User performance was as-sessed across 12 experimental participants over 12 location-sensitive topics, in a fully counter-balanced design. We found that participants interacted with ERF frequently, and there were signs that ERF has the potential to improve success rates and lead to more efficient searching for location-sensitive search tasks than NoERF.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132015235","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}
Dayong Wang, S. Hoi, Pengcheng Wu, Jianke Zhu, Ying He, C. Miao
Automated face annotation aims to automatically detect human faces from a photo and further name the faces with the corresponding human names. In this paper, we tackle this open problem by investigating a search-based face annotation (SBFA) paradigm for mining large amounts of web facial images freely available on the WWW. Given a query facial image for annotation, the idea of SBFA is to first search for top-n similar facial images from a web facial image database and then exploit these top-ranked similar facial images and their weak labels for naming the query facial image. To fully mine those information, this paper proposes a novel framework of Learning to Name Faces (L2NF) -- a unified multimodal learning approach for search-based face annotation, which consists of the following major components: (i) we enhance the weak labels of top-ranked similar images by exploiting the "label smoothness" assumption; (ii) we construct the multimodal representations of a facial image by extracting different types of features; (iii) we optimize the distance measure for each type of features using distance metric learning techniques; and finally (iv) we learn the optimal combination of multiple modalities for annotation through a learning to rank scheme. We conduct a set of extensive empirical studies on two real-world facial image databases, in which encouraging results show that the proposed algorithms significantly boost the naming accuracy of search-based face annotation task.
{"title":"Learning to name faces: a multimodal learning scheme for search-based face annotation","authors":"Dayong Wang, S. Hoi, Pengcheng Wu, Jianke Zhu, Ying He, C. Miao","doi":"10.1145/2484028.2484040","DOIUrl":"https://doi.org/10.1145/2484028.2484040","url":null,"abstract":"Automated face annotation aims to automatically detect human faces from a photo and further name the faces with the corresponding human names. In this paper, we tackle this open problem by investigating a search-based face annotation (SBFA) paradigm for mining large amounts of web facial images freely available on the WWW. Given a query facial image for annotation, the idea of SBFA is to first search for top-n similar facial images from a web facial image database and then exploit these top-ranked similar facial images and their weak labels for naming the query facial image. To fully mine those information, this paper proposes a novel framework of Learning to Name Faces (L2NF) -- a unified multimodal learning approach for search-based face annotation, which consists of the following major components: (i) we enhance the weak labels of top-ranked similar images by exploiting the \"label smoothness\" assumption; (ii) we construct the multimodal representations of a facial image by extracting different types of features; (iii) we optimize the distance measure for each type of features using distance metric learning techniques; and finally (iv) we learn the optimal combination of multiple modalities for annotation through a learning to rank scheme. We conduct a set of extensive empirical studies on two real-world facial image databases, in which encouraging results show that the proposed algorithms significantly boost the naming accuracy of search-based face annotation task.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134090909","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 political landscape is fluid. Discussions are always ongoing and new "hot topics" continue to appear in the headlines. But what made people start talking about that topic? And who started it? Because of the speed at which discussions sometimes take place this can be difficult to track down. We describe ThemeStreams: a demonstrator that maps political discussions to themes and influencers and illustrate how this mapping is used in an interactive visualization that shows us which themes are being discussed, and that helps us answer the question "Who put this issue on the map?" in streams of political data.
{"title":"ThemeStreams: visualizing the stream of themes discussed in politics","authors":"O. D. Rooij, Daan Odijk, M. de Rijke","doi":"10.1145/2484028.2484215","DOIUrl":"https://doi.org/10.1145/2484028.2484215","url":null,"abstract":"The political landscape is fluid. Discussions are always ongoing and new \"hot topics\" continue to appear in the headlines. But what made people start talking about that topic? And who started it? Because of the speed at which discussions sometimes take place this can be difficult to track down. We describe ThemeStreams: a demonstrator that maps political discussions to themes and influencers and illustrate how this mapping is used in an interactive visualization that shows us which themes are being discussed, and that helps us answer the question \"Who put this issue on the map?\" in streams of political data.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133881364","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}
Pub Date : 2013-07-28DOI: 10.1007/978-3-319-12979-2_10
Tony Russell-Rose
{"title":"Designing search usability","authors":"Tony Russell-Rose","doi":"10.1007/978-3-319-12979-2_10","DOIUrl":"https://doi.org/10.1007/978-3-319-12979-2_10","url":null,"abstract":"","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133906244","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}
Expert retrieval has been widely studied especially after the introduction of Expert Finding task in the TREC's Enterprise Track in 2005 [3]. This track provided two different test collections crawled from two organizations' public-facing websites and internal emails which led to the development of many state-of-the-art algorithms on expert retrieval [1]. Until recently, these datasets were considered good representatives of the information resources available within enterprise. However, the recent growth of social media also influenced the work environment, and social media became a common communication and collaboration tool within organizations. According to a recent survey by McKinsey Global Institute [2], 29% of the companies use at least one social media tool for matching their employees to tasks, and 26% of them assess their employees' performance by using social media. This shows that intra-organizational social media became an important resource to identify expertise within organizations. In recent years, in addition to the intra-organizational social media, public social media tools like Twitter, Facebook, LinkedIn also became common environments for searching expertise. These tools provide an opportunity for their users to show their specific skills to the world which motivates recruiters to look for talented job candidates on social media, or writers and reporters to find experts for consulting on specific topics they are working on. With these motivations in mind, in this work we propose to develop expert retrieval algorithms for intra-organizational and public social media tools. Social media datasets have both challenges and advantages. In terms of challenges, they do not always contain context on one specific domain, instead one social media tool may contain discussions on technical stuff, hobbies or news concurrently. They may also contain spam posts or advertisements. Compared to well-edited enterprise documents, they are much more informal in language. Furthermore, depending on the social media platform, they may have limits on the number of characters used in posts. Even though they include the challenges stated above, they also bring some unique authority signals, such as votes, comments, follower/following information, which can be useful in estimating expertise. Furthermore, compared to previously used enterprise documents, social media provides clear associations between documents and candidates in the context of authorship information. In this work, we propose to develop expert retrieval approaches which will handle these challenges while making use of the advantages. Expert retrieval is a very useful application by itself; furthermore, it can be a step towards improving other social media applications. Social media is different than other web based tools mainly because it is dependent on its users. In social media, users are not just content consumers, but they are also the primary and sometimes the only content creators
自2005年在TREC的企业轨道中引入专家查找任务以来，专家检索得到了广泛的研究。这条赛道提供了从两个组织的面向公众的网站和内部电子邮件中抓取的两个不同的测试集合，这导致了专家检索[1]上许多最先进算法的发展。直到最近，这些数据集还被认为是企业内可用信息资源的良好代表。然而，最近社交媒体的发展也影响了工作环境，社交媒体成为组织内部常见的沟通和协作工具。根据麦肯锡全球研究院(McKinsey Global Institute)最近的一项调查，29%的公司至少使用一种社交媒体工具来匹配员工的任务，26%的公司通过使用社交媒体来评估员工的表现。这表明组织内社交媒体成为组织内识别专业知识的重要资源。近年来，除了组织内部的社交媒体外，Twitter、Facebook、LinkedIn等公共社交媒体工具也成为搜索专业知识的常见环境。这些工具为他们的用户提供了一个向世界展示他们的特定技能的机会，这促使招聘人员在社交媒体上寻找有才华的求职者，或者作家和记者找到专家来咨询他们正在研究的特定主题。考虑到这些动机，在这项工作中，我们建议为组织内部和公共社交媒体工具开发专家检索算法。社交媒体数据集既有挑战，也有优势。就挑战而言，它们并不总是包含特定领域的上下文，相反，一个社交媒体工具可能同时包含有关技术内容、爱好或新闻的讨论。它们也可能包含垃圾邮件或广告。与精心编辑的企业文档相比，它们在语言上要随意得多。此外，根据社交媒体平台的不同，他们可能会限制帖子中使用的字符数量。尽管它们包括上述挑战，但它们也带来了一些独特的权威信号，如投票、评论、追随者/跟踪信息，这些信息在评估专业知识时很有用。此外，与以前使用的企业文档相比，社交媒体在作者信息上下文中提供了文档和候选人之间的明确关联。在这项工作中，我们建议开发专家检索方法来处理这些挑战，同时利用优势。专家检索本身就是一个非常有用的应用;此外，它可以成为改进其他社交媒体应用程序的一步。社交媒体不同于其他基于网络的工具，主要是因为它依赖于它的用户。在社交媒体中，用户不仅仅是内容的消费者，他们也是主要的，有时甚至是唯一的内容创造者。因此，社交媒体中任何用户生成内容的质量取决于其创建者。在本文中，我们建议使用用户的专业知识来改进现有的应用程序，以便他们不仅可以基于内容，还可以基于内容创建者的专业知识来估计内容的相关性。通过使用内容生成器的专业知识，我们也希望增加更可靠的内容。我们建议利用这些用户的专业知识信息来改进社交媒体中的特别搜索和问答应用程序。在这项工作中，以前的TREC企业数据集，可用的组织内部社交媒体和公共社交媒体数据集将用于测试所提出的算法。
{"title":"Effective approaches to retrieving and using expertise in social media","authors":"Reyyan Yeniterzi","doi":"10.1145/2484028.2484230","DOIUrl":"https://doi.org/10.1145/2484028.2484230","url":null,"abstract":"Expert retrieval has been widely studied especially after the introduction of Expert Finding task in the TREC's Enterprise Track in 2005 [3]. This track provided two different test collections crawled from two organizations' public-facing websites and internal emails which led to the development of many state-of-the-art algorithms on expert retrieval [1]. Until recently, these datasets were considered good representatives of the information resources available within enterprise. However, the recent growth of social media also influenced the work environment, and social media became a common communication and collaboration tool within organizations. According to a recent survey by McKinsey Global Institute [2], 29% of the companies use at least one social media tool for matching their employees to tasks, and 26% of them assess their employees' performance by using social media. This shows that intra-organizational social media became an important resource to identify expertise within organizations. In recent years, in addition to the intra-organizational social media, public social media tools like Twitter, Facebook, LinkedIn also became common environments for searching expertise. These tools provide an opportunity for their users to show their specific skills to the world which motivates recruiters to look for talented job candidates on social media, or writers and reporters to find experts for consulting on specific topics they are working on. With these motivations in mind, in this work we propose to develop expert retrieval algorithms for intra-organizational and public social media tools. Social media datasets have both challenges and advantages. In terms of challenges, they do not always contain context on one specific domain, instead one social media tool may contain discussions on technical stuff, hobbies or news concurrently. They may also contain spam posts or advertisements. Compared to well-edited enterprise documents, they are much more informal in language. Furthermore, depending on the social media platform, they may have limits on the number of characters used in posts. Even though they include the challenges stated above, they also bring some unique authority signals, such as votes, comments, follower/following information, which can be useful in estimating expertise. Furthermore, compared to previously used enterprise documents, social media provides clear associations between documents and candidates in the context of authorship information. In this work, we propose to develop expert retrieval approaches which will handle these challenges while making use of the advantages. Expert retrieval is a very useful application by itself; furthermore, it can be a step towards improving other social media applications. Social media is different than other web based tools mainly because it is dependent on its users. In social media, users are not just content consumers, but they are also the primary and sometimes the only content creators","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127693238","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}
A. Freitas, Fabrício F. de Faria, Seán O'Riain, E. Curry
This paper demonstrates Treo, a natural language query mechanism for Linked Data graphs. The approach uses a distributional semantic vector space model to semantically match user query terms with data, supporting vocabulary-independent (or schema-agnostic) queries over structured data.
{"title":"Answering natural language queries over linked data graphs: a distributional semantics approach","authors":"A. Freitas, Fabrício F. de Faria, Seán O'Riain, E. Curry","doi":"10.1145/2484028.2484209","DOIUrl":"https://doi.org/10.1145/2484028.2484209","url":null,"abstract":"This paper demonstrates Treo, a natural language query mechanism for Linked Data graphs. The approach uses a distributional semantic vector space model to semantically match user query terms with data, supporting vocabulary-independent (or schema-agnostic) queries over structured data.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127934250","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}
Electronic medical records (EMRs) are being increasingly used worldwide to facilitate improved healthcare services [2,3]. They describe the clinical decision process relating to a patient, detailing the observed symptoms, the conducted diagnostic tests, the identified diagnoses and the prescribed treatments. However, medical records search is challenging, due to the implicit knowledge inherent within the medical records - such knowledge may be known by medical practitioners, but hidden to an information retrieval (IR) system [3]. For instance, the mention of a treatment such as a drug may indicate to a practitioner that a particular diagnosis has been made even if this was not explicitly mentioned in the patient's EMRs. Moreover, the fact that a symptom has not been observed by a clinician may rule out some specific diagnoses. Our work focuses on searching EMRs to identify patients with medical histories relevant to the medical condition(s) stated in a query. The resulting system can be beneficial to healthcare providers, administrators, and researchers who may wish to analyse the effectiveness of a particular medical procedure to combat a specific disease [2,4]. During retrieval, a healthcare provider may indicate a number of inclusion criteria to describe the type of patients of interest. For example, the used criteria may include personal profiles (e.g. age and gender) or some specific medical symptoms and tests, allowing to identify patients that have EMRs matching the criteria. To attain effective retrieval performance, we hypothesise that, in such a medical IR system, both the information needs and patients should be modelled based on how the medical process is developed. Specifically, our thesis states that since the medical decision process typically encompasses four aspects (symptom, diagnostic test, diagnosis, and treatment), a medical search system should take into account these aspects and apply inferences to recover possible implicit knowledge. We postulate that considering these aspects and their derived implicit knowledge at different levels of the retrieval process (namely, sentence, record, and inter-record level) enhances the retrieval performance. Indeed, we propose to build a query and patient understanding framework that can gain insights from EMRs and queries, by modelling and reasoning during retrieval in terms of the four aforementioned aspects (symptom, diagnostic test, diagnosis, and treatment) at three different levels of the retrieval process.
{"title":"A query and patient understanding framework for medical records search","authors":"Nut Limsopatham","doi":"10.1145/2484028.2484228","DOIUrl":"https://doi.org/10.1145/2484028.2484228","url":null,"abstract":"Electronic medical records (EMRs) are being increasingly used worldwide to facilitate improved healthcare services [2,3]. They describe the clinical decision process relating to a patient, detailing the observed symptoms, the conducted diagnostic tests, the identified diagnoses and the prescribed treatments. However, medical records search is challenging, due to the implicit knowledge inherent within the medical records - such knowledge may be known by medical practitioners, but hidden to an information retrieval (IR) system [3]. For instance, the mention of a treatment such as a drug may indicate to a practitioner that a particular diagnosis has been made even if this was not explicitly mentioned in the patient's EMRs. Moreover, the fact that a symptom has not been observed by a clinician may rule out some specific diagnoses. Our work focuses on searching EMRs to identify patients with medical histories relevant to the medical condition(s) stated in a query. The resulting system can be beneficial to healthcare providers, administrators, and researchers who may wish to analyse the effectiveness of a particular medical procedure to combat a specific disease [2,4]. During retrieval, a healthcare provider may indicate a number of inclusion criteria to describe the type of patients of interest. For example, the used criteria may include personal profiles (e.g. age and gender) or some specific medical symptoms and tests, allowing to identify patients that have EMRs matching the criteria. To attain effective retrieval performance, we hypothesise that, in such a medical IR system, both the information needs and patients should be modelled based on how the medical process is developed. Specifically, our thesis states that since the medical decision process typically encompasses four aspects (symptom, diagnostic test, diagnosis, and treatment), a medical search system should take into account these aspects and apply inferences to recover possible implicit knowledge. We postulate that considering these aspects and their derived implicit knowledge at different levels of the retrieval process (namely, sentence, record, and inter-record level) enhances the retrieval performance. Indeed, we propose to build a query and patient understanding framework that can gain insights from EMRs and queries, by modelling and reasoning during retrieval in terms of the four aforementioned aspects (symptom, diagnostic test, diagnosis, and treatment) at three different levels of the retrieval process.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129079654","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}
Twitter is currently one of the largest social hubs for users to spread and discuss news. For most of the top news stories happening, there are corresponding discussions on social media. In this demonstration TweetMogaz is presented, which is a platform for microblog search and filtering. It creates a real-time comprehensive report about what people discuss and share around news happening in certain regions. TweetMogaz reports the most popular tweets, jokes, videos, images, and news articles that people share about top news stories. Moreover, it allows users to search for specific topics. A scalable automatic technique for microblog filtering is used to obtain relevant tweets to a certain news category in a region. TweetMogaz.com demonstrates the effectiveness of our filtering technique for reporting public response toward news in different Arabic regions including Egypt and Syria in real-time.
{"title":"TweetMogaz: a news portal of tweets","authors":"Walid Magdy","doi":"10.1145/2484028.2484212","DOIUrl":"https://doi.org/10.1145/2484028.2484212","url":null,"abstract":"Twitter is currently one of the largest social hubs for users to spread and discuss news. For most of the top news stories happening, there are corresponding discussions on social media. In this demonstration TweetMogaz is presented, which is a platform for microblog search and filtering. It creates a real-time comprehensive report about what people discuss and share around news happening in certain regions. TweetMogaz reports the most popular tweets, jokes, videos, images, and news articles that people share about top news stories. Moreover, it allows users to search for specific topics. A scalable automatic technique for microblog filtering is used to obtain relevant tweets to a certain news category in a region. TweetMogaz.com demonstrates the effectiveness of our filtering technique for reporting public response toward news in different Arabic regions including Egypt and Syria in real-time.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129305104","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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