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1.
Sophie Rutter Verena Blinzler Chaoyu Ye Max L. Wilson Michael D. Twidale 《Information processing & management》2019,56(3):919-938
With greater access to computational resources, people use search to address many everyday challenges in their lives, including solving technology problems. Although there are now many useful ‘how-to’ resources online (especially videos on YouTube), it can still be difficult to identify, understand, and resolve certain kinds of technical problem. While research tasks have been studied for many years and we know the tactics people use, we know far less about searchers’ tactics for how-to technical tasks that involve actually being able to apply found information to resolve a problem. Crucial to our study was developing and studying a highly realistic, how-to technical task, for which there was no single guidance resource: making a phone safe for a child. After providing 39 participants with an actual phone to fix, and a search engine to perform the task, we analysed their search tactics using retrospective cued think aloud interviews. Our primary contribution is a set of 77 tactics used, in three categories, along with detail of how common they were. We conclude that people had a lot of tactics in their repertoire. Although it was not hard for participants to find relevant information, what was hard was for participants to find information they could use; indeed only 23% of participants successfully completed the entire task. Domain knowledge affected the choice of tactics used (although not necessarily towards better task success). We discuss these influences and make design recommendations for how future search systems can support those in resolving how-to technical tasks. 相似文献
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《Information processing & management》2022,59(3):102934
Compared with explicit sentiment analysis that attracts considerable attention, implicit sentiment analysis is a more difficult task due to the lack of sentimental words. The abundant information in an external sentimental knowledge base can play a significant complementary and expansion role. In this paper, a sentimental commonsense knowledge graph embedded multi-polarity orthogonal attention model is proposed to learn the implication of the implicit sentiment. We analyzed the effectiveness of different knowledge relations in the ConceptNet knowledge base in detail, and proposed a matching and filtering method to distill useful knowledge tuples for implicit sentiment analysis automatically. By introducing the sentimental information in the knowledge base, the proposed model can extend the semantic of a sentence with an implicit sentiment. Then, a bi-directional long–short term memory model with multi-polarity orthogonal attention is adopted to fuse the distilled sentimental knowledge with the semantic embedding, effectively enriching the representation of sentences. Experiments on the SMP2019-ECISA implicit sentiment dataset show that our model fully utilizes the information of the knowledge base and improves the performance of Chinese implicit sentiment analysis. 相似文献
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A study to compare the cost effectiveness of retrospective manual and on-line bibliographic searching is described. Forty search queries were processed against seven abstracting-indexing publications and the corresponding SDC/ORBIT data bases. Equivalent periods of coverage and searcher skill levels were used for both search models. Separate task times were measured for question analysis, searching, photocopying, shelving, and output distribution. Component costs were calculated for labor, information, reproduction, equipment, physical space, and telecommunications. Results indicate that on-line searching is generally faster, less costly, and more effective than manual searching. However, for certain query/information-source combinations, manual searching may offer some advantages in precision and turn-around time. The results of a number of related studies are reviewed. 相似文献
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Professional, workplace searching is different from general searching, because it is typically limited to specific facets and targeted to a single answer. We have developed the semantic component (SC) model, which is a search feature that allows searchers to structure and specify the search to context-specific aspects of the main topic of the documents. We have tested the model in an interactive searching study with family doctors with the purpose to explore doctors’ querying behaviour, how they applied the means for specifying a search, and how these features contributed to the search outcome. In general, the doctors were capable of exploiting system features and search tactics during the searching. Most searchers produced well-structured queries that contained appropriate search facets. When searches failed it was not due to query structure or query length. Failures were mostly caused by the well-known vocabulary problem. The problem was exacerbated by using certain filters as Boolean filters. The best working queries were structured into 2–3 main facets out of 3–5 possible search facets, and expressed with terms reflecting the focal view of the search task. The findings at the same time support and extend previous results about query structure and exhaustivity showing the importance of selecting central search facets and express them from the perspective of search task. The SC model was applied in the highest performing queries except one. The findings suggest that the model might be a helpful feature to structure queries into central, appropriate facets, and in returning highly relevant documents. 相似文献
5.
Michael J. Cole Jacek Gwizdka Chang Liu Nicholas J. Belkin Xiangmin Zhang 《Information processing & management》2013
The acquisition of information and the search interaction process is influenced strongly by a person’s use of their knowledge of the domain and the task. In this paper we show that a user’s level of domain knowledge can be inferred from their interactive search behaviors without considering the content of queries or documents. A technique is presented to model a user’s information acquisition process during search using only measurements of eye movement patterns. In a user study (n = 40) of search in the domain of genomics, a representation of the participant’s domain knowledge was constructed using self-ratings of knowledge of genomics-related terms (n = 409). Cognitive effort features associated with reading eye movement patterns were calculated for each reading instance during the search tasks. The results show correlations between the cognitive effort due to reading and an individual’s level of domain knowledge. We construct exploratory regression models that suggest it is possible to build models that can make predictions of the user’s level of knowledge based on real-time measurements of eye movement patterns during a task session. 相似文献
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A better understanding of what motivates humans to perform certain actions is relevant for a range of research challenges including generating action sequences that implement goals (planning). A first step in this direction is the task of acquiring knowledge about human goals. In this work, we investigate whether Search Query Logs are a viable source for extracting expressions of human goals. For this purpose, we devise an algorithm that automatically identifies queries containing explicit goals such as find home to rent in Florida. Evaluation results of our algorithm achieve useful precision/recall values. We apply the classification algorithm to two large Search Query Logs, recorded by AOL and Microsoft Research in 2006, and obtain a set of ∼110,000 queries containing explicit goals. To study the nature of human goals in Search Query Logs, we conduct qualitative, quantitative and comparative analyses. Our findings suggest that Search Query Logs (i) represent a viable source for extracting human goals, (ii) contain a great variety of human goals and (iii) contain human goals that can be employed to complement existing commonsense knowledge bases. Finally, we illustrate the potential of goal knowledge for addressing following application scenario: to refine and extend commonsense knowledge with human goals from Search Query Logs. This work is relevant for (i) knowledge engineers interested in acquiring human goals from textual corpora and constructing knowledge bases of human goals (ii) researchers interested in studying characteristics of human goals in Search Query Logs. 相似文献
7.
We consider a network of autonomous peers forming a logically global but physically distributed search engine, where every peer has its own local collection generated by independently crawling the Web. A challenging task in such systems is to efficiently route user queries to peers that can deliver high quality results and be able to rank these returned results, thus satisfying the users’ information need. However, the problem inherent with this scenario is selecting a few promising peers out of an a priori unlimited number of peers. In recent research a rather strict notion of semantic overlay networks has been established. In most approaches, peers are connected to other peers based on a rigid semantic profile by clustering them based on their contents. In contrast, our strategy follows the spirit of peer autonomy and creates semantic overlay networks based on the notion of “peer-to-peer dating”. Peers are free to decide which connections they create and which they want to avoid based on various usefulness estimators. The proposed techniques can be easily integrated into existing systems as they require only small additional bandwidth consumption as most messages can be piggybacked onto established communication. We show how we can greatly benefit from these additional semantic relations during query routing in search engines, such as Minerva, and in the JXP algorithm, which computes the PageRank authority measure in a completely decentralized manner. 相似文献
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给出了在一个面向Agent、本体和关系的常识知识库中如何实现常识知识查询的过程。在查询过程中,用自然语言形式描述的查询先被转化成一种我们所定义的面向Agent和本体的描述性查询语言。接着,描述性查询被翻译成SQL查询语句。用SQL查询语句对已转化成为关系形式的常识知识库进行查询后,自然语言生成算法把以关系表为形式的查询结果转化成自然语言。实验表明,新的查询方法在查询时间上优于原有的直接在agent和本体上查询知识的旧方法。实验数据表明,查询时间缩短了大约80 %。 相似文献
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《Information processing & management》2016,52(4):628-643
Query auto completion (QAC) models recommend possible queries to web search users when they start typing a query prefix. Most of today’s QAC models rank candidate queries by popularity (i.e., frequency), and in doing so they tend to follow a strict query matching policy when counting the queries. That is, they ignore the contributions from so-called homologous queries, queries with the same terms but ordered differently or queries that expand the original query. Importantly, homologous queries often express a remarkably similar search intent. Moreover, today’s QAC approaches often ignore semantically related terms. We argue that users are prone to combine semantically related terms when generating queries.We propose a learning to rank-based QAC approach, where, for the first time, features derived from homologous queries and semantically related terms are introduced. In particular, we consider: (i) the observed and predicted popularity of homologous queries for a query candidate; and (ii) the semantic relatedness of pairs of terms inside a query and pairs of queries inside a session. We quantify the improvement of the proposed new features using two large-scale real-world query logs and show that the mean reciprocal rank and the success rate can be improved by up to 9% over state-of-the-art QAC models. 相似文献
10.
《Information processing & management》2023,60(2):103215
Traditional topic models are based on the bag-of-words assumption, which states that the topic assignment of each word is independent of the others. However, this assumption ignores the relationship between words, which may hinder the quality of extracted topics. To address this issue, some recent works formulate documents as graphs based on word co-occurrence patterns. It assumes that if two words co-occur frequently, they should have the same topic. Nevertheless, it introduces noise edges into the model and thus hinders topic quality since two words co-occur frequently do not mean that they are on the same topic. In this paper, we use the commonsense relationship between words as a bridge to connect the words in each document. Compared to word co-occurrence, the commonsense relationship can explicitly imply the semantic relevance between words, which can be utilized to filter out noise edges. We use a relational graph neural network to capture the relation information in the graph. Moreover, manifold regularization is utilized to constrain the documents’ topic distributions. Experimental results on a public dataset show that our method is effective at extracting topics compared to baseline methods. 相似文献
11.
We investigated the searching behaviors of twenty-four children in grades 6, 7, and 8 (ages 11–13) in finding information on three types of search tasks in Google. Children conducted 72 search sessions and issued 150 queries. Children's phrase- and question-like queries combined were much more prevalent than keyword queries (70% vs. 30%, respectively). Fifty two percent of the queries were reformulations (33 sessions). We classified children's query reformulation types into five classes based on the taxonomy by Liu et al. (2010). We found that most query reformulations were by Substitution and Specialization, and that children hardly repeated queries. We categorized children's queries by task facets and examined the way they expressed these facets in their query formulations and reformulations. Oldest children tended to target the general topic of search tasks in their queries most frequently, whereas younger children expressed one of the two facets more often. We assessed children's achieved task outcomes using the search task outcomes measure we developed. Children were mostly more successful on the fact-finding and fully self-generated task and partially successful on the research-oriented task. Query type, reformulation type, achieved task outcomes, and expressing task facets varied by task type and grade level. There was no significant effect of query length in words or of the number of queries issued on search task outcomes. The study findings have implications for human intervention, digital literacy, search task literacy, as well as for system intervention to support children's query formulation and reformulation during interaction with Google. 相似文献
12.
《Information processing & management》2022,59(1):102746
The importance of query performance prediction has been widely acknowledged in the literature, especially for query expansion, refinement, and interpolating different retrieval approaches. This paper proposes a novel semantics-based query performance prediction approach based on estimating semantic similarities between queries and documents. We introduce three post-retrieval predictors, namely (1) semantic distinction, (2) semantic query drift, and (3) semantic cohesion based on (1) the semantic similarity of a query to the top-ranked documents compared to the whole collection, (2) the estimation of non-query related aspects of the retrieved documents using semantic measures, and (3) the semantic cohesion of the retrieved documents. We assume that queries and documents are modeled as sets of entities from a knowledge graph, e.g., DBPedia concepts, instead of bags of words. With this assumption, semantic similarities between two texts are measured based on the relatedness between entities, which are learned from the contextual information represented in the knowledge graph. We empirically illustrate these predictors’ effectiveness, especially when term-based measures fail to quantify query performance prediction hypotheses correctly. We report our findings on the proposed predictors’ performance and their interpolation on three standard collections, namely ClueWeb09-B, ClueWeb12-B, and Robust04. We show that the proposed predictors are effective across different datasets in terms of Pearson and Kendall correlation coefficients between the predicted performance and the average precision measured by relevance judgments. 相似文献
13.
A key challenge in current Business Analytics (BA) is the selection of suitable indicators for business objectives. This requires the exploration of business data through data-driven approaches, while modelling business strategies together with domain experts in order to represent domain knowledge. In particular, Key Performance Indicators (KPIs) allow human experts to properly model ambiguous enterprise goals by means of quantitative variables with numeric ranges and clear thresholds. Besides business-related domains, the usefulness of KPIs has been shown in multiple domains, such as: Education, Healthcare and Agriculture. However, finding accurate KPIs for a given strategic goal still remains a complex task, specially due to the discrepancy between domain assumptions and data facts. In this regard, the semantic web emerges as a powerful technology for knowledge representation and data modeling through explicit representation formats and standards such as RDF(S) and OWL. By using this technology, the semantic annotation of indicators of business objectives would enrich the strategic model obtained. With this motivation, an ontology-driven approach is proposed to formally conceptualize essential elements of indicators, covering: performance, results, measures, goals and relationships of a given business strategy. In this way, all the data involved in the selection and analysis of KPIs are then integrated and stored in common repositories, hence enabling sophisticated querying and reasoning for semantic validation. The proposed semantic model is evaluated on a real-world case study on water management. A series of data analysis and reasoning tasks are conducted to show how the ontological model is able to detect semantic conflicts in actual correlations of selected indicators. 相似文献
14.
Internet已成为全球最丰富的数据源,数据类型繁杂且动态变化,如何从中快速准确地检索出用户所需要的信息是一个亟待解决的问题.传统的搜索引擎基于语法的方式进行搜索,缺乏语义信息,难以准确地表达用户的查询需求和被检索对象的文档语义,致使查准率和查全率较低且搜索范围有限.本文对现有的语义检索方法进行了研究,分析了其中存在的问题,在此基础上提出了一种基于领域的语义搜索引擎模型,结合语义Web技术,使用领域本体元数据模型对用户的查询进行语义化规范,依据领域本体模式抽取文档中的知识并RDF化,准确地表达了用户的查询语义和作为被查询对象的文档语义,可以大大提高检索的准确性和检索效率,详细地给出了模型的体系结构、基本功能和工作原理. 相似文献
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《Information processing & management》2020,57(2):102184
Recreational queries from users searching for places to go and things to do or see are very common in web and mobile search. Users specify constraints for what they are looking for, like suitability for kids, romantic ambiance or budget. Queries like “restaurants in New York City” are currently served by static local results or the thumbnail carousel. More complex queries like “things to do in San Francisco with kids” or “romantic places to eat in Seattle” require the user to click on every element of the search engine result page to read articles from Yelp, TripAdvisor, or WikiTravel to satisfy their needs. Location data, which is an essential part of web search, is even more prevalent with location-based social networks and offers new opportunities for many ways of satisfying information seeking scenarios.In this paper, we address the problem of recreational queries in information retrieval and propose a solution that combines search query logs with LBSNs data to match user needs and possible options. At the core of our solution is a framework that combines social, geographical, and temporal information for a relevance model centered around the use of semantic annotations on Points of Interest with the goal of addressing these recreational queries. A central part of the framework is a taxonomy derived from behavioral data that drives the modeling and user experience. We also describe in detail the complexity of assessing and evaluating Point of Interest data, a topic that is usually not covered in related work, and propose task design alternatives that work well.We demonstrate the feasibility and scalability of our methods using a data set of 1B check-ins and a large sample of queries from the real-world. Finally, we describe the integration of our techniques in a commercial search engine. 相似文献
17.
基于本体的专题域知识库系统设计与实现——以张謇研究专题知识库系统实现为例 总被引：7，自引：0，他引：7
综合现有研究成果概述本体和知识库含义，以张謇研究专题知识库系统开发为例，采用分层设计思想开发基于本体的专题域知识库系统，实现了完整的本体知识获取、构建、存储和用户服务的过程。 相似文献
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Noetica is a tool for structuring knowledge about concepts and the relationships between them. It differs from typical information systems in that the knowledge it represents is abstract, highly connected and includes meta-knowledge (knowledge about knowledge). Noetica represents knowledge using a strongly-typed semantic network. By providing a rich type system it is possible to represent conceptual information using formalised structures. A class hierarchy provides a basic classification for all objects. This allows for a consistency of representation that is not often found in “free” semantic networks and gives the ability to easily extend a knowledge model while retaining its semantics. We also provide visualisation and query tools for this data model. Visualisation can be used to explore complete sets of link-classes, show paths while navigating through the database, or visualise the results of queries. Noetica supports goal-directed queries (a series of user-supplied goals that the system attempts to satisfy in sequence) and path-finding queries (where the system find relationships between objects in the database by following links). 相似文献
20.
《Information processing & management》2005,41(5):1243-1262
The analysis of contextual information in search engine query logs enhances the understanding of Web users’ search patterns. Obtaining contextual information on Web search engine logs is a difficult task, since users submit few number of queries, and search multiple topics. Identification of topic changes within a search session is an important branch of search engine user behavior analysis. The purpose of this study is to investigate the properties of a specific topic identification methodology in detail, and to test its validity. The topic identification algorithm’s performance becomes doubtful in various cases. These cases are explored and the reasons underlying the inconsistent performance of automatic topic identification are investigated with statistical analysis and experimental design techniques. 相似文献