Article

Acquiring knowledge about human goals from Search Query Logs

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Abstract

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.

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... Transform the data in required form to apply the proposed approaches of data mining in supervised or unsupervised format. Now the dataset is ready to prepare the intention map, pseudo map or to be divided in intention categories [17,18]. ...
... [17] Showed that search query logs represented a viable, yet largely untapped, source for acquiring knowledge about human goals. Four types of wish detectors proposed in [18] to take insight of world"s wants and desires. They analyzed 80,000 English wish sentences of New Year. ...
... Research paper Quantity Tweets [9], [14], [22], [25], [28], [29], [23] 7 Facebook comments [10] 1 Product reviews [11], [15], [18], [19], [30] 5 Questionnaire Survey [16], [27], [24] 3 Web Search Log [21], [31], [32], [17], [19], [20] 4 Political reviews [18] 1 Chat logs [12] 1 Figure 5 reflects the classification of articles by quantity of used dataset as follows: ...
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Text mining is a frame work to retrieve valuable knowledge from unstructured form of textual documents. Extracted knowledge presents in the user understandable form of facts and knowledge. Text mining further classified into information retrieval, NLP, statistics, web mining and Intention mining. An intention is a human mental state represents a current or future action. Intention mining is an up-and-coming research area of text mining describes what actually customer wants and what actions he can take in future. It explicitly finds what people want to happen not just what they like or dislike. This paper aims to take a deep insight on intention mining by categorizing user intentions like sale / purchase, wish, emotional, search and real-time intentions. It will help the upcoming researchers to take a view on research have done on intention mining and compare the adopted approaches to find the optimal method. User may express the intention implicitly or explicitly. Explicit intention is the direct explosion of user's wishes which can easily detect from text documents. Implicit intentions communicated indirectly by user in perspective of other features of related object. Multiple classification, clustering, keyword based and machine learning techniques are used on different datasets to extract the user intentions. It is analyzed that now a days the most frequent used dataset for intention mining is micro blog tweets and frequent used techniques are support vector machine and Naïve Bayes with maximum accuracy rate.
... The mainstream research on intention mining lies in the domain of information retrieval (Jathava et al., 2011), (Baeza Yates et al., 2006) (González-Caro & Baeza-Yates, 2011), (Hashemi et al., 2008), (Sadikov et al., 2010), (Strohmaier & Kröll, 2012), (Zheng et al., 2002). Other applications have also been published, e.g. ...
... A quick search in the literature reveals that (a) many intention mining techniques have already been proposed, and (b) this research area is extremely dynamic with new contributions continuously published. Rather than aiming at a systematic literature review, this section first introduces the area by describing three particular approaches that were selected because of their impact or originality: (Strohmaier & Kröll, 2012), (Baeza et al., 2006) and (Outmazgin & Soffer, 2013). ...
... Strohmaier & Kröll, 2012 Strohmaier and Kröll's method is one of the many approaches to acquire knowledge about human intentions (the word used here is "goal") by investigating web engine query logs. The idea is that better understanding the rationale behind the actions of web engine users can be useful to deal with a range of issues such as recognizing users' intentions, reasoning about them, or generating plans to help them achieve their intentions. ...
... In the information retrieval context, the key idea is better understanding the rationale behind the users' activities through Web engine. This can be useful to deal with a range of issues such as, recognizing users' intentions, reasoning about them, or generating plans to help users to achieve their intentions [Strohmaier 2012, Hashemi 2008, Baeza-Yates 2006, Park 2010, Jethava 2011, González-Caro 2011. Most of the intention mining techniques focus on mining individual intentions out of Web engine queries. ...
... The salient feature of Strohmaier and Kröll's approach is that, it differentiates between implicit and explicit intentions [Strohmaier 2012]. Implicit intentions underlie what is expressed by people or can be observed from them. ...
... • Discovery: intention mining techniques mainly deal with the intention discovery problem [Strohmaier 2012, Hashemi 2008, Baeza-Yates 2006, Park 2010, Jethava 2011, González-Caro 2011. Discovery of intentions allows understanding how humans' think, how humans' brains work, identifying the users' intents behind their activities. ...
Article
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... The mainstream research on intention mining lies in the domain of information retrieval (Jathava et al., 2011), (Baeza Yates et al., 2006) (González-Caro & Baeza-Yates, 2011), (Hashemi et al., 2008), (Sadikov et al., 2010), (Strohmaier & Kröll, 2012), (Zheng et al., 2002). Other applications have also been published, e.g. ...
... A quick search in the literature reveals that (a) many intention mining techniques have already been proposed, and (b) this research area is extremely dynamic with new contributions continuously published. Rather than aiming at a systematic literature review, this section first introduces the area by describing three particular approaches that were selected because of their impact or originality: (Strohmaier & Kröll, 2012), (Baeza et al., 2006) and (Outmazgin & Soffer, 2013). ...
... Strohmaier & Kröll, 2012 Strohmaier and Kröll's method is one of the many approaches to acquire knowledge about human intentions (the word used here is "goal") by investigating web engine query logs. The idea is that better understanding the rationale behind the actions of web engine users can be useful to deal with a range of issues such as recognizing users' intentions, reasoning about them, or generating plans to help them achieve their intentions. ...
Article
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Understanding people's goals is a challenging issue that is met in many different areas such as security, sales, information retrieval, etc. Intention Mining aims at uncovering intentions from observations of actual activities. While most Intention Mining techniques proposed so far focus on mining individual intentions to analyze web engine queries, this paper proposes a generic technique to mine intentions from activity traces. The proposed technique relies on supervised learning and generates intentional models specified with the Map formalism. The originality of the contribution lies in the demonstration that it is actually possible to reverse engineer the underlying intentional plans built by people when in action, and specify them in models e.g. with intentions at different levels, dependencies, links with other concepts, etc. After an introduction on intention mining, the paper presents the Supervised Map Miner Method and reports two controlled experiments that were undertaken to evaluate precision, recall and F-Score. The results are promising since the authors were able to find the intentions underlying the activities as well as the corresponding map process model with satisfying accuracy, efficiency and performance.
... Guo et al. [5] attempted to differentiate between search intents by using interaction features such as mouse movements or scrolling behavior. In previous work Strohmaier et al. [13] showed that search query logs represented a viable, yet largely untapped, source for acquiring knowledge about human goals. ...
... By ''trivial to identify'' Kirsh means the ability to make a decision in constant time. This definition was adapted from previous work ( [13]) to serve the specific needs of our research. ...
Conference Paper
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Since more and more people use the micro-blogging platform Twitter to convey their needs and desires, it has become a particularly interesting medium for the task of identifying commercial activities. Potential buyers and sellers can be contacted directly thereby opening up novel perspectives and economic possibilities. By detecting commercial intent in tweets, this work is considered a first step to bring together buyers and sellers. In this work, we present an automatic method for detecting commercial intent in tweets where we achieve reasonable precision 57% and recall 77% scores. In addition, we provide insights into the nature and characteristics of tweets exhibiting commercial intent thereby contributing to our understanding of how people express commercial activities on Twitter.
... feature request, opinion asking, problem discovery, solution proposal, information giving, etc.). Baeza-Yates et al. (2006), Strohmaier and Kröll (2012) have proposed the development of specific approaches that work for the automatic identification of the user's interest. Baum and Eagon (1967) developed a new strategy to model and mine the captured intentions of camcorder users using digital video recorders and home video data. ...
Article
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Process Mining focused only on the activity-oriented process and neglected the users’ behaviors behind the activities, which led to overlooking the reality that they proposed to create. Recognizing the users’ underlying intentions can improve the guidance and offer better recommendations. As a result, an area of study known as Intention Mining has been merged. It aims at discovering the users’ behaviors using an event log. The intention is frequently used in different computer science research fields, including requirements definition, business process, and method engineering for context adaption. This paper reviews Intention-Oriented Process Mining based on event logs in the information systems engineering field. The objective is to identify the different models, methodologies, and algorithms proposed, the tools used, and the different challenges in these fields based on the four steps of review for the selection process, which start with the identification, followed by the screening, the eligibility, and the inclusion. For the first time, we are focused on Process Mining and intention mining based on log files and their relationship to get an idea about the area of intention mining. This paper reviews academic papers that are published in peer-reviewed venues from 2013 to 2022. These papers were examined through six main investigate questions and a systematic review. Also, we detailed the existing approaches in the Intention Mining area and present our comparative study. The results of the existing approaches indicate that Intention Mining shows a meaningful trace of research and creates existing opportunities for real technical applications.
... Dai et al. (2006) first proposed to identify search queries that contain online commercial intention. Since queries do not carry much information, much research extends the queries by including information extracted from search logs (Strohmaier and Kröll 2012), click through behavior data (Ashkan and Clarke 2009b), and users' mouse movements behaviors data (Guo and Agichtein 2010). ...
Article
Social media platforms are often used by people to express their needs and desires. Such data offer great opportunities to identify users’ consumption intention from user-generated contents, so that better tailored products or services can be recommended. However, there have been few efforts on mining commercial intents from social media contents. In this paper, we investigate the use of social media data to identify consumption intentions for individuals. We develop a Consumption Intention Mining Model (CIMM) based on convolutional neural network (CNN), for identifying whether the user has a consumption intention. The task is domain-dependent, and learning CNN requires a large number of annotated instances, which can be available only in some domains. Hence, we investigate the possibility of transferring the CNN mid-level sentence representation learned from one domain to another by adding an adaptation layer. To demonstrate the effectiveness of CIMM, we conduct experiments on two domains. Our results show that CIMM offers a powerful paradigm for effectively identifying users’ consumption intention based on their social media data. Moreover, our results also confirm that the CNN learned in one domain can be effectively transferred to another domain. This suggests that a great potential for our model to significantly increase effectiveness of product recommendations and targeted advertising.
... Online commercial intention identification This task is to identify online commercial intention from queries, documents or tweets. Most studies focus on capturing commercial intent by analyzing search queries (Dai et al. 2006;Strohmaier and Kröll 2012) or click-through (Ashkan and Clarke 2009). Chen et al. (2013) aims at identifying intents expressed in posts of forums. ...
Article
In this paper, we propose to study the problem of identifying and classifying tweets into intent categories. For example, a tweet “I wanna buy a new car” indicates the user’s intent for buying a car. Identifying such intent tweets will have great commercial value among others. In particular, it is important that we can distinguish different types of intent tweets. We propose to classify intent tweets into six categories, namely Food & Drink, Travel, Career & Education, Goods & Services, Event and Activities and Trifle. We propose a semisupervised learning approach to categorizing intent tweets into the six categories.We construct a test collection by using a bootstrap method. Our experimental results show that our approach is effective in inferring intent categories for tweets.
... The smart systems are able to gain people's knowledge about new ways of executing tasks or simply people's expectations for their behaviour (Strohmaier, 2012). Knowledge acquired directly from people, even if they are affording to attain much more knowledge by using machine learning, is often an essential skill for smart systems. ...
Chapter
Knowledge representation is of immense importance in the field of artificial intelligence and natural language processing. The representation of knowledge goes hand in hand with automated reasoning as one of the key goals of representing knowledge effectively is being able to reason about it. Researchers of knowledge representation and reasoning have built techniques and methods that are the main source of development in computer science and have made tremendous progress in a wide variety of real-life applications, ranging from natural language processing to robotics and software engineering. Further research is required in order to allow a more active role in guiding the reasoning process through the knowledge representation framework. This article has discussed knowledge representation and reasoning and analyzed the major challenges and new opportunities where novel knowledge representation and reasoning research have had a major impact.
... Weak Supervision Data Collection. Inspired by past work [57] that demonstrated search engine queries can contain explicit user objectives (e.g., get rid of belly fat), we first reify every goal in the goal taxonomy with a set of seed queries. For example, we manually generate queries such as "how to be charismatic" and "how to meet new friends" to elicit the high-ordered goal of being likeable, making friends, drawing others near. ...
Preprint
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Motives or goals are recognized in psychology literature as the most fundamental drive that explains and predicts why people do what they do, including when they browse the web. Although providing enormous value, these higher-ordered goals are often unobserved, and little is known about how to leverage such goals to assist people's browsing activities. This paper proposes to take a new approach to address this problem, which is fulfilled through a novel neural framework, Goal-directed Web Browsing (GoWeB). We adopt a psychologically-sound taxonomy of higher-ordered goals and learn to build their representations in a structure-preserving manner. Then we incorporate the resulting representations for enhancing the experiences of common activities people perform on the web. Experiments on large-scale data from Microsoft Edge web browser show that GoWeB significantly outperforms competitive baselines for in-session web page recommendation, re-visitation classification, and goal-based web page grouping. A follow-up analysis further characterizes how the variety of human motives can affect the difference observed in human behavioral patterns.
... This is an important task widely applicable in goal-oriented dialog systems, conversation analysis and online advertisement, and supervised learning methods [5][6][7][8][9][10][11][12] are typically adopted to learn classifiers from labeled intent datasets. According to the different application scenarios, intent recognition can be categorized into (1) query intent classification (e.g., a search engine [13][14][15]); (2) intent identification from social media (e.g. Twitter messages) [16,17]; (3) user intent understanding in a dialog system [6,18,19]. ...
Preprint
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Intent understanding plays an important role in dialog systems, and is typically formulated as a supervised classification problem. However, it is challenging and time-consuming to design the intent labels manually to support a new domain. This paper proposes an unsupervised two-stage approach to discover intents and generate meaningful intent labels automatically from a collection of unlabeled utterances. In the first stage, we aim to generate a set of semantically coherent clusters where the utterances within each cluster convey the same intent. We obtain the utterance representation from various pre-trained sentence embeddings and present a metric of balanced score to determine the optimal number of clusters in K-means clustering. In the second stage, the objective is to generate an intent label automatically for each cluster. We extract the ACTION-OBJECT pair from each utterance using a dependency parser and take the most frequent pair within each cluster, e.g., book-restaurant, as the generated cluster label. We empirically show that the proposed unsupervised approach can generate meaningful intent labels automatically and achieves high precision and recall in utterance clustering and intent discovery.
... Other uses for query logs include personalization of search results, search terms autocompletion, and correction of spelling errors in user queries, among others. For example, query logs have been used for acquiring knowledge about search user higher-level goals (Strohmaier and All, 2012); sampling for improved query probing in noncooperative distributed information retrieval environments (Shokouhi et al., 2007); capturing the hidden semantics of search queries (Bing et al., 2018); and discovering children's web search behavior (Duarte Torres et al., 2010). ...
Chapter
This chapter presents a tutorial introduction to modern information retrieval concepts, models, and systems. It begins with a reference architecture for the current Information Retrieval (IR) systems, which provides a backdrop for rest of the chapter. Text preprocessing is discussed using a mini Gutenberg corpus. Next, a categorization of IR models is presented followed by Boolean IR model description. Positional index is introduced, and execution of phrase and proximity queries is discussed. Various term weighting schemes are discussed next followed by descriptions of three IR models—Vector Space, Probabilistic, and Language models. Approaches to evaluating IR systems are presented. Relevance feedback techniques as a means to improving retrieval effectiveness are described. Various IR libraries, frameworks, and test collections are indicated. The chapter concludes by outlining facets of IR research and indicating additional reading.
... As future work, we plan to evaluate the utility of the results provided by our method with other user-centered data uses of query logs, such as behavioral analysis [6] or goal extraction [42]. ...
Article
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Query logs are of great interest for data analysis. They allow characterizing user profiles, user behaviors and search habits. However, since query logs usually contain personal information, data controllers should implement appropriate data protection mechanisms before releasing them for secondary use. In the past, the anonymization of query logs was tackled from the perspective of statistical disclosure control and by relying on privacy models such as k-anonymity, which do not scale well with the high dimensionality and dynamicity of query logs. To offer better privacy protection, some authors have recently embraced the robust privacy guarantees of ɛ-differential privacy. However, this comes at the cost of limiting the number and types of analyses that can be made on the protected queries. To tackle this issue, in this paper we propose a privacy protection method for query logs that joins the flexibility and convenience of privacy-preserving data releases with the strong privacy guarantees of ɛ-differential privacy. Moreover, to retain the analytical utility of the protected query, we have put special care in capturing, managing and preserving the semantics of the queries during the protection process. The empirical experiments we report show that our method produces differentially private query logs that are more useful for analysis than related works.
... Can we teach computers to reliably and accurately understand human intentions? is of course one of the great challenges of science, and language related technology is one of the great opportunities of information technology due to the need to automatically analyze large amounts of information stored within arbitrary text sources on the internet [1]. Yet, the acquisition of knowledge about common human goals represents a major challenge [3], we attempt to make use of 43things [6] Online Social Network that contain a great wealth of information about human"s goals and how to achieve them. ...
Technical Report
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Intention detection is one of the main components of human language understanding, which allows user goals to be identified. A challenging sub-task of intention detection is building a human intention " s knowledge base. We proposed a technique that build human intentions knowledge base, which has been extracted from 43things Online Social Network. In addition, we present results from a study that focused on evaluating intent profiles generated from transcripts of Egyptian presidential candidate speeches in
... Most of them try to categorize the queries as informational, navigational and transactional as proposed by Jansen et al [32]. Given a query suggestion, efforts have been done to understand the user intention using different means like web search logs [26], [33][34][35][36], previous user's search log for same query [37], clicked pages [38], user's search session history [39], Wikipedia [40], Wordnet and Google n-gram [41]. Using search query logs for existing users to identify intention cannot guarantee the correctness of search results [37]. ...
Article
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p>Finding the required URL among the first few result pages of a search engine is still a challenging task. This may require number of reformulations of the search string thus adversely affecting user's search time. Query ambiguity and polysemy are major reasons for not obtaining relevant results in the top few result pages. Efficient query composition and data organization are necessary for getting effective results. Context of the information need and the user intent may improve the autocomplete feature of existing search engines. This research proposes a Funnel Mesh-5 algorithm (FM5) to construct a search string taking into account context of information need and user intention with three main steps 1) Predict user intention with user profiles and the past searches via weighted mesh structure 2) Resolve ambiguity and polysemy of search strings with context and user intention 3) Generate a personalized disambiguated search string by query expansion encompassing user intention and predicted query. Experimental results for the proposed approach and a comparison with direct use of search engine are presented. A comparison of FM5 algorithm with K Nearest Neighbor algorithm for user intention identification is also presented. The proposed system provides better precision for search results for ambiguous search strings with improved identification of the user intention. Results are presented for English language dataset as well as Marathi (an Indian language) dataset of ambiguous search strings. </p
... For semantic features we explore user posts on a number of words denoting sentimental (positive and negative) attitude and cognitive work with the help of LIWC dictionary [7]. Moreover, we detect phrases of users that indicate their intentions from a linguistic point of view [5,8]. Furthermore, we added modularity [6] of a snapshot where a user appears. ...
... In [7], Gupta et al. follow an approach that defines domain-specific features, such as purchase action words, using the dependency structure of sentences. In web search, Strohmaier and Kröll [15] develop a method that learns a classifier from syntactic structure of (explicit) intent phrases and constructs a knowledge base for those intents with the search results obtained from the intent phrases as queries. The knowledge base is used to mark intents in a given document according to similarity measurements. ...
Conference Paper
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Intent classification refers to the process of identifying a set of intents of interest that appear in a given document. This work considers the task of annotating travel-related reviews with travel intents that best represent the reviewer's reason for visiting the place of interest (POI). A domain-tailored word embedding model is learned to construct intent-specific feature vectors, thereby improving classification accuracy. The feasibility of multiclass intent classification is explored using an intent corpus, consisting of 6,560 labelled reviews.
... For semantic features we explore user posts on a number of words denoting sentimental (positive and negative) attitude and cognitive work with the help of LIWC dictionary [7]. Moreover, we detect phrases of users that indicate their intentions from a linguistic point of view [5,8]. Furthermore, we added modularity [6] of a snapshot where a user appears. ...
... Both are representational states, expressing a possible attitude towards a current state of affairs, occupying however different places in path that leads towards action: An intention is the result of a reflection process, pondering through different desires and perspectives, being a step closer to real action than a desire [17].Analyzing intent is orthogonal to sentiment analysis as well as opinion mining [14] and provides a different perspective about the human goals and desires. Strohmaier and Kroll [25][27] propose a novel NLP application called Intent Analysis, focusing on the extraction of goals and intentions present in textual context. Intent Analysis is similar to Sentiment Analysis; the main difference is while the former focuses on topic categorization by labeling them "positive" or "negative, the latter aims to classify text by the presence or not of an intent on its contents. ...
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Traditional approaches for process modeling usually comprise the control flow of well-structured activities that an organization performs in order to achieve its objectives. However, many processes involving decision-making and creativity do not follow a well-structured flow of activities, having rather a more ad-hoc nature at each instance. Knowledge Intensive Processes (KIP) is an example of this kind of process. It is difficult to gather information about a KIP and create a representative model, since it might vary from instance to instance due to decisions made by its participants. The contextual information of each activity - as well as the desires and intentions of the participants - are vital to the complete understanding of the process itself. In this paper, we propose a method to extract intentions and desires from KIP participants using NLP Techniques and social media content, as well as exploring its possibilities on a real case study using Twitter.
... Moreover, one further feature was extracted while mining user posts. We detect phrases of users that indicate their intentions from a linguistic point of view [8,13]. ...
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Understanding fluctuation of users help stakeholders to provide a better support to communities. Below we present an experiment where we detect communities, their evolution and based on the data characterize users that stay, leave or join a community. Using a resulted feature set and logistic regression we operate with models of users that are joining and users that are staying in a community. In the related work we emphasize a number of features we will include in our future experiments to enhance train accuracy. This work represents a ?first from a series of experiments devoted to user fluctuation in communities.
... Over the past years, there has been an increasing awareness of the user"s goals and intentions and such information has been proven important in a variety of applications which support information search, retrieval (Rose and Levinson, 2004;Strohmeier, 2008;Strohmeier and Kröll, 2012) and social networking (43Things Website mentioned earlier). ...
Chapter
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This paper presents a hierarchical taxonomy of human goals, based on similarity judgments of 135 goals gleaned from the literature. Women and men in 3 age groups—17–30, 25–62, and 65 and older—sorted the goals into conceptually similar groups. These were cluster analyzed and a taxonomy of 30 goal clusters was developed for each age group separately and for the total sample. The clusters were conceptually meaningful and consistent across the 3 samples. The broadest distinction in each sample was between interpersonal or social goals and intrapersonal or individual goals, with interpersonal goals divided into family-related and more general social goals. Further, the 30 clusters were organized into meaningful higher order clusters. The role of such a taxonomy in promoting theory development and research is discussed, as is its relationship to other organizations of human goals and to the Big Five structure of personality.
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In this paper, we define and present a comprehensive classification of user intent for Web searching. The classification consists of three hierarchical levels of informational, navigational, and transactional intent. After deriving attributes of each, we then developed a software application that automatically classified queries using a Web search engine log of over a million and a half queries submitted by several hundred thousand users. Our findings show that more than 80% of Web queries are informational in nature, with about 10% each being navigational and transactional. In order to validate the accuracy of our algorithm, we manually coded 400 queries and compared the results from this manual classification to the results determined by the automated method. This comparison showed that the automatic classification has an accuracy of 74%. Of the remaining 25% of the queries, the user intent is vague or multi-faceted, pointing to the need for probabilistic classification. We discuss how search engines can use knowledge of user intent to provide more targeted and relevant results in Web searching.
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People interact with interfaces to accomplish goals, and knowledge about human goals can be useful for building intelligent user interfaces. We suggest that modeling high, human-level goals like "repair my credit score", is especially useful for coordinating workflows between interfaces, automated planning, and building introspective applications. We analyzed data from 43Things.com, a website where users share and discuss goals and plans in natural language, and constructed a goal network that relates what goals people have with how people solve them. We then label goals with specific details, such as where the goal typically is met and how long it takes to achieve, facilitating plan and goal recognition. Lastly, we demonstrate a simple application of goal networks, deploying it in a mobile, location-aware to-do list application, ToDoGo, which uses goal networks to help users plan where and when to accomplish their desired goals.
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We consider here the problem of building a never-ending language learner; that is, an intelligent computer agent that runs forever and that each day must (1) extract, or read, information from the web to populate a growing structured knowledge base, and (2) learn to perform this task better than on the previous day. In particular, we propose an approach and a set of design principles for such an agent, describe a partial implementation of such a system that has already learned to extract a knowledge base containing over 242,000 beliefs with an estimated precision of 74% after running for 67 days, and discuss lessons learned from this preliminary attempt to build a never-ending learning agent. Copyright © 2010, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
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The identification of the user’s intention or interest through queries that they submit to a search engine can be very useful to offer them more adequate results. In this work we present a framework for the identification of user’s interest in an automatic way, based on the analysis of query logs. This identification is made from two perspectives, the objectives or goals of a user and the categories in which these aims are situated. A manual classification of the queries was made in order to have a reference point and then we applied supervised and unsupervised learning techniques. The results obtained show that for a considerable amount of cases supervised learning is a good option, however through unsupervised learning we found relationships between users and behaviors that are not easy to detect just taking the query words. Also, through unsupervised learning we established that there are categories that we are not able to determine in contrast with other classes that were not considered but naturally appear after the clustering process. This allowed us to establish that the combination of supervised and unsupervised learning is a good alternative to find user’s goals. From supervised learning we can identify the user interest given certain established goals and categories; on the other hand, with unsupervised learning we can validate the goals and categories used, refine them and select the most appropriate to the user’s needs.
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In this research, we investigate a methodology to classify automatically Web queries by topic and user intent. Taking a 20,000 plus Web query data set sectioned by topic, we manually classified each query using a three-level hierarchy of user intent. We note that significant differences in user intent across topics. Results show that user intent (informational, navigational, and transactional) varies by topic (15 to 24 percent depending on the category). We then use this manually classified data set to classify searches in a Web search engine query stream automatically, using an exact match followed by n-gram approach. These approaches have the advantage of being implementable in real time for query classification of Web searches. The implications are that a search engine can improve retrieval performance by more effectively identifying the intent underlying user queries.
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The automatic removal of suffixes from words in English is of particular interest in the field of information retrieval. An algorithm for suffix stripping is described, which has been implemented as a short, fast program in BCPL. Although simple, it performs slightly better than a much more elaborate system with which it has been compared. It effectively works by treating complex suffixes as compounds made up of simple suffixes, and removing the simple suffixes in a number of steps. In each step the removal of the suffix is made to depend upon the form of the remaining stem, which usually involves a measure of its syllable length.
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Conceptual modeling has been fundamental to the management of structured data. However, its value is increasingly being recognized for knowledge management in general. In trying to develop suitable conceptual models for unstructured information, issues such as the level of representation and complexity of processing techniques arise. Here, we investigate the use of a conceptual model that is simple enough to allow efficient automatic extraction from documents. Our model focused on the problem-solution relationship that is central to the analysis of scientific papers. It also consists of supporting relationships such as benefits and drawbacks, assumptions, methods, extensions, and claims. Our study considered two kinds of documents - scientific research papers and patents. We evaluated the utility of the approach by building a prototype system and our user evaluation shows promising results.
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The KnowItAll system aims to automate the tedious process of extracting large collections of facts (e.g., names of scientists or politicians) from the Web in an unsupervised, domain-independent, and scalable manner. The paper presents an overview of KnowItAll's novel architecture and design principles, emphasizing its distinctive ability to extract information without any hand-labeled training examples. In its first major run, KnowItAll extracted over 50,000 class instances, but suggested a challenge: How can we improve KnowItAll's recall and extraction rate without sacrificing precision?This paper presents three distinct ways to address this challenge and evaluates their performance. Pattern Learning learns domain-specific extraction rules, which enable additional extractions. Subclass Extraction automatically identifies sub-classes in order to boost recall (e.g., “chemist” and “biologist” are identified as sub-classes of “scientist”). List Extraction locates lists of class instances, learns a “wrapper” for each list, and extracts elements of each list. Since each method bootstraps from KnowItAll's domain-independent methods, the methods also obviate hand-labeled training examples. The paper reports on experiments, focused on building lists of named entities, that measure the relative efficacy of each method and demonstrate their synergy. In concert, our methods gave KnowItAll a 4-fold to 8-fold increase in recall at precision of 0.90, and discovered over 10,000 cities missing from the Tipster Gazetteer.
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In this research, we investigated whether a learning process has unique information searching characteristics. The results of this research show that information searching is a learning process with unique searching characteristics specific to particular learning levels. In a laboratory experiment, we studied the searching characteristics of 72 participants engaged in 426 searching tasks. We classified the searching tasks according to Anderson and Krathwohl’s taxonomy of the cognitive learning domain. Research results indicate that applying and analyzing, the middle two of the six categories, generally take the most searching effort in terms of queries per session, topics searched per session, and total time searching. Interestingly, the lowest two learning categories, remembering and understanding, exhibit searching characteristics similar to the highest order learning categories of evaluating and creating. Our results suggest the view of Web searchers having simple information needs may be incorrect. Instead, we discovered that users applied simple searching expressions to support their higher-level information needs. It appears that searchers rely primarily on their internal knowledge for evaluating and creating information needs, using search primarily for fact checking and verification. Overall, results indicate that a learning theory may better describe the information searching process than more commonly used paradigms of decision making or problem solving. The learning style of the searcher does have some moderating effect on exhibited searching characteristics. The implication of this research is that rather than solely addressing a searcher’s expressed information need, searching systems can also address the underlying learning need of the user.
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We survey many of the measures used to describe and evaluate the efficiency and effectiveness of large-scale search services. These measures, herein visualized versus verbalized, reveal a domain rich in complexity and scale. We cover six principle facets of search: the query space, users' query sessions, user behavior, operational requirements, the content space, and user demographics. While this paper focuses on measures, the measurements themselves raise questions and suggest avenues of further investigation.
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Many users are familiar with the interesting but limited functionality of Data Detector interfaces like Microsoft's Smart Tags and Google's AutoLink. In this paper we significantly expand the breadth and functionality of this type of user interface through the use of large-scale knowledge bases of semantic information. The result is a Web browser that is able to generate personalized semantic hypertext, providing a goal-oriented browsing experience. We present (1) Creo, a Programming by Example system for the Web that allows users to create a general-purpose procedure with a single example, and (2) Miro, a Data Detector that matches the content of a page to high-level user goals.
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Knowing the intent of a search query allows for more intelligent ways of retrieving relevant search results. Most of the recent work on automatic detection of query intent uses supervised learning methods that require a substantial amount of labeled data; manually collecting such data is often time-consuming and costly. Human computation is an active research area that includes studies of how to build online games that people enjoy playing, while in the process providing the system with useful data. In this work, we present the design principles behind a new game called Intentions, which aims to collect data about the intent behind search queries.
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In most previous work on personalized search algorithms, the results for all queries are personalized in the same manner. However, as we show in this paper, there is a lot of variation across queries in the benefits that can be achieved through personalization. For some queries, everyone who issues the query is looking for the same thing. For other queries, different people want very different results even though they express their need in the same way. We examine variability in user intent using both explicit relevance judgments and large-scale log analysis of user behavior patterns. While variation in user behavior is correlated with variation in explicit relevance judgments the same query, there are many other factors, such as result entropy, result quality, and task that can also affect the variation in behavior. We characterize queries using a variety of features of the query, the results returned for the query, and people's interaction history with the query. Using these features we build predictive models to identify queries that can benefit from personalization. Categories and Subject Descriptors