What makes re-finding information difficult? A study of email re-finding

Re-nding information that has been seen or accessed before is a task which can be relatively straight-forward, but often it can be extremely challenging, time-consuming and frustrating. Little is known, however, about what makes one re-finding task harder or easier than another. We performed a user study to learn about the contextual factors that influence users' perception of task diculty in the context of re-finding email messages. 21 participants were issued re-nding tasks to perform on their own personal collections. The participants' responses to questions about the tasks combined with demographic data and collection statistics for the experimental population provide a rich basis to investigate the variables that can influence the perception of diculty. A logistic regression model was developed to examine the relationships be- tween variables and determine whether any factors were associated with perceived task diculty. The model reveals strong relationships between diculty and the time lapsed since a message was read, remembering when the sought-after email was sent, remembering other recipients of the email, the experience of the user and the user's ling strategy. We discuss what these findings mean for the design of re-nding interfaces and future re-finding research

3DIR: exploiting topological relationships in three-dimensional information retrieval from BIM environments

An increasing amount of information is being packed into Building Information Models, with the 3D geometrical model serving as the central index leading to other building information. The Three-Dimensional Information Retrieval (3DIR) project investigates information retrieval from such environments, where information or documents are linked to a 3D artefact. Here, the 3D visualization/geometry can be exploited when formulating information retrieval queries, computing the relevance of information items to the query, or visualizing search results. Following reviews of literature in BIM and information retrieval, a clear gap was identified in the practice of information retrieval from BIM systems. The practical need for such a system was further specified using workshops with construction professionals as end users. A software prototype was developed, built on a commercial BIM platform. The 3DIR prototype creates an index of all text attached to the 3D model. The user can search for information by selecting specific 3D objects, specifying a spherical volume of the model and/or entering search keywords. This paper focuses on the exploitation of model topology. Relationships between 3D objects are used to widen the search, whereby relevant information items linked to a related 3D object (rather than information linked directly to a 3D object selected by the user) are still retrieved but ranked lower. Several such relationships between 3D objects were tested, whether explicitly encoded in the BIM information architecture or inferred from geometrical computations. An evaluation of the software prototype which exploits such topological relationships demonstrates its effectiveness but highlights the challenges to software users of added complexity. The system is subjectively rated comparably favorably. It is concluded that care needs to be taken when exploiting topological relationships, but that a tight coupling between text-based retrieval and the 3D model is generally effective in information retrieval from 3D BIM environments

3DIR: three-dimensional information retrieval from 3D building information modelling environments.

More and more information is being packed into Building Information Models (BIMs), with the 3D geometrical model serving as the central index to lead users to the many other types of building information. The Three-Dimensional Information Retrieval (3DIR) project investigates information retrieval from such environments, where information or documents are linked to a 3D artefact. In these situations, the 3D visualisation or 3D geometry of the building can be exploited when formulating information retrieval queries, computing the relevance of information items to the query, or visualising search results. Following reviews of literature in BIM/CAD and information retrieval, a clear gap was identified in the practice of information retrieval from BIM/CAD systems. End users were consulted to ascertain the precise user requirements in such an information retrieval system. Scenario-based design was adopted to design a software prototype. The 3DIR system was developed as an add-in under the Autodesk Revit BIM platform. The 3DIR prototype creates an index of all text data attached to the 3D model. The user is able to search for information by selecting specific 3D objects, by keyword and by specifying particular 3D regions of the model. Relationships between 3D objects are also used to rank search results. Search results are displayed by highlighting 3D objects in the 3D model. Findings from the evaluation of the prototype demonstrate its usefulness but suggest directions for future development. It is concluded that a tight coupling between text-based retrieval and the 3D model is extremely effective in 3D BIM environments

Three-dimensional information retrieval (3DIR): A graph theoretic formulation for exploiting 3D geometry and model topology in information retrieval

The 3DIR project investigated the use of 3D visualization to formulate queries, compute the relevance of information items, and visualize search results. Workshops identified the user needs. Based on these, a graph theoretic formulation was created to inform the emerging system architecture. A prototype was developed. This enabled relationships between 3D objects to be used to widen a search. An evaluation of the prototype demonstrated that a tight coupling between text-based retrieval and 3D models could enhance information retrieval but add an extra layer of complexity

Three-Dimensional Information Retrieval (3DIR): exploiting 3D geometry and model topology in information retrieval from BIM environments

In building modelling environments, more and more information is being crammed into 2D/3D building and product models. This is particularly true given the rise of Building Information Modelling (BIM, Eastman et al., 2011). The Three-Dimensional Information Retrieval (3DIR) project investigates information retrieval from these environments, where information or documents are linked to a 3D building model. In these situations, the 3D visualization or 3D geometry of the building can be exploited when formulating information retrieval queries, computing the relevance of information items to the query, or visualizing search results. Managing such building information repositories in this way would take advantage of human strengths in vision, spatial cognition and visual memory (Lansdale and Edmonds, 1992; Robertson et al., 1998). Information retrieval is associated with documents, and a critic might argue that documents are relics from the pre-BIM age that are no longer relevant in the era of BIM. However, the challenge of information retrieval is pertinent whether we are dealing with documents which are coarse grains of information or building object parameters/attributes as finer grains of information. Demian and Fruchter (2005) demonstrated that traditional retrieval computations can be applied with good results to 3D building models where textual or symbolic data are treated as very short documents. In this sense, it is almost a question of semantics whether the information being retrieved comes from object properties embedded in the BIM, or from external documents linked to the BIM. The challenge remains of retrieving non-geometric or textual information. This paper describes the findings of the 3DIR project whose aim was to improve information retrieval when retrieving information or documents linked to a 3D artefact, or non-geometric information embedded in the model of the artefact. The central objective was to develop an information retrieval toolset for documents/information linked to 3D building models which exploits 3D geometry and model visualisation. Such a toolset is essentially a search engine for retrieving information with a BIM platform. As a further objective, the toolset should leverage topological relationships in the 3D model to enhance information retrieval

Knowledge management: Hype, hope, or help?

This article examines the nature of Knowledge Management—how it differs from Data Management and Information Management, and its relationship to the development of Expert Systems and Decision Support Systems. It also examines the importance of Communities of Practice and Tacit Knowledge for Knowledge Management. The discussion is organized around five explicit questions. One: What is “knowledge”? Two: Why are people, especially managers, thinking about Knowledge Management? Three: What are the enabling technologies for Knowledge Management? Four: What are the prerequisites for Knowledge Management? Five: What are the major challenges for Knowledge Management?Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/35289/1/10113_ftp.pd

Application of Common Sense Computing for the Development of a Novel Knowledge-Based Opinion Mining Engine

The ways people express their opinions and sentiments have radically changed in the past few years thanks to the advent of social networks, web communities, blogs, wikis and other online collaborative media. The distillation of knowledge from this huge amount of unstructured information can be a key factor for marketers who want to create an image or identity in the minds of their customers for their product, brand, or organisation. These online social data, however, remain hardly accessible to computers, as they are specifically meant for human consumption. The automatic analysis of online opinions, in fact, involves a deep understanding of natural language text by machines, from which we are still very far. Hitherto, online information retrieval has been mainly based on algorithms relying on the textual representation of web-pages. Such algorithms are very good at retrieving texts, splitting them into parts, checking the spelling and counting their words. But when it comes to interpreting sentences and extracting meaningful information, their capabilities are known to be very limited. Existing approaches to opinion mining and sentiment analysis, in particular, can be grouped into three main categories: keyword spotting, in which text is classified into categories based on the presence of fairly unambiguous affect words; lexical affinity, which assigns arbitrary words a probabilistic affinity for a particular emotion; statistical methods, which calculate the valence of affective keywords and word co-occurrence frequencies on the base of a large training corpus. Early works aimed to classify entire documents as containing overall positive or negative polarity, or rating scores of reviews. Such systems were mainly based on supervised approaches relying on manually labelled samples, such as movie or product reviews where the opinionist’s overall positive or negative attitude was explicitly indicated. However, opinions and sentiments do not occur only at document level, nor they are limited to a single valence or target. Contrary or complementary attitudes toward the same topic or multiple topics can be present across the span of a document. In more recent works, text analysis granularity has been taken down to segment and sentence level, e.g., by using presence of opinion-bearing lexical items (single words or n-grams) to detect subjective sentences, or by exploiting association rule mining for a feature-based analysis of product reviews. These approaches, however, are still far from being able to infer the cognitive and affective information associated with natural language as they mainly rely on knowledge bases that are still too limited to efficiently process text at sentence level. In this thesis, common sense computing techniques are further developed and applied to bridge the semantic gap between word-level natural language data and the concept-level opinions conveyed by these. In particular, the ensemble application of graph mining and multi-dimensionality reduction techniques on two common sense knowledge bases was exploited to develop a novel intelligent engine for open-domain opinion mining and sentiment analysis. The proposed approach, termed sentic computing, performs a clause-level semantic analysis of text, which allows the inference of both the conceptual and emotional information associated with natural language opinions and, hence, a more efficient passage from (unstructured) textual information to (structured) machine-processable data. The engine was tested on three different resources, namely a Twitter hashtag repository, a LiveJournal database and a PatientOpinion dataset, and its performance compared both with results obtained using standard sentiment analysis techniques and using different state-of-the-art knowledge bases such as Princeton’s WordNet, MIT’s ConceptNet and Microsoft’s Probase. Differently from most currently available opinion mining services, the developed engine does not base its analysis on a limited set of affect words and their co-occurrence frequencies, but rather on common sense concepts and the cognitive and affective valence conveyed by these. This allows the engine to be domain-independent and, hence, to be embedded in any opinion mining system for the development of intelligent applications in multiple fields such as Social Web, HCI and e-health. Looking ahead, the combined novel use of different knowledge bases and of common sense reasoning techniques for opinion mining proposed in this work, will, eventually, pave the way for development of more bio-inspired approaches to the design of natural language processing systems capable of handling knowledge, retrieving it when necessary, making analogies and learning from experience

Application of Common Sense Computing for the Development of a Novel Knowledge-Based Opinion Mining Engine

The ways people express their opinions and sentiments have radically changed in the past few years thanks to the advent of social networks, web communities, blogs, wikis and other online collaborative media. The distillation of knowledge from this huge amount of unstructured information can be a key factor for marketers who want to create an image or identity in the minds of their customers for their product, brand, or organisation. These online social data, however, remain hardly accessible to computers, as they are specifically meant for human consumption. The automatic analysis of online opinions, in fact, involves a deep understanding of natural language text by machines, from which we are still very far. Hitherto, online information retrieval has been mainly based on algorithms relying on the textual representation of web-pages. Such algorithms are very good at retrieving texts, splitting them into parts, checking the spelling and counting their words. But when it comes to interpreting sentences and extracting meaningful information, their capabilities are known to be very limited. Existing approaches to opinion mining and sentiment analysis, in particular, can be grouped into three main categories: keyword spotting, in which text is classified into categories based on the presence of fairly unambiguous affect words; lexical affinity, which assigns arbitrary words a probabilistic affinity for a particular emotion; statistical methods, which calculate the valence of affective keywords and word co-occurrence frequencies on the base of a large training corpus. Early works aimed to classify entire documents as containing overall positive or negative polarity, or rating scores of reviews. Such systems were mainly based on supervised approaches relying on manually labelled samples, such as movie or product reviews where the opinionist’s overall positive or negative attitude was explicitly indicated. However, opinions and sentiments do not occur only at document level, nor they are limited to a single valence or target. Contrary or complementary attitudes toward the same topic or multiple topics can be present across the span of a document. In more recent works, text analysis granularity has been taken down to segment and sentence level, e.g., by using presence of opinion-bearing lexical items (single words or n-grams) to detect subjective sentences, or by exploiting association rule mining for a feature-based analysis of product reviews. These approaches, however, are still far from being able to infer the cognitive and affective information associated with natural language as they mainly rely on knowledge bases that are still too limited to efficiently process text at sentence level. In this thesis, common sense computing techniques are further developed and applied to bridge the semantic gap between word-level natural language data and the concept-level opinions conveyed by these. In particular, the ensemble application of graph mining and multi-dimensionality reduction techniques on two common sense knowledge bases was exploited to develop a novel intelligent engine for open-domain opinion mining and sentiment analysis. The proposed approach, termed sentic computing, performs a clause-level semantic analysis of text, which allows the inference of both the conceptual and emotional information associated with natural language opinions and, hence, a more efficient passage from (unstructured) textual information to (structured) machine-processable data. The engine was tested on three different resources, namely a Twitter hashtag repository, a LiveJournal database and a PatientOpinion dataset, and its performance compared both with results obtained using standard sentiment analysis techniques and using different state-of-the-art knowledge bases such as Princeton’s WordNet, MIT’s ConceptNet and Microsoft’s Probase. Differently from most currently available opinion mining services, the developed engine does not base its analysis on a limited set of affect words and their co-occurrence frequencies, but rather on common sense concepts and the cognitive and affective valence conveyed by these. This allows the engine to be domain-independent and, hence, to be embedded in any opinion mining system for the development of intelligent applications in multiple fields such as Social Web, HCI and e-health. Looking ahead, the combined novel use of different knowledge bases and of common sense reasoning techniques for opinion mining proposed in this work, will, eventually, pave the way for development of more bio-inspired approaches to the design of natural language processing systems capable of handling knowledge, retrieving it when necessary, making analogies and learning from experience