Computer Sciences

Automatic extraction of semantic information from text and links in Web pages is key to improving the quality of search results. However, the assessment of automatic semantic measures is limited by the coverage of user studies, which do not scale with the size, heterogeneity, and growth of the Web. Here we propose to leverage human-generated metadata -namely topical directories -to measure semantic relationships among massive numbers of pairs of Web pages or topics. The Open Directory Project classifies millions of URLs in a topical ontology, providing a rich source from which semantic relationships between Web pages can be derived. While semantic similarity measures based on taxonomies (trees) are well studied, the design of well-founded similarity measures for objects stored in the nodes of arbitrary ontologies (graphs) is an open problem. This paper defines an information-theoretic measure of semantic similarity that exploits both the hierarchical and non-hierarchical structure of an ontology. An experimental study shows that this measure improves significantly on the traditional taxonomy-based approach. This novel measure allows us to address the general question of how text and link analyses can be combined to derive measures of relevance that are in good agreement with semantic similarity. Surprisingly, the traditional use of text similarity turns out to be ineffective for relevance ranking.

Electronic concept mapping tools empower experts to play an active role in the knowledge capture process, and provide a medium for building richly connected multimedia knowledge models-sets of linked concept maps and resources about a particular domain. Knowledge models are intended to be used as a means for sharing knowledge among humans, not as carefully-crafted knowledge bases upon which machines will be performing inference. However, users must still confront the questions of what to include in a concept map and which concept maps to include in a knowledge model. This paper describes ongoing research on methods to provide content-based support to users as they extend concept maps by adding concepts and propositions, and as they select topics for new maps. The goal is to provide scaffolding for experts as they build their own concept maps, link their maps to others', and decide how to extend their knowledge models. The paper presents three approaches which start from a concept map under construction and mine related information-both from prior concept maps, and from the web-to propose information to aid the user's knowledge capture and knowledge construction. The paper begins with a brief summary of the concept mapping process and the CmapTools concept mapping software. It then presents three types of implemented suggesters, to suggest concepts, propositions, concept maps, and new topics to aid experts using the CmapTools, and describes preliminary experiments to assess their performance. It closes with a discussion of next steps for testing and refining these methods.

In the last years several specialized techniques for improving web search have been developed. Most existing approaches are still limited, mainly due to the absence of qualitative criteria for ranking results and insensitivity to user preferences for guiding the search. At the same time, defeasible argumentation evolved as a successful approach in AI to model commonsense qualitative reasoning with applications in many areas, such as agent theory, knowledge engineering and legal reasoning. This paper presents ArgueNet, a recommender system that classifies search results according to preference criteria declaratively specified by the user. The proposed approach integrates a traditional web search engine with a defeasible argumentation framework.

Web pages is key to improving the quality of search results. However, the assessment of automatic semantic measures is limited by the coverage of user studies, which do not scale with the size, heterogeneity, and growth of the Web. Here we propose to leverage human-generated metadata -namely topical directories -to measure semantic relationships among massive numbers of pairs of Web pages or topics. The Open Directory Project classifies millions of URLs in a topical ontology, providing a rich source from which semantic relationships between Web pages can be derived. While semantic similarity measures based on taxonomies (trees) are well studied, the design of well-founded similarity measures for objects stored in the nodes of arbitrary ontologies (graphs) is an open problem. This paper defines an information-theoretic measure of semantic similarity that exploits both the hierarchical and non-hierarchical structure of an ontology. An experimental study shows that this measure improves significantly on the traditional taxonomy-based approach. This novel measure allows us to address the general question of how text and link analyses can be combined to derive measures of relevance that are in good agreement with semantic similarity. Surprisingly, the traditional use of text similarity turns out to be ineffective for relevance ranking.

In recent years we have witnessed the wide-spread evolution of support tools that operate in association with the user to accomplish a range of computer-mediated tasks. Two examples of these tools are critics and recommenders. Critics are cooperative tools that observe the user interacting with a computer system and present reasoned opinions about a product under development. Recommender systems are tools that assist users by facilitating access to relevant items. At the same time, defeasible argumentation has evolved as a successful approach in AI to model commonsense qualitative reasoning, with applications in many areas, such as agent theory, knowledge engineering and legal reasoning. This paper presents a novel approach towards the integration of user support systems, such as critics and recommender systems, with a defeasible argumentation framework. The final goal is to enhance practical reasoning capabilities of current user support tools by incorporating argument-based qualitative inference.

GiveALink is a public site where users donate their bookmarks to the Web community. Bookmarks are analyzed to build a new generation of Web mining techniques and new ways to search, recommend, surf, personalize and visualize the Web. We present a semantic similarity measure for URLs that takes advantage both of the hierarchical structure of the bookmark files of individual users, and of collaborative filtering across users. We analyze the social bookmark network induced by the similarity measure. A search and recommendation system is built from a number of ranking algorithms based on prestige, generality, and novelty measures extracted from the similarity data.

In spite of the significant evolution of spelling and grammar checkers for word-processing software, the problem of judging the appropriateness of language usage in different contexts remains to a large extent still unsolved. This paper presents a novel, argumentative approach to providing proactive assistance for language usage assessment on the basis of the web linguistic corpus. A defeasible argumentation system determines if a given expression is ultimately acceptable by analyzing a defeasible logic program which encodes the user's preferences. Those expressions assessed as unsuitable are further inspected automatically to help the user make the necessary repairs.

Concept maps capture knowledge about the concepts and concept relationships in a domain, using a two-dimensional visually-based representation. Computer tools for concept mapping empower experts to directly construct, navigate, share, and criticize rich knowledge models. This paper describes ongoing research on augmenting concept mapping tools with systems to support the user by proactively suggesting relevant concepts and associated resources (e.g., images, video, and text pages) during concept map creation. Providing such support requires efficient and effective algorithms for judging concept similarity and the relevance of prior concepts to new concept maps. We discuss key issues for such algorithms and present four new approaches developed for assessing conceptual similarity for concepts in concept maps. Two use precomputed summaries of structural and correlational information to determine the relevance of stored concepts to selected concepts in a new concept map, and two use information about the context in which the selected concept appears. We close by discussing their tradeoffs and their relationships to research in areas such as information retrieval and analogical reasoning. £

Much intelligent user interfaces research addresses the problem of providing information relevant to a current user topic. However, little work addresses the complementary question of helping the user identify potential topics to explore next. In knowledge acquisition, this question is crucial to deciding how to extend previouslycaptured knowledge. This paper examines requirements for effective topic suggestion and presents a domain-independent topicgeneration algorithm designed to generate candidate topics that are novel but related to the current context. The algorithm iteratively performs a cycle of topic formation, Web search for connected material, and context-based filtering. An experimental study shows that this approach significantly outperforms a baseline at developing new topics similar to those chosen by an expert for a handcoded knowledge model.

Electronic concept mapping tools provide a flexible vehicle for constructing concept maps, linking concept maps to other concept maps and related resources, and distributing concept maps to others. As electronic concept maps are constructed, it is often helpful for users to consult additional resources, in order to jog their memories or to locate resources to link to the map under construction. The World Wide Web provides a rich range of resources for these tasks-if the right resources can be found. This paper presents ongoing research on how to automatically generate Web queries from concept maps under construction, in order to proactively suggest related information to aid concept mapping. First, it examines how concept map structure and content can be exploited to automatically select terms to include in initial queries, based on studies of (1) how concept map structure influences human judgments of concept importance, and (2) the relative value of including information from concept labels and linking phrases. Second, it examines how a concept map can be used to refine future queries by reinforcing the weights of terms that have proven to be good discriminators for the topic of the concept map. The described methods are being applied to developing "intelligent suggesters" to support the concept mapping process.

The understanding of the immense and intricate topological structure of the World Wide Web (WWW) is a major scientific and technological challenge. This has been recently tackled by characterizing the properties of its representative graphs, in which vertices and directed edges are identified with Web pages and hyperlinks, respectively. Data gathered in large-scale crawls have been analyzed by several groups resulting in a general picture of the WWW that encompasses many of the complex properties typical of rapidly evolving networks. In this article, we report a detailed statistical analysis of the topological properties of four different WWW graphs obtained with different crawlers. We find that, despite the very large size of the samples, the statistical measures characterizing these graphs differ quantitatively, and in some cases qualitatively, depending on the domain analyzed and the crawl used for gathering the data. This spurs the issue of the presence of sampling biases and structural differences of Web crawls that might induce properties not representative of the actual global underlying graph. In short, the stability of the widely accepted statistical description of the Web is called into question. In order to provide a more accurate characterization of the Web graph, we study statistical measures beyond the degree distribution, such as degree-degree correlation functions or the statistics of reciprocal connections. The latter Article 10 / 2 • M.Ángeles Serrano et al.

Concept mapping is widely used in educational and other settings to aid knowledge construction, sharing, and comparison; concept maps are also used as a vehicle for assessing understanding. To aid the concept mapping process, projects at Indiana University and the Institute for Human & Machine Cognition (IHMC) are developing "intelligent suggesters" to support users as they build concept maps, by presenting them with relevant information from existing knowledge models and the Internet. This depends on identifying important concepts in the concept map under construction. This paper presents and evaluates models of the influence of concept map layout and structure on the selection of concepts expected to be relevant to the topic of concept maps. It presents and assesses a set of potentially-relevant structural factors and evaluates how these factors combine to affect human judgments of concept importance. Twenty subjects were asked to judge the relative importance of concepts in concept maps selected to highlight particular characteristics, and three models were compared to their judgments. Analysis of the results shows that subjects were significantly influenced by concept map topology, but little influenced by other aspects of concept map layout. The results suggest that layout-independent models of concept maps can provide a suitable representation for guiding retrieval of topicrelevant information to support concept map construction, provided that the representation reflects topologically-based influences. The results are applied in the design of the suggesters' similarity assessment procedures for retrieving relevant concept maps.

Effective knowledge management may require going beyond initial knowledge capture, to support decisions about how to extend previously-captured knowledge. Electronic concept maps, interlinked with other concept maps and multimedia resources, can provide rich knowledge models for human knowledge capture and sharing. This paper presents research on methods for supporting experts as they extend these knowledge models, by searching the Web for new context-relevant topics as candidates for inclusion. This topic search problem presents two challenges: First, how to formulate queries to seek topics that reflect the context of the current knowledge model, and, second, how to identify candidate topics with the right balance of novelty and relevance. More generally, this problem raises the broad question of the interaction of topic information from the local analysis space (a collected set of documents) and the global search space (the Web). The paper develops a framework for understanding this interaction, and proposes and evaluates techniques for addressing the query formation and topic identification questions by dynamically extracting topic descriptors and discriminators from a knowledge model, to characterize information needs for retrieval and filtering of relevant material. Using these techniques, we have developed a support tool that starts from a knowledge model under construction and automatically produces a set of suggestions for topics to include, proactively supporting users as they extend knowledge models.

In recent years, AI research has started to take seriously the role of context in developing models. This trend is heralded by a departure from logic-based views and their over-emphasis on the formal aspects of thought processes. The departure, however, has not been complete| some fundamental assumptions of formal logic are still maintained in the new approaches. Similarly, \relevance" remains a major challenge for AI research. This paper outlines an alternative proposal that takes context and relevance as intertwined aspects of thought and intelligence. We argue for a pragmatic approach, which shows better promise than formal logic in dealing with such issues.

Successful knowledge management may depend not only on knowledge capture, but on knowledge construction-on formulating new and useful knowledge that was not previously available. Electronic concept mapping tools are a promising method for supporting knowledge capture and construction, but users may find it difficult to determine the right knowledge to include. Consequently, knowledge-based methods for suggesting relevant information are desirable for supporting the knowledge modeling process. We are developing methods to aid concept mapping by suggesting relevant information to compare, contrast, and possibly include in knowledge models represented as concept maps. This paper presents two specific methods we are developing for this task, both of which automatically identify topics related to a concept map in order to guide the retrieval of related information. The first, DISCERNER, automatically organizes concept map libraries into a hierarchical structure of topic categories and subcategories that are used as indices for efficient access to relevant stored concept maps. The second, EXTENDER, characterizes the topics of concept maps under construction, applies clustering techniques to the resulting information, and performs incremental web-mining for new but related, topics. It suggests these topics as potential areas for extending the existing concept map or to include in new maps to increase current knowledge coverage.

Proactive retrieval systems monitor a user's task context and automatically provide the user with related resources. The effectiveness of such systems depends on their ability to perform context-based retrieval, generating queries which return context-relevant results. Two factors make this task especially challenging for Web-based retrieval. First, the quality of Web retrieval can be strongly affected by the vocabulary used to generate the queries. If the system's vocabulary for describing the context differs from the vocabulary used in the resources themselves, relevant resources may be missed. Second, search engine restrictions on query length may make it difficult to include sufficient contextual information in a single query. This paper presents an algorithm, IACS (Incremental Algorithm for Context-Based Search), which addresses these problems by building up, applying, and refining partial context descriptions incrementally. In IACS, an initial term-based context description is the starting point for a cycle of mining search engines, performing context-based filtering of results, and refining context descriptions to generate new rounds of queries in an expanded vocabulary. IACS has been applied in a system for proactively supporting concept-map-based knowledge modeling, by retrieving resources relevant to target concepts in the context of the rich information provided by "in progress" concept maps. An evaluation of the system shows that it provides significant improvements over a baseline for retrieving context-relevant resources. We expect the algorithm to have broad applicability to context-based Web retrieval for rich contexts.

The understanding of the immense and intricate topological structure of the World Wide Web (WWW) is a major scientific and technological challenge. This has been tackled recently by characterizing the properties of its representative graphs in which vertices and directed edges are identified with web-pages and hyperlinks, respectively. Data gathered in large scale crawls have been analyzed by several groups resulting in a general picture of the WWW that encompasses many of the complex properties typical of rapidly evolving networks . In this paper, we report a detailed statistical analysis of the topological properties of four different WWW graphs obtained with different crawlers. We find that, despite the very large size of the samples, the statistical measures characterizing these graphs differ quantitatively, and in some cases qualitatively, depending on the domain analyzed and the crawl used for gathering the data. This spurs the issue of the presence of sampling biases and structural differences of Web crawls that might induce properties not representative of the actual global underlying graph. In order to provide a more accurate characterization of the Web graph and identify observables which are clearly discriminating with respect to the sampling process, we study the behavior of degree-degree correlation functions and the statistics of reciprocal connections. The latter appears to enclose the relevant correlations of the WWW graph and carry most of the topological information of the Web. The analysis of this quantity is also of major interest in relation to the navigability and searchability of the Web.