OpenRaiser/Intern-Atlas huggingface dataset:
An experimental study of the impact of information extraction accuracy on semantic search performance
Authors: Jennifer Chu-Carroll, John M. Prager
Venue: CIKM
Year: 2007

Researchers have shown that various natural language processing techniques can be used in document analysis to impact search performance. For the most part, they examined how an analysis system with certain performance characteristics can be leveraged to improve document and/or passage search results. We have previously shown that semantic queries which utilize named entity and relation information extracted from the corpus can lead to significant improvement in search performance. In this paper, we extend our previous efforts and examine how search performance degrades in the face of imperfect named entity and relation extraction. Our study was carried out by developing gold standard annotated corpora and applying different error models to the gold standard annotations to simulate errors made by automatic recognizers. We identify automatic recognizer characteristics that make them more amenable to our search tasks, show that recognizer recall has more significant impact on semantic search performance than its precision, and demonstrate that significant improvement in both MAP and Exact Precision scores can be achieved by adopting automatic named entity and relation recognizers with near state-of-the-art performance.
The t4sql temporal query language
Authors: Carlo Combi, Angelo Montanari, Giuseppe Pozzi
Venue: CIKM
Year: 2007

Time characterizes every aspect of our life and its management when storing and querying data is very important. In this paper we propose a new temporal query language, called T4SQL, supporting multiple temporal dimensions of data. Besides the well-known valid and transaction times, it encompasses two additional temporal dimensions, namely, availability and event times. The availability time records when information is known and treated as true by the information system; the event times record the occurrence times of both the event that starts the valid time and the event that ends it. T4SQL is capable to deal with different temporal semantics (atemporal aka non-sequenced, current, sequenced, next) with respect to every temporal dimension. Moreover, T4SQL provides a novel temporal grouping clause and an orthogonal management of temporal properties when defining the selection condition(s) and the schema for the output relation.
Spam filtering for short messages
Authors: Gordon V. Cormack, José María Gómez Hidalgo, Enrique Puertas Sanz
Venue: CIKM
Year: 2007

We consider the problem of content-based spam filtering for short text messages that arise in three contexts: mobile (SMS) communication, blog comments, and email summary information such as might be displayed by a low-bandwidth client. Short messages often consist of only a few words, and therefore present a challenge to traditional bag-of-words based spam filters. Using three corpora of short messages and message fields derived from real SMS, blog, and spam messages, we evaluate feature-based and compression-model-based spam filters. We observe that bag-of-words filters can be improved substantially using different features, while compression-model filters perform quite well as-is. We conclude that content filtering for short messages is surprisingly effective.
Effective ranked conceptual retrieval
Authors: Amjad M. Daoud
Venue: CIKM
Year: 2007

We describe the design and implementation of the SMARTER prototype system for collecting, structuring and searching information derived from full text ebooks available on the World Wide Web [4]. Standard methods of parsing and extended Boolean schemes for weighting are used to build an index of individual pages. Ranked pages are retrieved from the system by means of a correlated and combined p-norm and vector query mechanisms. Furthermore, we present a complete evaluation of the relative effectiveness of the SMARTER system versus the Google Desktop [7].
Perfect hash functions for large dictionaries
Authors: Amjad M. Daoud
Venue: CIKM
Year: 2007

We describe a new practical algorithm for finding perfect hash functions with no specification space at all, suitable for key sets ranging in size from small to very large. The method is able to find perfect hash functions for various sizes of key sets in linear time. The perfect hash functions produced are optimal in terms of time (perfect) and require at most computation of h1(k) and h2(k); two simple auxiliary pseudorandom functions.
RDF data exploration and visualization
Authors: Leonidas Deligiannidis, Krys J. Kochut, Amit P. Sheth
Venue: CIKM
Year: 2007

We present Paged Graph Visualization (PGV), a new semi-autonomous tool for RDF data exploration and visualization. PGV consists of two main components: a) the "PGV explorer" and b) the "RDF pager" module utilizing BRAHMS, our high per-formance main-memory RDF storage system. Unlike existing graph visualization techniques which attempt to display the entire graph and then filter out irrelevant data, PGV begins with a small graph and provides the tools to incrementally explore and visualize relevant data of very large RDF ontologies. We implemented several techniques to visualize and explore hot spots in the graph, i.e. nodes with large numbers of immediate neighbors. In response to the user-controlled, semantics-driven direction of the exploration, the PGV explorer obtains the necessary sub-graphs from the RDF pager and enables their incremental visualization leaving the previously laid out sub-graphs intact. We outline the problem of visualizing large RDF data sets, discuss our interface and its implementation, and through a controlled experiment we show the benefits of PGV.
Leveraging semantic technologies for enterprise search
Authors: Gianluca Demartini
Venue: CIKM
Year: 2007

Enterprise search is very different from Web search (for example in the link structure or in the user's needs and goal)and some steps have been already done to exploit these differences in order to improve the effectiveness of enterprise search. In this paper we present the state of the art of the enterprise search field with some open issues. We also present a research plan that aims at using Information Retrieval, Semantic Web, and User Modelling techniques to cope with these issues improving the current state of enterprise search.
Discovering interesting usage patterns in text collections: integrating text mining with visualization
Authors: Anthony Don, Elena Zheleva, Machon Gregory, Sureyya Tarkan, Loretta Auvil, Tanya E. Clement, Ben Shneiderman, Catherine Plaisant
Venue: CIKM
Year: 2007

This paper addresses the problem of making text mining results more comprehensible to humanities scholars, journalists, intelligence analysts, and other researchers, in order to support the analysis of text collections. Our system, FeatureLens1, visualizes a text collection at several levels of granularity and enables users to explore interesting text patterns. The current implementation focuses on frequent itemsets of n-grams, as they capture the repetition of exact or similar expressions in the collection. Users can find meaningful co-occurrences of text patterns by visualizing them within and across documents in the collection. This also permits users to identify the temporal evolution of usage such as increasing, decreasing or sudden appearance of text patterns. The interface could be used to explore other text features as well. Initial studies suggest that FeatureLens helped a literary scholar and 8 users generate new hypotheses and interesting insights using 2 text collections.
Ontology module extraction for ontology reuse: an ontology engineering perspective
Authors: Paul Doran, Valentina Tamma, Luigi Iannone
Venue: CIKM
Year: 2007

Problems resulting from the management of shared, distributed knowledge has led to ontologies being employed as a solution, in order to effectively integrate information across applications. This is dependent on having ways to share and reuse existing ontologies; with the increased availability of ontologies on the web, some of which include thousands of concepts, novel and more efficient methods for reuse are being devised. One possible way to achieve efficient ontology reuse is through the process of ontology module extraction. A novel approach to ontology module extraction is presented that aims to achieve more efficient reuse of very large ontologies; the motivation is drawn from an Ontology Engineering perspective. This paper provides a definition of ontology modules from the reuse perspective and an approach to module extraction based on such a definition. An abstract graph model for module extraction has been defined, along with a module extraction algorithm. The novel contribution of this paper is a module extraction algorithm that is independent of the language in which the ontology is expressed. This has been implemented in ModTool; a tool that produces ontology modules via extraction. Experiments were conducted to compare ModTool to other modularisation methods.
A strategy for allowing meaningful and comparable scores in approximate matching
Authors: Carina F. Dorneles, Carlos A. Heuser, Viviane Moreira Orengo, Altigran Soares da Silva, Edleno Silva de Moura
Venue: CIKM
Year: 2007

The goal of approximate data matching is to assess whether two distinct data instances represent the same real world object. This is usually achieved through the use of a similarity function, which returns a score that defines how similar two data instances are. If this score surpasses a given threshold, both data instances are considered as representing the same real world object. The score values returned by a similarity function depend on the algorithm that implements the function and have no meaning to the user (apart from the fact that a higher similarity value means that two data instances are more similar). In this paper, we propose that instead of defining the threshold in terms of the scores returned by a similarity function, the user specifies the precision that is expected from the matching process. Precision is a well known quality measure and has a clear interpretation from the user's point of view. Our approach relies on mapping between similarity scores and precision values based on a training data set. Experimental results show the training may be executed against a representative data set, and reused for other databases from the same domain.
Quickmig: automatic schema matching for data migration projects
Authors: Christian Drumm, Matthias Schmitt, Hong Hai Do, Erhard Rahm
Venue: CIKM
Year: 2007

A common task in many database applications is the migration of legacy data from multiple sources into a new one. This requires identifying semantically related elements of the source and target systems and the creation of mapping expressions to transform instances of those elements from the source format to the target format. Currently, data migration is typically done manually, a tedious and timeconsuming process, which is difficult to scale to a high number of data sources. In this paper, we describe QuickMig, a new semi-automatic approach to determining semantic correspondences between schema elements for data migration applications. QuickMig advances the state of the art with a set of new techniques exploiting sample instances, domain ontologies, and reuse of existing mappings to detect not only element correspondences but also their mapping expressions. QuickMig further includes new mechanisms to effectively incorporate domain knowledge of users into the matching process. The results from a comprehensive evaluation using real-world schemas and data indicate the high quality and practicability of the overall approach.
Rk-hist: an r-tree based histogram for multi-dimensional selectivity estimation
Authors: Todd Eavis, Alex Lopez
Venue: CIKM
Year: 2007

Database query engines typically rely upon query size estimators in order to evaluate the potential cost of alternate query plans. In multi-dimensional database systems, such as those typically found in large data warehousing environments, these selectivity estimators often take the form of multi-dimensional histograms. But while single dimensional histograms have proven to be quite accurate, even in the presence of data skew, the multi-dimensional variations have generally been far less reliable. In this paper, we present a new histogram model that is based upon an r-tree space partitioning. The localization of the r-tree boxes is in turn controlled by a Hilbert space filling curve, while a series of efficient area equalization heuristics restructures the initial boxes to provide improved bucket representation. Experimental results demonstrate significantly improved estimation accuracy relative to state of the art alternatives, as well as superior consistency across a variety of record distributions.
Mapgraph: efficient methods for complex olap hierarchies
Authors: Todd Eavis, Ahmad Taleb
Venue: CIKM
Year: 2007

Online Analytical Processing is a database paradigm that provides for the rich analysis of multi-dimensional data. OLAP is often supported by a logical structure known as the Cube. However, supporting efficient OLAP query resolution in enterprise scale environments is an issue of considerable complexity. In practice, the difficulty of the problem is exacerbated by the existence of dimension hierarchies that sub-divide core dimensions into aggregation layers of varying granularity. Common hierarchy-sensitive query operations such as Rollup and Drilldown can be very costly on large cubes. Moreover, facilities for the representation of more complex hierarchical relationships are not well supported by conventional techniques. This paper presents a robust hierarchy infrastructure called mapGraph that supports the efficient and transparent manipulation of attribute hierarchies within OLAP environments. Experimental results verify that, when compared to the alternatives, very little additional overhead is introduced, even when advanced functionality is exploited.
Learning on the border: active learning in imbalanced data classification
Authors: Seyda Ertekin, Jian Huang, Léon Bottou, C. Lee Giles
Venue: CIKM
Year: 2007

This paper is concerned with the class imbalance problem which has been known to hinder the learning performance of classification algorithms. The problem occurs when there are significantly less number of observations of the target concept. Various real-world classification tasks, such as medical diagnosis, text categorization and fraud detection suffer from this phenomenon. The standard machine learning algorithms yield better prediction performance with balanced datasets. In this paper, we demonstrate that active learning is capable of solving the class imbalance problem by providing the learner more balanced classes. We also propose an efficient way of selecting informative instances from a smaller pool of samples for active learning which does not necessitate a search through the entire dataset. The proposed method yields an efficient querying system and allows active learning to be applied to very large datasets. Our experimental results show that with an early stopping criteria, active learning achieves a fast solution with competitive prediction performance in imbalanced data classification.
Randomized metric induction and evolutionary conceptual clustering for semantic knowledge bases
Authors: Nicola Fanizzi, Claudia d&apos, Amato, Floriana Esposito
Venue: CIKM
Year: 2007

We present an evolutionary clustering method which can be applied to multi-relational knowledge bases storing semantic resource annotations expressed in the standard languages for the Semantic Web. The method exploits an effective and language-independent semi-distance measure defined for the space of individual resources, that is based on a finite number of dimensions corresponding to a committee of features represented by a group of concept descriptions (discriminating features). We show how to obtain a maximally discriminating group of features through a feature construction method based on genetic programming. The algorithm represents the possible clusterings as strings of central elements (medoids, w.r.t. the given metric) of variable length. Hence, the number of clusters is not needed as a parameter since the method can optimize it by means of the mutation operators and of a proper fitness function. We also show how to assign each cluster with a newly constructed intensional definition in the employed concept language. An experimentation with some ontologies proves the feasibility of our method and its effectiveness in terms of clustering validity indices.
Evaluation of phrasal query suggestions
Authors: Alan Feuer, Stefan Savev, Javed A. Aslam
Venue: CIKM
Year: 2007

This paper evaluates the uptake and efficacy of a unified approach to phrasal query suggestions in the context of a high-precision search engine. The search engine performs ranked extended-Boolean searches with the proximity operator <scp>NEAR</scp> being the default operation. Suggestions are offered to the searcher when the length of the result list falls outside predefined bounds. If the list is too long, the engine suggests narrowing the query through the use of super phrases; if the list is too short, the engine suggests broadening the query through the use of proximal subphrases.
Improving the classification of newsgroup messages through social network analysis
Authors: Blaz Fortuna, Eduarda Mendes Rodrigues, Natasa Milic-Frayling
Venue: CIKM
Year: 2007

Improving the classification of newsgroup messages through social network analysis.
A volcano erupts: semantically mediated integration of heterogeneous volcanic and atmospheric data
Authors: Peter Fox, Deborah L. McGuinness, Robert Raskin, A. Krishna Sinha
Venue: CIKM
Year: 2007

We present a research effort into the application of semantic web methods and technologies to address the challenging problem of integrating heterogeneous volcanic and atmospheric data in support of assessing the atmospheric effects of a volcanic eruption.
Optimizing parallel itineraries for knn query processing in wireless sensor networks
Authors: Tao-Young Fu, Wen-Chih Peng, Wang-Chien Lee
Venue: CIKM
Year: 2007

Spatial queries for extracting data from wireless sensor networks are important for many applications, such as environmental monitoring and military surveillance. One such query is K Nearest Neighbor (KNN) query that facilitates sampling of monitored sensor data in correspondence with a given query location. Recently, itinerary-based KNN query processing techniques, that propagate queries and collect data along a pre-determined itinerary, have been developed concurrently [12] [14]. These research works demonstrate that itinerary-based KNN query processing algorithms are able to achieve better energy efficiency than other existing algorithms. However, how to derive itineraries based on different performance requirements remains a challenging problem. In this paper, we propose a new itinerary-based KNN query processing technique, called PCIKNN, that derives different itineraries aiming at optimizing two performance criteria, response latency and energy consumption. The performance of PCIKNN is analyzed mathematically and evaluated through extensive experiments. Experimental results show that PCIKNN has better performance and scalability than the state-of-the-art.
A CDD-based formal model for expert finding
Authors: Yupeng Fu, Rongjing Xiang, Yiqun Liu, Min Zhang, Shaoping Ma
Venue: CIKM
Year: 2007

Searching an organization's document repositories for experts is a frequently faced problem in intranet information management. This paper proposes a candidate-centered model which is referred as Candidate Description Document (CDD)-based retrieval model. The expertise evidence about an expert candidate scattered over repositories is mined and aggregated automatically to form a profile called the candidate's CDD, which represents his knowledge. We present the model from its foundations through its logical development and argue in favor of this model for expert finding. We devise and compare the different strategies for exploring a variety of expertise evidence. The experiments on TREC enterprise corpora demonstrate that the CDD-based model achieves significant and consistent improvement on performance through comparative studies with non-CDD methods.
Ntjfsatnot: a novel method for query with not-predicates on xml data
Authors: Vahid Garakani, Sayyed Kamyar Izadi, Mostafa Haghjoo, Mohammad Harizi
Venue: CIKM
Year: 2007

Previous researches, in the filed of XML databases, have been done to evaluate XML queries with AND-branches. However, as far as we know, very little work has examined the efficient processing of XML queries with NOT-predicates. Also these methods have to process all of the query nodes in the document when dealing with queries with NOT-branch. In this paper, with some modification in TJFast method, we propose a new manner for answering to various NOT-queries. This method processes nodes efficiently, in a way that in the ideal state, we obtain part of the answer after the process of each node, and we don't have any unreasonable processing of each node.
Efficient on-line index maintenance for dynamic text collections by using dynamic balancing tree
Authors: Ruijie Guo, Xueqi Cheng, Hongbo Xu, Bin Wang
Venue: CIKM
Year: 2007

Previous on-line index maintenance strategies are mainly designed for document insertions without considering document deletions. In a truly dynamic search environment, however, documents may be added to and removed from the collection at any point in time. In this paper, we examine issues of on-line index maintenance with support for instantaneous document deletions and insertions. We present a DBT Merge strategy that can dynamically adjust the sequence of sub-index merge operations during index construction, and offers better query processing performance than previous methods, while providing an equivalent level of index maintenance performance when document insertions and deletions exist in parallel. Using experiments on 426 GB of web data we demonstrate the efficiency of our method in practice, showing that on-line index construction for dynamic text collections can be performed efficiently and almost as fast as for growing text collections.
Parameter sensitivity in the probabilistic model for ad-hoc retrieval
Authors: Ben He, Iadh Ounis
Venue: CIKM
Year: 2007

The term frequency normalisation parameter sensitivity is an important issue in the probabilistic model for Information Retrieval. A high parameter sensitivity indicates that a slight change of the parameter value may considerably affect the retrieval performance. Therefore, a weighting model with a high parameter sensitivity is not robust enough to provide a consistent retrieval performance across different collections and queries. In this paper, we suggest that the parameter sensitivity is due to the fact that the query term weights are not adequate enough to allow informative query terms to differ from non-informative ones. We show that query term reweighing, which is part of the relevance feedback process, can be successfully used to reduce the parameter sensitivity. Experiments on five Text REtrieval Conference (TREC) collections show that the parameter sensitivity does remarkably decrease when query terms are reweighed.
Management and preservation of research data with iRODS
Authors: Mark Hedges, Adil Hasan, Tobias Blanke
Venue: CIKM
Year: 2007

This paper presents first steps towards implementing a data layer to support a semi-automated preservation management system for research data in the arts and humanities. We suggest to use e-Science technology and grid middleware to implement a virtualised storage system for research data. We will outline how iRODS (Rule-Oriented Data management System)can be used within an architecture to implement complex,automated,scalable digital preservation strategies.