OpenRaiser/Intern-Atlas huggingface dataset:
Is formalizing events necessary for full exploitation
Authors: Antonio Badia
Venue: CIKM
Year: 2010

Part of what makes event extraction difficult is the lack of a clear definition of the event concept. We argue that a good definition would be a positive contribution to Information Extraction and its applications. We give a preliminary discussion of a logical definition to stimulate discussion.
A method for discovering components of human rituals from streams of sensor data
Authors: Athanasios Bamis, Jia Fang, Andreas Savvides
Venue: CIKM
Year: 2010

This paper describes an algorithm for determining if an event occurs persistently within an interval where the interval is periodic but the event is not. The goal of the algorithm is to identify events with this property and also determine the minimum interval in which they occur. This solution is geared towards discovering human routines by considering the triggering of simple sensors over a diverse set of spatial and temporal scales. After describing the problem and the proposed solution, in this paper we demonstrate using testbed data and simulations that this approach uncovers components of routines by identifying which events are parts of the same routine through their temporal properties.
XReal: an interactive XML keyword searching
Authors: Zhifeng Bao, Jiaheng Lu, Tok Wang Ling
Venue: CIKM
Year: 2010

Keyword search over XML data usually brings irrelevant results especially when the keywords in a user query have ambiguities. We demonstrate a statistic-based approach to identify the search targets and constraints of a user query in the presence of keyword ambiguities, and come out a relevance oriented result ranking scheme called XML TF*IDF. Since the search intention of a same query may even vary from user to user, we provide an interactive search strategy by allowing user to simply tick their desired search targets from a list of suggestions recommended by the search engine. In this way, we can acquire more precise results and also take the burden of learning the schema of XML data off users.
Summarizing biological literature with BioSumm
Authors: Elena Baralis, Alessandro Fiori
Venue: CIKM
Year: 2010

BioSumm is a summarization environment that supports user queries on online repositories of scientific publications by providing abstract descriptions of focused document groups. The summarization approach is driven by a grading function which evaluates the occurrences of domain dictionary terms.
Online update of b-trees
Authors: Marina Barsky, Alex Thomo, Zoltan Toth, Calisto Zuzarte
Venue: CIKM
Year: 2010

Many scenarios impose a heavy update load on B-tree indexes in modern databases. A typical case is when B-trees are used for indexing all the keywords of a text field. For example upon the insertion of a new text record (e.g. a new document arrives), a barrage of new keywords has to be inserted into the index causing many random disk I/Os and interrupting the normal operation of the database. The common approach has been to collect the updates in a separate structure and then perform a batch update of the index. This update "freezes" the database. Many applications, however, require the immediate availability of the new updates without any interruption of the normal database operation. In this paper we present a novel online B-tree update method based on a new buffering data structure we introduce - Dynamic Bucket Tree (DBT). The DBT-buffer serves as a differential index for new updates. The grouping of keys in DBT-buffer is based on the longest common prefixes (LCP) of their binary representations. The LCP is used as a measure of the locality of keys to be transferred to the main B-tree. Our online update system does not slow down concurrent user transactions or lead to degradation of search performance. Experiments confirm that our DBT buffer can be efficiently used for online updates of text fields. As such it represents an effective solution to the notorious problem of handling updates to an Inverted Index.
Summary of the 4th workshop on analytics for noisy unstructured text data (AND)
Authors: Roberto Basili, Daniel P. Lopresti, Christoph Ringlstetter, Shourya Roy, Klaus U. Schulz, L. Venkata Subramaniam
Venue: CIKM
Year: 2010

Noisy unstructured text data is ubiquitous in real-world communication. Natural language and the creative ways that humans use it can create problems for computational techniques. Electronic text from the Internet (emails, message boards, newsgroups, blogs, microblogs, wikis, chatlogs and web pages), contact centers (complaints, emails, call transcriptions, message summaries), and mobile phones (SMS) is often noisy – contains spelling errors, abbreviations, non-standard words, false starts, repetitions, missing punctuation, missing case information and special characters. Informal communications are not the only source of noisy text; Text produced by processing signals intended for human use such as printed/handwritten documents, spontaneous speech, and camera-captured scene images, are also noisy.
A tool for ontology-editing and ontology-based information exploration
Authors: Feza Baskaya, Jaana Kekäläinen, Kalervo Järvelin
Venue: CIKM
Year: 2010

Users of traditional information retrieval (IR) systems encounter the problems of vocabulary mismatch and fuzzy search goals. This is due to the number of ways the search concepts may be expressed in texts. The effects of the vocabulary mismatch can be alleviated via query expansion and document annotation through thesauri, tags or ontologies. On the one hand, users would benefit from ontology-based annotation. On the other hand for overcoming fuzzy search goals users need to analyze the search results and reformulate their queries. In the present paper we suggest ontologies for retrieval and annotation and describe a tool supporting online ontology editing and ontology-based multilingual document exploration. It provides an aid for multiple-source searching and document analysis through ontology based summarization, annotation and clustering, among others.
Constructing classification features using minimal predictive patterns
Authors: Iyad Batal, Milos Hauskrecht
Venue: CIKM
Year: 2010

Choosing good features to represent objects can be crucial to the success of supervised machine learning methods. Recently, there has been a great interest in applying data mining techniques to construct new classification features. The rationale behind this approach is that patterns (feature-value combinations) could capture more underlying semantics than single features. Hence the inclusion of some patterns can improve the classification performance. Currently, most methods adopt a two-phases approach by generating all frequent patterns in the first phase and selecting the discriminative patterns in the second phase. However, this approach has limited success because it is usually very difficult to correctly identify important predictive patterns in a large set of highly correlated frequent patterns.
Exploiting co-occurrence and information quality metrics to recommend tags in web 2.0 applications
Authors: Fabiano Muniz Belém, Eder Ferreira Martins, Jussara Marques de Almeida, Marcos André Gonçalves, Gisele L. Pappa
Venue: CIKM
Year: 2010

This work addresses the task of recommending high quality tags by exploiting not only previously assigned tags, but also terms extracted from other textual features (e.g., title and description) associated with the target object.To estimate the quality of a candidate tag recommendation, we use several metrics related to both tag co-occurrence and information quality. We also propose a heuristic function to combine the metrics to produce a final ranking of the recommended tags. We evaluate our heuristic function in various scenarios, for three popular Web 2.0 applications. Our experimental results indicate that our heuristic function significantly outperforms two state-of-the-art tag recommendation algorithms.
Online stratified sampling: evaluating classifiers at web-scale
Authors: Paul N. Bennett, Vitor R. Carvalho
Venue: CIKM
Year: 2010

Deploying a classifier to large-scale systems such as the web requires careful feature design and performance evaluation. Evaluation is particularly challenging because these large collections frequently change. In this paper we adapt stratified sampling techniques to evaluate the precision of classifiers deployed in large-scale systems. We investigate different types of stratification strategies, and then we derive a new online sampling algorithm that incrementally approximates the theoretical optimal disproportionate sampling strategy. In experiments, the proposed algorithm significantly outperforms both simple random sampling as well as other types of stratified sampling, with an average reduction of about 20% in labeling effort to reach the same confidence and interval-bounds on precision
Learning ontology resolution for document representation and its applications in text mining
Authors: Lidong Bing, Bai Sun, Shan Jiang, Yan Zhang, Wai Lam
Venue: CIKM
Year: 2010

It is well known that synonymous and polysemous terms often bring in some noises when calculating the similarity between documents. Existing ontology-based document representation methods are static, hence, the chosen semantic concept set for representing a document has a fixed resolution and it is not adaptable to the characteristics of a document collection and the text mining problem in hand. We propose an Adaptive Concept Resolution (ACR) model to overcome this issue. ACR can learn a concept border from an ontology taking into consideration of the characteristics of a particular document collection. Then this border can provide a tailor-made semantic concept representation for a document coming from the same domain. Another advantage of ACR is that it is applicable in both classification task where the groups are given in the training document set, and clustering task where no group information is available. Furthermore, the result of this model is not sensitive to the model parameter. The experimental results show that ACR outperforms an existing static method significantly.
FALCON: seamless access to meeting data from the inbox and calendar
Authors: Peter Bjellerup, Karl J. Cama, Mukundan Desikan, Yi Guo, Ajinkya Kale, Jennifer C. Lai, Nizar Lethif, Jie Lu, Mercan Topkara, Stephan H. Wissel
Venue: CIKM
Year: 2010

We present a system that supports seamless access to information contained in recorded meetings from the cornerstone points of a knowledge worker's daily life: mailbox and calendar. The solution supports granular search of meeting content from an enterprise email system and automatically displays recordings of meetings related to the message the user is currently viewing. Additionally thumbnail summaries of the meetings are added to the user's calendar entries after the meetings have taken place. Lastly our system supports easy sharing of videos associated with recorded meetings through the use of hot-linked thumbnail summaries which can be sent via email.
Building efficient multi-threaded search nodes
Authors: Carolina Bonacic, Carlos García, Mauricio Marín, Manuel Prieto-Matías, Francisco Tirado
Venue: CIKM
Year: 2010

Search nodes are single-purpose components of large Web search engines and their efficient implementation is critical to sustain thousands of queries per second and guarantee individual query response times within a fraction of a second. Current technology trends indicate that search nodes ought to be implemented as multi-threaded multi-core systems. The straightforward solution that system designers can apply in this case is simply to follow standard practice by deploying one asynchronous thread per active query in the node and attaching each thread to a different core. Each concurrent thread is responsible for sequentially processing a single query at a time. The only potential source of read/write conflicts among threads are the accesses to the different application caches present in the search node. However, new Web applications pose much more demanding requirements in terms of read/write conflicts than recent past applications since now data updates must take place concurrently with query processing. Insisting on the same paradigm of concurrent threads now augmented with a transaction concurrency control protocol is a feasible solution. In this paper we propose a more efficient and much simpler solution which has the additional advantage of enabling a very efficient administration of application caches. We propose performing relaxed bulk-synchronous parallelism at multi-core level.
SPac: a distributed, peer-to-peer, secure and privacy-aware social space
Authors: Angela Bonifati, Wendy Hui Wang, Ruilin Liu
Venue: CIKM
Year: 2010

To support privacy-aware management of data in social spaces, the user personal data needs to be stored at each user device, and shared only with a trusted subset of other users. To date, social spaces only have fairly limited access control capabilities, that do not protect the possibly sensitive data of the users. In this demonstration, we showcase our SPAC system, a distributed, peer-to-peer, secure and privacy-aware social space system. SPAC is equipped with: (i) an SQL-based declarative distributed query language to specify which data to share and whom to share with. Such a language guarantees the fine-grained access to the data, (ii) a fully-decentralized authorization that relies on classic cryptographic protocols to provide robust and resilient key-based encryption for access control enforcement, and (iii) an update-friendly access control mechanism, that also addresses the updates on both the network and the access control policies.
A semantic annotation framework for retrieving and analyzing observational datasets
Authors: Shawn Bowers, Huiping Cao, Mark Schildhauer, Matthew B. Jones, Ben Leinfelder, Margaret O&apos, Brien
Venue: CIKM
Year: 2010

In many scientific disciplines, including ecology, hydrology, and earth science, scientific analysis requires access to a broad range of observational data. However, because of the amount and heterogeneity both in the structure and semantics) of observational data, approaches are needed that allow scientists to easily discover and analyze them. To address this issue, we describe a framework for accessing sobservational data. This framework combines a core observational model, domain-specific ontologies compatible with the core model, and a semantic annotation language. The annotation language provides a formal bridge between the core model and the underlying data to enable queries and analysis over annotations. The framework has been implemented to take advantage of ontology and web-based standards, and has also been integrated within a popular metadata tool for managing ecological datasets.
Exploiting site-level information to improve web search
Authors: Andrei Z. Broder, Evgeniy Gabrilovich, Vanja Josifovski, George Mavromatis, Donald Metzler, Jane Wang
Venue: CIKM
Year: 2010

Ranking Web search results has long evolved beyond simple bag-of-words retrieval models. Modern search engines routinely employ machine learning ranking that relies on exogenous relevance signals. Yet the majority of current methods still evaluate each Web page out of context. In this work, we introduce a novel source of relevance information for Web search by evaluating each page in the context of its host Web site. For this purpose, we devise two strategies for compactly representing entire Web sites. We formalize our approach by building two indices, a traditional page index and a new site index, where each "document" represents the an entire Web site. At runtime, a query is first executed against both indices, and then the final page score for a given query is produced by combining the scores of the page and its site. Experimental results carried out on a large-scale Web search test collection from a major commercial search engine confirm the proposed approach leads to consistent and significant improvements in retrieval effectiveness.
Ranking related entities: components and analyses
Authors: Marc Bron, Krisztian Balog, Maarten de Rijke
Venue: CIKM
Year: 2010

Related entity finding is the task of returning a ranked list of homepages of relevant entities of a specified type that need to engage in a given relationship with a given source entity. We propose a framework for addressing this task and perform a detailed analysis of four core components; co-occurrence models, type filtering, context modeling and homepage finding. Our initial focus is on recall. We analyze the performance of a model that only uses co-occurrence statistics. While this method identifies the potential set of related entities, it fails to rank them effectively. Two types of error emerge: (1) entities of the wrong type pollute the ranking and (2) while somehow associated to the source entity, some retrieved entities do not engage in the right relation with it. To address (1), we add type filtering based on category information available in Wikipedia. To correct for (2), we complement our related entity finding method with contextual information, represented as language models derived from documents in which source and target entities co-occur. To complete the pipeline, we find homepages of top ranked entities by combining a language modeling approach with heuristics based on Wikipedia's external links. Our method achieves very high recall scores on the end-to-end task, providing a solid starting point for expanding our focus to improve precision. Our framework can effectively incorporate additional heuristics and these extensions lead to state-of-the-art performance.
Toward the design of a methodology to predict relevance through multiple sources of evidence
Authors: Emanuele Di Buccio, Massimo Melucci
Venue: CIKM
Year: 2010

Textual queries, often short and ambiguous, can be insufficient when describing complex user information needs. Since users are reluctant or unable to provide long or precise descriptions, a possible solution to the low Information Retrieval (IR) system relevance prediction capability is to exploit diverse sources of evidence which are available during the search process. One of the open problems of the combination of diverse sources of evidence is the need of a uniform formalism which seamlessly describes the sources and the document ranking function within a single model. To this end, this paper discusses an IR view which explicitly considers other sources in addition to the information need and the document, and proposes a methodology to exploit them to support feedback. The IR view is described using the Entity-Relationship (ER) model which allows us to view the sources as properties of entities -- e.g. of the entity information need, document, or user -- or of their relationships.
Digging for knowledge with information extraction: a case study on human gene-disease associations
Authors: Markus Bundschus, Anna Bauer-Mehren, Volker Tresp, Laura Inés Furlong, Hans-Peter Kriegel
Venue: CIKM
Year: 2010

We present the information extraction system Text2SemRel. The system (semi-) automatically constructs knowledge bases from textual data consisting of facts about entities using semantic relations. An integral part of the system is a graph-based interactive visualization and search layer. The second contribution in this paper is the presentation of a case study on the (semi-) automatic construction of a knowledge base consisting of gene-disease associations. The resulting knowledge base, the Literature-derived Human Gene-Disease Network (LHGDN), is now an integral part of the Linked Life Data initiative and represents currently the largest publicly available gene-disease repository. The LHGDN is compared against several curated state of the art databases. A unique feature of the LHGDN is that the semantics of the associations constitute a wide variety of biomolecular conditions.
Search-log anonymization and advertisement: are they mutually exclusive?
Authors: Thorben Burghardt, Klemens Böhm, Achim Guttmann, Chris Clifton
Venue: CIKM
Year: 2010

The revenue of search-engine providers strongly depends on targeted advertisement. Targeted advertisement is becoming more reliant on personal data. This puts user privacy at risk. One way to improve privacy is to anonymize search logs, but this reduces usefulness for ad placement. Further, the usefulness depends on the target function used for the anonymization. This paper is the first to study this tradeoff systematically. We quantify the usefulness of an anonymized search log for advertisement purposes, by estimating outcomes such as the number of clicks on ads or the number of ad impressions possible after anonymization. A main result is that anonymized search logs are still useful for advertisement purposes, but the extent strongly depends on the target function.
Personalized search by tag-based user profile and resource profile in collaborative tagging systems
Authors: Yi Cai, Qing Li
Venue: CIKM
Year: 2010

With the increase of resource-sharing web sites such as YouTube1 and Flickr2, personalized search becomes more important and challenging, as users demand higher retrieval quality. To achieve this goal, personalized search needs to take users' personalized profiles and information needs into consideration. Collaborative tagging (also known as folksonomy [11]) systems allow users to annotate resources with their own tags, which provide a simple but powerful way for organizing, retrieving and sharing different types of social resources. In this paper, we examine the limitations of previous tag-based personalized search. To handle these limitations, we propose a new method to model user profiles and resource profiles in a collaborative tagging environment. A novel search method using such users' and resources' profiles is proposed to facilitate the desired personalization in resource search. We implement a prototype system named as FMRS. Experiments using FMRS data set and MovieLens data set show that our proposed method outperforms baseline methods.
Path-hop: efficiently indexing large graphs for reachability queries
Authors: Jing Cai, Chung Keung Poon
Venue: CIKM
Year: 2010

Graph reachability is a fundamental research problem that finds its use in many applications such as geographic navigation, bioinformatics, web ontologies and XML databases, etc. Given two vertices, u and v, in a directed graph, a reachability query asks if there is a directed path from u to v. Over the last two decades, many indexing schemes have been proposed to support reachability queries on large graphs. Typically, those schemes based on chain or tree covers work well when the graph is sparse. For dense graphs, they still have fast query time but require large storage for their indices. In contrast, the 2-Hop cover and its variations/extensions produce compact indices even for dense graphs but have slower query time than those chain/tree covers. In this paper, we propose a new indexing scheme, called Path-Hop, which is even more space-efficient than those schemes based on 2-Hop cover and yet has query processing speed comparable to those chain/tree covers. We conduct extensive experiments to illustrate the effectiveness of our approach relative to other state-of-the-art methods.
Search engine support for software applications
Authors: Jamie Callan
Venue: CIKM
Year: 2010

Question-answering, computer-assisted language learning, text mining, and other software applications that use a full-search engine to find information in a large text corpus are becoming common. A software application may use metadata and text annotations to reduce the mismatch between the concept-based representations convenient for inference and the word-based representations typically used for text retrieval. Software applications may also be able to specify detailed requirements that retrieved passages must satisfy. This use of text search is very different than the ad-hoc, interactive search that information retrieval research typically studies.