Attribute value extraction from product profiles is essential for many applications such as product retrieval, comparison, and recommendation. While existing techniques focus mainly on the extraction task, none of them deals with the problem of correcting wrong attribute values. In this paper we propose CAVE, a novel system for attribute correction and enrichment using the Question Answering (QA) paradigm. CAVE learns information from both titles and attribute tables, using encoder and language models to correct attribute values. It also has the capability to enrich existing product descriptions with new attribute values extracted from titles. To the best of our knowledge, CAVE is the first system that allows users to experiment with a number of powerful QA models and compare their performances on attribute values correction using real-word datasets.

Most news summarization techniques are static, and thus do not satisfy user needs in having summaries with specific structures or details. Meanwhile, existing dynamic techniques such as query-based summarization fail to handle content-independent queries that target the type of summary information such as time, location, reasons, and consequences of reported events. The NEWS system supports multi-granular summarization along two dimensions: the level of detail and type of information. The system employs fine-grained information extraction to extract facts and their facets with type tagging. The extracted information is then modeled as a graph used to create summaries. The system incrementally expands summaries based on the nodes visited by users, folding related events into the search space.

Recent knowledge extraction methods are moving towards ternary and higher-arity relations to capture more information about binary facts. An example is to include the time, the location, and the duration of a specific fact. These relations can be even more complex to extract in advanced domains such as news, where events typically come with different facets including reasons, consequences, purposes, involved parties, and related events. The main challenge consists in first finding the set of facets related to each fact, and second tagging those facets to the relevant category.

The PIKM workshop offers Ph.D. students the opportunity to bring their work to an international and interdisciplinary research community, and create a network of young researchers to exchange and develop new and promising ideas. Similar to the CIKM, the PIKM workshop covers a wide range of topics in the areas of databases, information retrieval and knowledge management.

News websites give their users the opportunity to participate in discussions about published articles, by writing comments. Typically, these comments are unstructured making it hard to understand the flow of user discussions. Thus, there is a need for organizing comments to help users to (1) gain more insights about news topics, and (2) have an easy access to comments that trigger their interests. In this work, we address the above problem by organizing comments around the entities and the aspects they discuss. More specifically, we propose an approach for entity and aspect extraction from user comments through the following contributions. First, we extend traditional Named-Entity Recognition approaches, using coreference resolution and external knowledge bases, to detect more occurrences of entities in comments. Second, we exploit part-of-speech tag, dependency tag, and lexical databases to extract explicit and implicit aspects around discussed entities. Third, we evaluate our entity and aspect extraction approach, on manually annotated data, showing that it highly increases precision and recall compared to baseline approaches.

PIKM workshop offers to Ph.D. students the possibility to bring their work to an international and interdisciplinary research community, and create a network of young researchers to exchange and develop new and promising ideas. Similarly to the CIKM, PIKM workshop covers a wide range of topics in the areas of databases, information retrieval and knowledge management.

Chemotherapy is used to treat cancer by killing malignant cells or stopping them from multiplying. However, it can also harm healthy cells, which causes side effects. Some of the side effects of chemotherapy do not pose a serious threat to patients' health. But, some others can be very serious such as the rapid fall in white blood cells making the patient vulnerable to serious infections. Different approaches have been proposed in the literature to predict the probability of experiencing a certain side effect on a specified day of each cycle of the chemotherapy. In our work, we are interested in predicting the side effects a patient is more likely to experience in each cycle. To this end, we take a different approach where we propose a predictive model based on patient clinical information, such as concomitant diseases and medications. We have analyzed data from from 75 patients under FOLFOX chemotherapy for colon cancer. The results show that our model improves the prediction accuracy compared to previously proposed time-based approaches. The goal of this work is to help healthcare professionals in identifying possible side effects before starting a chemotherapy and taking the necessary actions to improve the quality of the treatment.

A query topic can be subjective involving a variety of opinions, judgments, arguments, and many other debatable aspects. Typically, search engines process queries independently from the nature of their topics using a relevance-based retrieval strategy. Hence, search results about subjective topics are often biased towards a specific view point or version. In this demo, we shall present MOUNA, a novel approach for opinion diversification. Given a query on a subjective topic, MOUNA ranks search results based on three scores: (1) relevance of documents, (2) semantic diversity to avoid redundancy and capture the different arguments used to discuss the query topic, and (3) sentiment diversity to cover a balanced set of documents having positive, negative, and neutral sentiments about the query topic. Moreover, MOUNA enhances the representation of search results with a summary of the different arguments and sentiments related to the query topic. Thus, the user can navigate through the results and explore the links between them. We provide an example scenario in this demonstration to illustrate the inadequacy of relevance-based techniques for searching subjective topics and highlight the innovative aspects of MOUNA. A video showing the demo can be found in http://www.youtube.com/user/mounakacimi/videos .

Diversifying search results of queries seeking for different view points about controversial topics is key to improving satisfaction of users. The challenge for finding different opinions is how to maximize the number of discussed arguments without being biased against specific sentiments. This paper addresses the issue by first introducing a new model that represents the patterns occurring in documents about controversial topics. Second, proposing an opinion diversification model that uses (1) relevance of documents, (2) semantic diversification to capture different arguments and (3) sentiment diversification to identify positive, negative and neutral sentiments about the query topic. We have conducted our experiments using queries on various controversial topics and applied our diversification model on the set of documents returned by Google search engine. The results show that our model outperforms the native ranking of Web pages about controversial topics by a significant margin.

While images of famous people and places are abundant on the Internet, they are much harder to retrieve for less popular entities such as notable computer scientists or regionally interesting churches. Querying the entity names in image search engines yields large candidate lists, but they often have low precision and unsatisfactory recall. In this paper, we propose a principled model for finding images of rare or ambiguous named entities. We propose a set of efficient, light-weight algorithms for identifying entity-specific keyphrases from a given textual description of the entity, which we then use to score candidate images based on the matches of keyphrases in the underlying Web pages. Our experiments show the high precision-recall quality of our approach.

Knowledge-sharing communities like Wikipedia and automated extraction methods like those of DBpedia enable the construction of large machine-processible knowledge bases with relational facts about entities. These endeavors lack multimodal data like photos and videos of people and places. While photos of famous entities are abundant on the Internet, they are much harder to retrieve for less popular entities such as notable computer scientists or regionally interesting churches. Querying the entity names in image search engines yields large candidate lists, but they often have low precision and unsatisfactory recall.

Online communities have become popular for publishing and searching content, and also for connecting to other users. User-generated content includes, for example, personal blogs, bookmarks, and digital photos. Items can be annotated and rated by different users, and users can connect to others that are usually friends and/or share common interests.

Online communities have become popular for publishing and searching content, as well as for finding and connecting to other users. User-generated content includes, for example, personal blogs, bookmarks, and digital photos. These items can be annotated and rated by different users, and these social tags and derived user-specific scores can be leveraged for searching relevant content and discovering subjectively interesting items. Moreover, the relationships among users can also be taken into consideration for ranking search results, the intuition being that you trust the recommendations of your close friends more than those of your casual acquaintances.

Online communities like Flickr, del.icio.us and YouTube have established themselves as very popular and powerful services for publishing and searching contents, but also for identifying other users who share similar interests. In these communities, data are usually annotated with carefully selected and often semantically meaningful tags, collaboratively chosen by the user who uploaded an item and other users who came across the item. Items like urls or videos are typically retrieved by issueing queries that consist of a set of tags, returning items that have been frequently annotated with these tags. However, users often prefer a more personalized way of searching over such a 'global' search, exploiting preferences of and connections between users. The SENSE system presented in this demo supports hybrid personalization along two dimensions: in the social dimension, a search process is focused towards items tagged by users explicitly selected as friends by the querying user, whereas in the spiritual dimension, users that share preferences with the querying user are preferred. Orthorgonal to this, the system additionally integrates semantic expansion of query tags to improve search results. SENSE provides an efficient top-k algorithm that dynamically expands the search to related users and tags. It is based on principles of threshold algorithms, folding related users and tags into the search space in an incremental on-demand manner, thus visiting only a small fraction of the social network when evaluating a query. The demonstration uses three different real-world datasets: a large set of urls from del.icio.us, a large set of pictures from Flickr, and a large set of books from librarything, each together with a large fraction of the corresponding social network of these sites.

P2P systems represent a large portion of the Internet traffic which makes the data discovery of great importance to the user and the broad Internet community. Hence, the power of a P2P system comes from its ability to provide an efficient search service. In this paper we address the problem of similarity search in a Hybrid Overlay P2P Network which organizes data and peers in a high dimensional feature space. Data and peers are described by a set of features and clustered using a density-based algorithm. We experimentally evaluate the effectiveness of the similarity-search using uniform and zipf data distribution.

P2P systems are increasingly used to discover and share various data between users. The performance of a P2P based information retrieval system is determined by the efficiency of locating information and the precision with which the retrived data corresponds to the submitted query. In this paper, we address the problem of similarity search by providing a combined peer organizing and data organizing scheme for approximate similarity search in a Hybrid Overlay P2P Network. Through extensive simulations, we show the efficiency of the similarity search in our approach using a density-based algorithm to cluster data and peers in a high dimensional feature space.