Conversational Bibliographic Search is a conversational search engine designed to support users in retrieving scientific papers and authors within the domain of computer science. It enables natural language-based searches for data within the dblp computer science bibliography, which is enriched with data from Semantic Scholar. The system presents a novel user interface and bridges the gap between keyword-based search engines, faceted search systems, and generative conversational approaches. It enables users to access the latest publications, formulate intricate queries, as often required in scholarly research, and engage in multi-turn conversations to discover the most relevant results. Users can iteratively refine their queries and ask follow-up questions. Conversational Bibliographic Search actively supports the search process by posing clarification questions and providing suggestions.

In computational argumentation, so-called quality dimensions such as coherence or rhetoric are often used for ranking arguments. However, the literature often only predicts which argument is more persuasive, but not why this is the case. In this paper, we introduce AQUAPLANE, a transparent and easy-to-extend application that not only decides for a pair of arguments which one is more convincing with respect to a statement, but also provides an explanation.

To answer queries many SPARQL query processors use different sources, e.g., various knowledge bases (KBs) or end points. RESTful Web APIs are rarely the focus of those systems as they come with many limitations, like not being able to process SPARQL queries. Moreover, most existing approaches optimize their query plans only for performance, even though users often have additional preferences, e.g., coverage, reliability, or currency. Additionally, data is often provided with different levels of quality so that not all sources should be trusted equally. In this paper, we therefore present TunA, a query engine that is able to combine RESTful Web APIs and local RDF KBs in the form of triple stores while tuning its (query) plans towards user preferences. Erroneous information from Web APIs is detected using hierarchical agglomerative clustering. Our evaluation shows that TunA outperforms current state-of-the-art systems and is less vulnerable to erroneous information, even in settings where only unreliable sources are available.

Computational argumentation and especially argument mining together with retrieval enjoys increasing popularity. In contrast to standard search engines that focus on finding documents relevant to a query, argument retrieval aims at finding the best supporting and attacking premises given a query claim, e.g., from a predefined collection of arguments. Here, a claim is the central part of an argument representing the standpoint of a speaker with the goal to persuade the audience, and a premise serves as evidence to the claim. In addition to the actual retrieval process, existing work has focused on (1) classifying polarities of arguments into supporting or opposing, (2) classifying arguments by their frames (such as economic or environmental), and (3) clustering similar arguments by their meaning to avoid repetitions in the result list. For experiments, either hand-made argument collections or arguments extracted from debate portals were used. In this paper, we extend existing work on argument clustering, making the following contributions: First, we introduce a novel pipeline for clustering arguments. While previous work classified arguments either by polarity, frame, or meaning, our pipeline incorporates these three, allowing a more systematic presentation of arguments. Second, we introduce a new dataset consisting of 365 argument graphs accompanying more than 11,000 high-quality arguments that, contrary to previous datasets, have been generated, displayed, and verified by journalists and were published in newspapers. A thorough evaluation with this dataset provides a first baseline for future work.

Scientific publishing heavily relies on the assessment of quality of submitted manuscripts by peer reviewers. Assigning a set of matching reviewers to a submission is a highly complex task which can be performed only by domain experts. We introduce RevASIDE, a reviewer recommendation system that assigns suitable sets of complementing reviewers from a predefined candidate pool without requiring manually defined reviewer profiles. Here, suitability includes not only reviewers’ expertise, but also their authority in the target domain, their diversity in their areas of expertise and experience, and their interest in the topics of the manuscript. We present three new data sets for the expert search and reviewer set assignment tasks and compare the usefulness of simple text similarity methods to document embeddings for expert search. Furthermore, an quantitative evaluation demonstrates significantly better results in reviewer set assignment compared to baselines. A qualitative evaluation also shows their superior perceived quality.

With the Web augmenting every day and computers increasingly getting more powerful, research in the field of computational argumentation becomes more and more important. One of its research branches is argument retrieval, which aims at finding and presenting users the best arguments for their queries. Several systems already exist for this purpose, all having the same goal but reaching it in different ways. In line with existing work, an argument consists of a claim supported or attacked by a premise. Now that argument retrieval has become a separate task in the CLEF lab Touché, displaying the ranking is becoming increasingly important.

Argument search engines identify, extract, and rank the most important arguments for and against a given controversial topic. A number of such systems have recently been developed, usually focusing on classic information retrieval ranking methods that are based on frequency information. An important aspect that has been ignored so far by search engines is the quality of arguments. We present a quality-aware ranking framework for arguments already extracted from texts and represented as argument graphs, considering multiple established quality measures. An extensive evaluation with a standard benchmark collection demonstrates that taking quality into account significantly helps to improve retrieval quality for argument search. We also publish a dataset in which arguments with respect to topics were tediously annotated by humans with three widely accepted argument quality dimensions.

In order to create the most comprehensive RDF Knowledge Base possible, data integration is essential. Many different data sources are used to extend a given dataset or to correct errors in the data. Nowadays, Web APIs (instead of data dumps) are common external data sources, since many data providers make their data publicly available. However, the classic problems of data integration, i.e., which parts of the datasets can be mapped, remain. In addition, Web APIs are often more restrictive than data dumps and of course slower to access due to latencies and other constraints. In this paper we demonstrate the FiLiPo (Finding Linkage Points) system to automatically find connections (i.e., linkage points) between Web APIs and local Knowledge Bases in a reasonable amount of time. To this end, we developed a sample-driven schema matching system, which models Web API services as parameterized queries. These Web API services return a view definition of their data which subsequently need to be connected to the local database scheme. Furthermore, our approach is able to find valid input values for Web API services automatically (e.g. IDs) and can determine combined linkage points (e.g. first and last name) despite different structures. Our results on six real world API services with two local databases show that our linkage point detection algorithm performs well in terms of precision (0.89 up to 1.0) and recall (0.69 up to 1.0).

Research on computational argumentation has recently become very popular. An argument consists of a claim that is supported or attacked by at least one premise. Its intention is the persuasion of others. An important problem in this field is retrieving good premises for a designated claim from a corpus of arguments. Given a claim, oftentimes existing approaches' first step is finding textually similar claims. In this paper we compare 196 methods systematically for determining similar claims by textual similarity, using a large corpus of (claim, premise) pairs crawled from debate portals. We also evaluate how well textual similarity of claims can predict relevance of the associated premises.

Structured knowledge bases are an increasingly important way for storing and retrieving information. Within such knowledge bases, an important search task is finding similar entities based on one or more example entities. We present QBEES, a novel framework for defining entity similarity based on structural features, so-called aspects and maximal aspects of the entities, that naturally model potential interest profiles of a user submitting an ambiguous query. Our approach based on maximal aspects provides natural diversity awareness and includes query-dependent and query-independent entity ranking components. We present evaluation results with a number of existing entity list completion benchmarks, comparing to several state-of-the-art baselines.

Structured knowledge bases are an increasingly important way for storing and retrieving information. Within such knowledge bases, an important search task is finding similar entities based on one or more example entities. We present QBEES, a novel framework for defining entity similarity based only on structural features, so-called aspects, of the entities, that includes query-dependent and query-independent entity ranking components. We present evaluation results with a number of existing entity list completion benchmarks, comparing to several state-of-the-art baselines.

Given an entity represented by a single node q in semantic knowledge graph D , the Graphical Entity Summarisation problem (GES) consists in selecting out of D a very small surrounding graph S that constitutes a generic summary of the information concerning the entity q with given limit on size of S . This article concerns the role of diversity in this quite novel problem. It gives an overview of the diversity concept in information retrieval, and proposes how to adapt it to GES. A measure of diversity for GES, called ALC, is defined and two algorithms presented, baseline, diversity-oblivious PRECIS and diversity-aware DIVERSUM. A reported experiment shows that DIVERSUM actually achieves higher values of the ALC diversity measure than PRECIS. Next, an objective evaluation experiment demonstrates that diversity-aware algorithm is superior to the diversity-oblivious one in terms of fact selection. More precisely, DIVERSUM clearly achieves higher recall than PRECIS on ground truth reference entity summaries extracted from Wikipedia. We also report another intrinsic experiment, in which the output of diversity-aware algorithm is significantly preferred by human expert evaluators. Importantly, the user feedback clearly indicates that the notion of diversity is the key reason for the preference. In addition, the experiment is repeated twice on an anonymous sample of broad population of Internet users by means of a crowd-sourcing platform, that further confirms the results mentioned above.

An important building block of many graph applications such as searching in social networks, keyword search in graphs, and retrieval of linked documents is retrieving the transitive neighbors of a node in ascending order of their distances. Since large graphs cannot be kept in memory and graph traversals at query time would be prohibitively expensive, the list of neighbors for each node is usually precomputed and stored in a compact form. While the problem of precomputing all-pairs shortest distances has been well studied for decades, efficiently maintaining this information when the graph changes is not as well understood. This paper presents an algorithm for maintaining nearest neighbor lists in weighted graphs under node insertions and decreasing edge weights. It considers the important case where queries are a lot more frequent than updates, and presents two approaches for transparently performing necessary index updates while executing queries. Extensive experiments with large graphs, including a subset of Twitter's user graph, demonstrate that the overhead for this maintenance is small.

Semantic recognition and annotation of unqiue enities and their relations is a key in understanding the essence contained in large text corpora. It typically requires a combination of efficient automatic methods and manual verification. Usually, both parts are seen as consecutive steps. In this demo we present MIKE, a user interface enabling the integration of user feedback into an iterative extraction process. We show how an extraction system can directly learn from such integrated user supervision. In general, this setup allows for stepwise training of the extraction system to a particular domain, while using user feedback early in the iterative extraction process improves extraction quality and reduces the overall human effort needed.

Time-travel text search enriches standard text search by temporal predicates, so that users of web archives can easily retrieve document versions that are considered relevant to a given keyword query and existed during a given time interval. Different index structures have been proposed to efficiently support time-travel text search. None of them, however, can easily be updated as the Web evolves and new document versions are added to the web archive.

Ranking of retrieval systems for focused tasks requires large number of relevance judgments. We propose an approach that minimizes the number of relevance judgments, where the performance measures are approximated using a Monte-Carlo sampling technique. Partial measures are taken using relevance judgments, whereas the remaining part of passages are annotated using a generated relevance probability distribution based on result rank. We define two conditions for stopping the assessment procedure when the ranking between systems is stable.

Linked Data refers to data published in accordance with a number of principles rooted in web standards. In the past few years we have witnessed a tremendous growth in Linked Data publishing on the web, leading to tens of billions of data items published online. Querying the data is a key functionality required to make use of the wealth of rich interlinked data. The goal of the tutorial is to introduce, motivate, and detail techniques for querying heterogeneous structured data from across the web. Our tutorial aims to introduce database researchers and practitioners to the new publishing paradigm on the web, and show how the abundance of data published as Linked Data can serve as fertile ground for database research and experimentation. As such, the tutorial focuses on applying database techniques to processing Linked Data, such as optimized indexing and query processing methods in the centralized setting as well as distributed approaches for querying. At the same time, we make the connection from Linked Data best practices to established technologies in distributed databases and the concept of Dataspaces and show differences as well as commonalities between the fields.

Term proximity scoring is an established means in information retrieval for improving result quality of full-text queries. Integrating such proximity scores into efficient query processing, however, has not been equally well studied. Existing methods make use of precomputed lists of documents where tuples of terms, usually pairs, occur together, usually incurring a huge index size compared to term-only indexes. This article introduces a joint framework for trading off index size and result quality, and provides optimization techniques for tuning precomputed indexes towards either maximal result quality or maximal query processing performance under controlled result quality, given an upper bound for the index size. The framework allows to selectively materialize lists for pairs based on a query log to further reduce index size. Extensive experiments with two large text collections demonstrate runtime improvements of more than one order of magnitude over existing text-based processing techniques with reasonable index sizes.

Traditional information retrieval techniques based on keyword search help to identify a ranked set of relevant documents, which often contains many documents in the top ranks that do not meet the user's intention. By considering the semantics of the keywords and their relationships, both precision and recall can be improved. Using an ontology and mapping keywords to entities/concepts and identifying the relationship between them that the user is interested in, allows for retrieving documents that actually meet the user's intention. In this paper, we present a framework that enables semantic-aware document retrieval. User queries are mapped to semantic statements based on entities and their relationships. The framework searches for documents expressing these statements in different variations, e.g., synonymous names for entities or different textual expressions for relations between them. The size of potential result sets makes ranking documents according to their relevance to the user an essential component of such a system. The ranking model proposed in this paper is based on statistical language-models and considers aspects such as the authority of a document and the confidence in the textual pattern representing the queried information.

This paper summarizes the details of the first international workshop on search and mining entity-relationship data. This workshop will bridge between IR, DB, and KM researchers to seek novel solutions for search and data mining of rich entity-relationship data and their applications in various domains. We first provide an overview about the workshop. We then briefly discuss the workshop program.

Time-travel queries that couple temporal constraints with keyword queries are useful in searching large-scale archives of time-evolving content such as the web archives or wikis. Typical approaches for efficient evaluation of these queries involve slicing either the entire collection [20] or individual index lists [10] along the time-axis. Both these methods are not satisfactory since they sacrifice compactness of index for processing efficiency making them either too big or, otherwise, too slow.

Query processing with precomputed term pair lists can improve efficiency for some queries, but suffers from the quadratic number of index lists that need to be read. We present a novel hybrid index structure that aims at decreasing the number of index lists retrieved at query processing time, trading off a reduced number of index lists for an increased number of bytes to read. Our experiments demonstrate significant cold-cache performance gains of almost 25% on standard benchmark queries.

This paper investigates the influence of pruning feature lists to keep a given budget for the evaluation of ranking methods. We learn from a given training set how important the individual prefixes are for the ranking quality. Based on there importance we choose the best prefixes to calculate the ranking while keeping the budget.

Modern text analytics applications operate on large volumes of temporal text data such as Web archives, newspaper archives, blogs, wikis, and micro-blogs. In these settings, searching and mining needs to use constraints on the time dimension in addition to keyword constraints. A natural approach to address such queries is using an inverted index whose entries are enriched with valid-time intervals. It has been shown that these indexes have to be partitioned along time in order to achieve efficiency. However, when the temporal predicate corresponds to a long time range, requiring the processing of multiple partitions, naive query processing incurs high cost of reading of redundant entries across partitions.

There is an increasing amount of structure on the Web as a result of modern Web languages, user tagging and annotation, and emerging robust NLP tools. These meaningful, semantic, annotations hold the promise to significantly enhance information access, by enhancing the depth of analysis of today's systems. Currently, we have only started exploring the possibilities and only begin to understand how these valuable semantic cues can be put to fruitful use. Unleashing the potential of semantic annotations requires us to think outside the box, by combining the insights of natural language processing (NLP) to go beyond bags of words, the insights of databases (DB) to use structure efficiently even when aggregating over millions of records, the insights of information retrieval (IR) in effective goal-directed search and evaluation, and the insights of knowledge management (KM) to get grips on the greater whole.

In recent years, there has been considerable research on information extraction and constructing RDF knowledge bases. In general, the goal is to extract all relevant information from a corpus of documents, store it into an ontology, and answer future queries based only on the created knowledge base. Thus, the original documents become dispensable. On the one hand, an ontology is a convenient and non-redundant structured source of information, based on which specific queries can be answered efficiently. On the other hand, many users doubt the correctness of facts and ontology subgraphs presented to them as query results without proof. Instead, users often wish to verify the obtained facts or subgraphs by reading about them in context, i.e., in a document relating the facts and providing background information. In this demo, we present ROXXI, a system operating on top of an existing knowledge base and reviving the abandoned witness documents. In doing so, it goes the opposite way of information extraction approaches -- starting with ontological facts and tracing their way back to the documents they were extracted from. ROXXI offers interfaces for expert users (SPARQL) as well as for non-experts (ontology browser) and provides a ranked list of documents each associated with a content snippet highlighting the queried facts in context. At the demonstration site, we will show the advantages of this novel approach towards document retrieval and illustrate the benefits of reviving the documents that information extraction approaches neglect.

The success of knowledge-sharing communities like Wikipedia and the advances in automatic information extraction from textual and Web sources have made it possible to build large "knowledge repositories" such as DBpedia, Freebase, and YAGO. These collections can be viewed as graphs of entities and relationships (ER graphs) and can be represented as a set of subject-property-object (SPO) triples in the Semantic-Web data model RDF. Queries can be expressed in the W3C-endorsed SPARQL language or by similarly designed graph-pattern search. However, exact-match query semantics often fall short of satisfying the users' needs by returning too many or too few results. Therefore, IR-style ranking models are crucially needed.

We consider a novel problem of top-k query processing under budget constraints. We provide both a framework and a set of algorithms to address this problem. Existing algorithms for top-k processing are budget-oblivious, i.e., they do not take budget constraints into account when making scheduling decisions, but focus on the performance to compute the final top-k results. Under budget constraints, these algorithms therefore often return results that are a lot worse than the results that can be achieved with a clever, budget-aware scheduling algorithm. This paper introduces novel algorithms for budget-aware top-k processing that produce results that have a significantly higher quality than those of state-of-the-art budget-oblivious solutions.

Top- k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper introduces novel optimization methods for top- k aggregation queries in such distributed environments. The optimizations can be applied to all algorithms that fall into the frameworks of the prior TPUT and KLEE methods. The optimizations address three degrees of freedom: 1) hierarchically grouping input lists into top- k operator trees and optimizing the tree structure, 2) computing data-adaptive scan depths for different input sources, and 3) data-adaptive sampling of a small subset of input sources in scenarios with hundreds or thousands of query-relevant network nodes. All optimizations are based on a statistical cost model that utilizes local synopses, e.g., in the form of histograms, efficiently computed convolutions, and estimators based on order statistics. The paper presents comprehensive experiments, with three different real-life datasets and using the ns-2 network simulator for a packet-level simulation of a large Internet-style network.

Proximity-aware scoring functions lead to significant effectiveness improvements for text retrieval. For XML IR, we can sometimes enhance the retrieval quality by exploiting knowledge about the document structure combined with established text IR methods. This paper introduces modified proximity scores that take the document structure into account and demonstrates the effect for the INEX benchmark.

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.

This demonstration presents an XML IR system that allows users to give feedback of different granularities and types, using Dempster-Shafer theory of evidence to compute expanded and reweighted queries.

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.

Recent IR extensions to XML query languages such as Xpath 1.0 Full-Text or the NEXI query language of the INEX benchmark series reflect the emerging interest in IR-style ranked retrieval over semistructured data. TopX is a top- k retrieval engine for text and semistructured data. It terminates query execution as soon as it can safely determine the k top-ranked result elements according to a monotonic score aggregation function with respect to a multidimensional query. It efficiently supports vague search on both content- and structure-oriented query conditions for dynamic query relaxation with controllable influence on the result ranking. The main contributions of this paper unfold into four main points: (1) fully implemented models and algorithms for ranked XML retrieval with XPath Full-Text functionality, (2) efficient and effective top- k query processing for semistructured data, (3) support for integrating thesauri and ontologies with statistically quantified relationships among concepts, leveraged for word-sense disambiguation and query expansion, and (4) a comprehensive description of the TopX system, with performance experiments on large-scale corpora like TREC Terabyte and INEX Wikipedia.

This paper proposes a demo of the TopX search engine, an extensive framework for unified indexing, querying, and ranking of large collections of unstructured, semistructured, and structured data. TopX integrates efficient algorithms for top-k-style ranked retrieval with powerful scoring models for text and XML documents, as well as dynamic and self-tuning query expansion based on background ontologies.

The HOPI index, a connection index for XML documents based on the concept of a 2-hop cover, provides space- and time-efficient reachability tests along the ancestor, descendant, and link axes to support path expressions with wildcards in XML search engines. This paper presents enhanced algorithms for building HOPI, shows how to augment the index with distance information, and discusses incremental index maintenance. Our experiments show substantial improvements over the existing divide-and-conquer algorithm for index creation, low space overhead for including distance information in the index, and efficient updates.

We present a novel approach for efficient and self-tuning query expansion that is embedded into a top-k query processor with candidate pruning. Traditional query expansion methods select expansion terms whose thematic similarity to the original query terms is above some specified threshold, thus generating a disjunctive query with much higher dimensionality. This poses three major problems: 1) the need for hand-tuning the expansion threshold, 2) the potential topic dilution with overly aggressive expansion, and 3) the drastically increased execution cost of a high-dimensional query. The method developed in this paper addresses all three problems by dynamically and incrementally merging the inverted lists for the potential expansion terms with the lists for the original query terms. A priority queue is used for maintaining result candidates, the pruning of candidates is based on Fagin's family of top-k algorithms, and optionally probabilistic estimators of candidate scores can be used for additional pruning. Experiments on the TREC collections for the 2004 Robust and Terabyte tracks demonstrate the increased efficiency, effectiveness, and scalability of our approach.