Publications

2024

1. Assessing Brittleness of Image-Text Retrieval Benchmarks from Vision-Language Models Perspective

   Mariya Hendriksen, Shuo Zhang, Ridho Reinanda, and 3 more authors

   CoRR, 2024

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2. 

   A new era of AI-assisted journalism at Bloomberg

   Claudia Quinonez, and Edgar Meij

   AI Magazine, 2024

   Abs Website

   Artificial intelligence (AI) is impacting and has the potential to upend entire business models and structures. The adoption of such new technologies to support newsgathering processes is established practice for newsrooms. For AI specifically, we are seeing a new era of AI-assisted journalism emerge with trust in the AI-driven analyses and accuracy of results as core tenets. In Part I of this position paper, we discuss the contributions of six recently published research papers co-authored by Bloomberg’s Artificial Intelligence Engineering team that show the intricacies of training AI models for reliable newsgathering processes. The papers investigate (a) the creation of models for updated headline generation, showing that headline generation models benefit from access to the past state of the article, (b) sequentially controlled text generation, which is a novel task and we show that in general, more structured awareness results in higher control accuracy and grammatical coherence, (c) chart summarization, which looks into identifying the key message and generating sentences that describe salient information in the multimodal documents, (d) a semistructured natural language inference task to develop a framework for data augmentation for tabular inference, (e) the introduction of a human-annotated dataset (ENTSUM) for controllable summarization with a focus on named entities as the aspect to control, and (f) a novel defense mechanism against adversarial attacks (ATINTER). We also examine Bloomberg’s research work, building its own internal, not-for-commercial-use large language model, BloombergGPT, and training it with the goal of demonstrating support for a wide range of tasks within the financial industry. In Part II, we analyze the evolution of automation tasks in the Bloomberg newsroom that led to the creation of Bloomberg’s News Innovation Lab. Technology-assisted content creation has been a reality at Bloomberg News for nearly a decade and has evolved from rules-based headline generation from structured files to the constant exploration of potential ways to assist story creation and storytelling in the financial domain. The Lab now oversees the operation of hundreds of software bots that create semi- and fully automated stories of financial relevance, providing journalists with depth in terms of data and analysis, speed in terms of reacting to breaking news, and transparency to corners of the financial world where data investigation is a gigantic undertaking. The Lab recently introduced new tools that provide journalists with the ability to explore automation on demand while it continues to experiment with ways to assist story production. In Part III, we conceptually discuss the transformative impact that generative AI can have in any newsroom, along with considerations about the technology’s shortcomings in its current state of development. As with any revolutionary new technology, as well as with exciting research opportunities, part of the challenge is balancing any potential positive and negative impacts on society. We offer our principles and guidelines used to inform our approach to experimenting with the new generative AI technologies. Bloomberg News’ style guide reminds us that our “journalism is aimed at possibly the most sophisticated audience in the world, for whom accuracy is essential.”

2023

1. 

   ECIR 23 tutorial: Neuro-symbolic approaches for information retrieval

   Laura Dietz, Hannah Bast, Shubham Chatterjee, and 3 more authors

   In European Conference on Information Retrieval, 2023

   Abs Website

   This tutorial will provide an overview of recent advances on neuro-symbolic approaches for information retrieval. A decade ago, knowledge graphs and semantic annotations technology led to active research on how to best leverage symbolic knowledge. At the same time, neural methods have demonstrated to be versatile and highly effective. From a neural network perspective, the same representation approach can service document ranking or knowledge graph reasoning. End-to-end training allows to optimize complex methods for downstream tasks. We are at the point where both the symbolic and the neural research advances are coalescing into neuro-symbolic approaches. The underlying research questions are how to best combine symbolic and neural approaches, what kind of symbolic/neural approaches are most suitable for which use case, and how to best integrate both ideas to advance the state of the art in information retrieval.

2. Dense retrieval adaptation using target domain description

   Helia Hashemi, Yong Zhuang, Sachith Sri Ram Kothur, and 3 more authors

   In Proceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval, 2023

2022

1. Understanding Financial Information Seeking Behavior from User Interactions with Company Filings

   Mozhdeh Ariannezhad, Mohamed Yahya, Edgar Meij, and 2 more authors

   In Companion Proceedings of the Web Conference 2022, 2022
2. Entity Retrieval from Multilingual Knowledge Graphs

   Saher Esmeir, Arthur Câmara, and Edgar Meij

   MRL 2022, 2022
3. Similarity-based multi-domain dialogue state tracking with copy mechanisms for task-based virtual personal assistants

   Jarana Manotumruksa, Jeffrey Dalton, Edgar Meij, and 1 more author

   In Proceedings of the ACM Web Conference 2022, 2022

2021

1. Improving dialogue state tracking with turn-based loss function and sequential data augmentation

   Jarana Manotumruksa, Jeff Dalton, Edgar Meij, and 1 more author

   In Findings of the Association for Computational Linguistics: EMNLP 2021, 2021
2. Report on the first workshop on bias in automatic knowledge graph construction at AKBC 2020

   Tara Safavi, Edgar Meij, Fatma Özcan, and 3 more authors

   In ACM SIGIR Forum, 2021
3. News article retrieval in context for event-centric narrative creation

   Nikos Voskarides, Edgar Meij, Sabrina Sauer, and 1 more author

   In Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval, 2021

2020

1. 

   Knowledge graphs: An information retrieval perspective

   Ridho Reinanda, Edgar Meij, Maarten Rijke, and 1 more author

   Foundations and Trends in Information Retrieval, 2020

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2. Identifying Notable News Stories

   Antonia Saravanou, Giorgio Stefanoni, and Edgar Meij

   In Advances in Information Retrieval, 2020

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   The volume of news content has increased significantly in recent years and systems to process and deliver this information in an automated fashion at scale are becoming increasingly prevalent. One critical component that is required in such systems is a method to automatically determine how notable a certain news story is, in order to prioritize these stories during delivery. One way to do so is to compare each story in a stream of news stories to a notable event. In other words, the problem of detecting notable news can be defined as a ranking task; given a trusted source of notable events and a stream of candidate news stories, we aim to answer the question: “Which of the candidate news stories is most similar to the notable one?”. We employ different combinations of features and learning to rank (LTR) models and gather relevance labels using crowdsourcing. In our approach, we use structured representations of candidate news stories (triples) and we link them to corresponding entities. Our evaluation shows that the features in our proposed method outperform standard ranking methods, and that the trained model generalizes well to unseen news stories.

3. Improving the Utility of Knowledge Graph Embeddings with Calibration

   Tara Safavi, Danai Koutra, and Edgar Meij

   2020

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4. Novel Entity Discovery from Web Tables

   Shuo Zhang, Edgar Meij, Krisztian Balog, and 1 more author

   In Proceedings of The Web Conference 2020, Taipei, Taiwan, 2020

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2019

1. Special issue on knowledge graphs and semantics in text analysis and retrieval

   Laura Dietz, Chenyan Xiong, Jeff Dalton, and 1 more author

   Information Retrieval Journal, Mar 2019

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2018

1. Statistical language modeling for information access

   Krisztian Balog, Maarten Rijke, and Edgar Meij

   Mar 2018
2. Utilizing Knowledge Graphs for Text-Centric Information Retrieval

   Laura Dietz, Alexander Kotov, and Edgar Meij

   In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, Ann Arbor, MI, USA, Mar 2018

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3. The Second Workshop on Knowledge Graphs and Semantics for Text Retrieval, Analysis, and Understanding (KG4IR)

   Laura Dietz, Chenyan Xiong, Jeff Dalton, and 1 more author

   In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, Ann Arbor, MI, USA, Mar 2018

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4. Overview of The First Workshop on Knowledge Graphs and Semantics for Text Retrieval and Analysis (KG4IR)

   Laura Dietz, Chenyan Xiong, and Edgar Meij

   SIGIR Forum, Feb 2018

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5. Related Entity Finding on Highly-heterogeneous Knowledge Graphs

   Ridho Reinanda, Edgar Meij, Joshua Pantony, and 1 more author

   In ASONAM, Feb 2018

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6. Weakly-supervised Contextualization of Knowledge Graph Facts

   Nikos Voskarides, Edgar Meij, Ridho Reinanda, and 5 more authors

   In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, Ann Arbor, MI, USA, Feb 2018

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2017

1. The First Workshop on Knowledge Graphs and Semantics for Text Retrieval and Analysis (KG4IR)

   Laura Dietz, Chenyan Xiong, and Edgar Meij

   In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Shinjuku, Tokyo, Japan, Feb 2017

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2. Generating descriptions of entity relationships

   Nikos Voskarides, Edgar Meij, and Maarten Rijke

   In ECIR 2017: 39th European Conference on Information Retrieval, Apr 2017

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2016

1. Utilizing Knowledge Bases in Text-centric Information Retrieval

   Laura Dietz, Alexander Kotov, and Edgar Meij

   In Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval, Apr 2016

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2. Dynamic Collective Entity Representations for Entity Ranking

   David Graus, Manos Tsagkias, Wouter Weerkamp, and 2 more authors

   In Proceedings of the ninth ACM international conference on Web search and data mining, Apr 2016

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3. Document filtering for long-tail entities

   Ridho Reinanda, Edgar Meij, and Maarten Rijke

   In CIKM 2016: 25th ACM Conference on Information and Knowledge Management, Oct 2016

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2015

1. Fast and Space-Efficient Entity Linking in Queries

   Roi Blanco, Giuseppe Ottaviano, and Edgar Meij

   In Proceedings of the eighth ACM international conference on Web search and data mining, Oct 2015

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2. Dynamic query modeling for related content finding

   Daan Odijk, Edgar Meij, Isaac Sijaranamual, and 1 more author

   In SIGIR 2015: 38th international ACM SIGIR conference on Research and development in information retrieval, Aug 2015

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3. Mining, ranking and recommending entity aspects

   Ridho Reinanda, Edgar Meij, and Maarten Rijke

   In SIGIR 2015: 38th international ACM SIGIR conference on Research and development in information retrieval, Aug 2015

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4. Learning to Explain Entity Relationships in Knowledge Graphs

   Nikos Voskarides, Edgar Meij, Manos Tsagkias, and 2 more authors

   In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Beijing, China, Aug 2015

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2014

1. Time-Aware Rank Aggregation for Microblog Search

   Shangsong Liang, Zhaochun Ren, Wouter Weerkamp, and 2 more authors

   In Proceedings of the 23th ACM conference on Information and knowledge management, Aug 2014

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2. Using temporal bursts for query modeling

   Maria-Hendrike Peetz, Edgar Meij, and Maarten Rijke

   Information Retrieval, Aug 2014

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2013

1. Een bril die alles weet

   E. Meij

   NRC Handelsblad, Feb 2013

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2. Feeding the Second Screen: Semantic Linking based on Subtitles

   D. Odijk, E. Meij, and M. Rijke

   In OAIR ’13, Feb 2013

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3. Multilingual Semantic Linking for Video Streams: Making ’Ideas Worth Sharing’ More Accessible

   Daan Odijk, Edgar Meij, David Graus, and 1 more author

   In Proceedings of the 2nd International Workshop on Web of Linked Entities (WoLE 2013), Feb 2013

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4. Personalized Time-aware Tweets Summarization

   Zhaochun Ren, Shangsong Liang, Edgar Meij, and 1 more author

   In Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, Feb 2013

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2012

1. The University of Amsterdam at TREC 2012

   R. Berendsen, E. Meij, D. Odijk, and 2 more authors

   In TREC 2012 Working Notes, Nov 2012

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2. Generating Pseudo Test Collections for Learning to Rank Scientific Articles

   Richard Berendsen, Manos Tsagkias, Maarten Rijke, and 1 more author

   In Information Access Evaluation. Multilinguality, Multimodality, and Visual Analytics - Third International Conference of the CLEF Initiative, CLEF 2012, Nov 2012

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3. Team COMMIT at TREC 2011

   Marc Bron, Edgar Meij, Maria-Hendrike Peetz, and 2 more authors

   In The Twentieth Text REtrieval Conference, Nov 2012

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4. A Corpus for Entity Profiling in Microblog Posts

   Damiano Spina, Edgar Meij, Andrei Oghina, and 3 more authors

   In LREC 2012 Workshop on Language Engineering for Online Reputation Management, Nov 2012

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5. Identifying Entity Aspects in Microblog Posts

   Damiano Spina, Edgar Meij, Maarten Rijke, and 3 more authors

   In The 35th International ACM SIGIR conference on research and development in Information Retrieval, Nov 2012

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6. Overview of RepLab 2012: Evaluating Online Reputation Management Systems

   Enrique Amigó, Adolfo Corujo, Julio Gonzalo, and 2 more authors

   In CLEF (Online Working Notes/Labs/Workshop), Nov 2012

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7. Context-Based Entity Linking – University of Amsterdam at TAC 2012

   D. Graus, T. Kenter, M. Bron, and 2 more authors

   In TAC 2012 Working Notes, Nov 2012

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8. The University of Amsterdam at the TREC 2011 Session Track

   Bouke Huurnink, Richard Berendsen, Katja Hofmann, and 2 more authors

   In The Twentieth Text REtrieval Conference, Jan 2012

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9. A Framework for Unsupervised Spam Detection in Social Networking Sites

   Maarten Bosma, Edgar Meij, and Wouter Weerkamp

   In Advances in Information Retrieval - 34th European Conference on IR Research, ECIR 2012, Jan 2012

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10. Adding Semantics to Microblog Posts

    Edgar Meij, Wouter Weerkamp, and Maarten Rijke

    In Proceedings of the fifth ACM international conference on Web search and data mining, Jan 2012

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11. OpenGeist: Insight in the Stream of Page Views on Wikipedia

    M-H. Peetz, E. Meij, and M. Rijke

    In SIGIR 2012 Workshop on Time-aware Information Access, Jan 2012

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12. Adaptive Temporal Query Modeling

    Maria-Hendrike Peetz, Edgar Meij, Maarten Rijke, and 1 more author

    In Advances in Information Retrieval - 34th European Conference on IR Research, ECIR 2012, Jan 2012

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13. DutchHatTrick: Semantic query modeling, ConText, section detection, and match score maximization

    M. Schuemie, Dolf Trieschnigg, and Edgar Meij

    In The Twentieth Text REtrieval Conference, Jan 2012

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2011

1. DIR 2011 - Dutch-Belgian Information Retrieval Workshop

   Jan 2011
2. Online Religious Studies

   J. Bekkenkamp, E. Meij, and M. Rijke

   In Web Science 2011, Jan 2011

   Abs PDF

   Data transitions have revolutionized many scientific disciplines, starting with the exact sciences, then the life sciences, and now the social sciences and humanities are in the process of making the transition to becoming data intensive sciences, with descriptions through quantitative measurements. New analysis tools and publicly accessible utterances, opinions, transactions and interactions resulting from widespread internet and social media usage facilitate new, data-intensive research methods in disciplines that have so far relied on small-scale literature and/or panel-based studies. To illustrate the new possibilities, we report on a pilot carried out by a cross-disciplinary team consisting of computer scientists and researchers in religious studies. In the latter area, research is often focused on mapping out the convictions, hopes, and beliefs of groups of people, be it within certain religions or within any other group, such as those defined by a political party. In the pilot, religious scholars examined the core keywords in a left-wing political party in order to determine their hopes and beliefs. Rather than following their standard way-of- working, they were equipped with a search engine with an index of content crawled from discussion forums, the party’s web site plus a range of online publications relating to the party and going back to 1990. In this paper we focus on lessons learned and on methodological innovations for religious scholars as well as for computer scientists building the enabling technology.

3. Classifying Queries Submitted to a Vertical Search Engine

   R. Berendsen, B. Kovachev, E. Meij, and 2 more authors

   In Web Science 2011, Jan 2011

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4. Workshop report: Dutch-Belgian Information Retrieval

   C. Boscarino, K. Hofmann, V.B. Jijkoun, and 3 more authors

   SIGIR Forum, Jan 2011

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5. Mapping queries to the Linking Open Data cloud: A case study using DBpedia

   Edgar Meij, Marc Bron, Laura Hollink, and 2 more authors

   Web Semantics: Science, Services and Agents on the World Wide Web, Jan 2011

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   We introduce the task of mapping search engine queries to DBpedia, a major linking hub in the Linking Open Data cloud. We propose and compare various methods for addressing this task, using a mixture of information retrieval and machine learning techniques. Specifically, we present a supervised machine learning-based method to determine which concepts are intended by a user issuing a query. The concepts are obtained from an ontology and may be used to provide contextual information, related concepts, or navigational suggestions to the user submitting the query. Our approach first ranks candidate concepts using a language modeling for information retrieval framework. We then extract query, concept, and search-history feature vectors for these concepts. Using manual annotations we inform a machine learning algorithm that learns how to select concepts from the candidates given an input query. Simply performing a lexical match between the queries and concepts is found to perform poorly and so does using retrieval alone, i.e., omitting the concept selection stage. Our proposed method significantly improves upon these baselines and we find that support vector machines are able to achieve the best performance out of the machine learning algorithms evaluated.

6. Result diversification based on query-specific cluster ranking

   Jiyin He, Edgar Meij, and Maarten Rijke

   J. Am. Soc. Inf. Sci., Jan 2011

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   Result diversification is a retrieval strategy for dealing with ambiguous or multi-faceted queries by providing documents that cover as many facets of the query as possible. We propose a result diversification framework based on query-specific clustering and cluster ranking, in which diversification is restricted to documents belonging to clusters that potentially contain a high percentage of relevant documents. Empirical results show that the proposed framework improves the performance of several existing diversification methods. The framework also gives rise to a simple yet effective cluster-based approach to result diversification that selects documents from different clusters to be included in a ranked list in a round robin fashion. We describe a set of experiments aimed at thoroughly analyzing the behavior of the two main components of the proposed diversification framework, ranking and selecting clusters for diversification. Both components have a crucial impact on the overall performance of our framework, but ranking clusters plays a more important role than selecting clusters. We also examine properties that clusters should have in order for our diversification framework to be effective. Most relevant documents should be contained in a small number of high-quality clusters, while there should be no dominantly large clusters. Also, documents from these high-quality clusters should have a diverse content. These properties are strongly correlated with the overall performance of the proposed diversification framework.

7. The University of Amsterdam at TREC 2010: Session, Entity, and Relevance Feedback

   M. Bron, J. He, K. Hofmann, and 4 more authors

   In The Nineteenth Text REtrieval Conference, Jan 2011

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   We describe the participation of the University of Amsterdam’s Intelligent Systems Lab in the web track at TREC 2009. We participated in the adhoc and diversity task. We find that spam is an important issue in the ad hoc task and that Wikipedia-based heuristic optimization approaches help to boost the retrieval performance, which is assumed to potentially reduce spam in the top ranked results. As for the diversity task, we explored different methods. Clustering and a topic model-based approach have a similar performance and both are relatively better than a query log based approach.

8. Wij-woorden op websites: Zoekmachines voor geesteswetenschappers

   E. Meij, and M. Rijke

   In Onszelf voorbij. Over de grenzen van verbondenheid, Jan 2011

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9. Zoekmachines van de toekomst

   E. Meij

   Informatie Professional, Nov 2011

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10. People searching for people: analysis of a people search engine log

    Wouter Weerkamp, Richard Berendsen, Bogomil Kovachev, and 3 more authors

    In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information, Nov 2011

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2010

1. Entity search: building bridges between two worlds

   Krisztian Balog, Edgar Meij, and Maarten Rijke

   In Proceedings of the 3rd International Semantic Search Workshop, Nov 2010

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2. Archieven Linken met Semantische Zoekmachines

   M. Rijke, K. Balog, M. Bron, and 8 more authors

   DIXIT (Tijdschrift over toegepaste taal- en spraaktechnologie), Nov 2010

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3. Heuristic Ranking and Diversification of Web Documents

   J. He, K. Balog, K. Hofmann, and 4 more authors

   In The Eighteenth Text REtrieval Conference, Nov 2010

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   We describe the participation of the University of Amsterdam’s Intelligent Systems Lab in the web track at TREC 2009. We participated in the adhoc and diversity task. We find that spam is an important issue in the ad hoc task and that Wikipedia-based heuristic optimization approaches help to boost the retrieval performance, which is assumed to potentially reduce spam in the top ranked results. As for the diversity task, we explored different methods. Clustering and a topic model-based approach have a similar performance and both are relatively better than a query log based approach.

4. Enabling Large Data Transfers Between Web Services

   S. Koulouzis, E. Meij, and A. Belloum

   In 5th EGEE User Forum, Nov 2010

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5. Learning Semantic Query Suggestions (Abstract)

   E. Meij, M. Bron, L. Hollink, and 2 more authors

   In DIR ’10, Nov 2010

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6. Topical Diversity and Relevance Feedback

   E. Meij, J. He, W. Weerkamp, and 1 more author

   In The Eighteenth Text REtrieval Conference, Nov 2010

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7. Supervised query modeling using Wikipedia

   Edgar Meij, and Maarten Rijke

   In Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Nov 2010

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8. Conceptual language models for domain-specific retrieval

   Edgar Meij, Dolf Trieschnigg, Maarten Rijke, and 1 more author

   Inf. Process. Manage., Nov 2010

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9. Combining Concepts and Language Models for Information Access

   Edgar Meij

   University of Amsterdam, Nov 2010

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2009

1. Learning Semantic Query Suggestions

   E. Meij, M. Bron, B. Huurnink, and 2 more authors

   In Proceedings of the 8th International Conference on The Semantic Web, Nov 2009

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   Learning Semantic Query Suggestions by Edgar Meij, Marc Bron, Laura Hollink, Bouke Huurnink and Maarten de Rijke is available online now. An important application of semantic web technology is recognizing human-defined concepts in text. Query transformation is a strategy often used in search engines to derive queries that are able to return more useful search results than the original query and most popular search engines provide facilities that let users complete, specify, or reformulate their queries. We study the problem of semantic query suggestion, a special type of query transformation based on identifying semantic concepts contained in user queries. We use a feature-based approach in conjunction with supervised machine learning, augmenting term-based features with search history-based and concept-specific features. We apply our method to the task of linking queries from real-world query logs (the transaction logs of the Netherlands Institute for Sound and Vision) to the DBpedia knowledge base. We evaluate the utility of different machine learning algorithms, features, and feature types in identifying semantic concepts using a manually developed test bed and show significant improvements over an already high baseline. The resources developed for this paper, i.e., queries, human assessments, and extracted features, are available for download.

2. Investigating the Demand Side of Semantic Search through Query Log Analysis

   Edgar Meij, P. Mika, and H. Zaragoza

   In Proceedings of the Workshop on Semantic Search (SemSearch 2009) at the 18th International World Wide Web Conference (WWW 2009), Nov 2009

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3. A Semantic Perspective on Query Log Analysis

   K. Hofmann, M. Rijke, B. Huurnink, and 1 more author

   In Working Notes for the CLEF 2009 Workshop, Nov 2009

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   We present our views on the CLEF log file analysis task. We argue for a task definition that focuses on the semantic enrichment of query logs. In addition, we discuss how additional information about the context in which queries are being made could further our understanding of users’ information seeking and how to better facilitate this process.

4. The impact of document structure on keyphrase extraction

   Katja Hofmann, Manos Tsagkias, Edgar Meij, and 1 more author

   In Proceedings of the 18th ACM conference on Information and knowledge management, Nov 2009

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5. Semantic disclosure in an e-Science environment

   M.S. Marshall, M. Roos, E. Meij, and 3 more authors

   In Semantic e-Science (Springer Annals of Information Systems AoIS), Nov 2009

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6. De AIDA toolbox: Een gecombineerde aanpak voor het beheren van kennis

   M.S. Marshall, M. Roos, Edgar Meij, and 3 more authors

   Agro Informatica, Nov 2009

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7. Incorporating Non-Relevance Information in the Estimation of Query Models

   E. Meij, W. Weerkamp, J. He, and 1 more author

   In The Seventeenth Text REtrieval Conference, Nov 2009

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   We describe the participation of the University of Amsterdam’s ILPS group in the relevance feedback track at TREC 2008. We introduce a new model which incorporates information from relevant and non-relevant documents to improve the estimation of query models. Our main findings are twofold: (i) in terms of statMAP, a larger number of judged non-relevant documents improves retrieval effectiveness and (ii) on the TREC Terabyte topics, we can effectively replace the estimates on the judged non-relevant documents with estimations on the document collection.

8. Concept models for domain-specific search

   Edgar Meij, and Maarten Rijke

   In Evaluating Systems for Multilingual and Multimodal Information Access, 9th Workshop of the Cross-Language Evaluation Forum, CLEF 2008, Aarhus, Denmark, September 17-19, 2008, Revised Selected Papers, Nov 2009

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9. An evaluation of entity and frequency based query completion methods

   Edgar Meij, Peter Mika, and Hugo Zaragoza

   In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, Nov 2009

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10. A query model based on normalized log-likelihood

    Edgar Meij, Wouter Weerkamp, and Maarten Rijke

    In Proceedings of the 18th ACM conference on Information and knowledge management, Nov 2009

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11. Investigating the Semantic Gap through Query Log Analysis.

    Peter Mika, Edgar Meij, and Hugo Zaragoza

    In Proceedings of the 8th International Semantic Web Conference, Nov 2009

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12. Structuring and extracting knowledge for the support of hypothesis generation in molecular biology

    M. Roos, M.S. Marshall, A.P. Gibson, and 6 more authors

    BMC Bioinformatics, Nov 2009

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13. A Generative Language Modeling Approach for Ranking Entities

    W. Weerkamp, K. Balog, and E. Meij

    In Advances in Focused Retrieval, Nov 2009

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    We describe our participation in the INEX 2008 Entity Ranking track. We develop a generative language modeling approach for the entity ranking and list completion tasks. Our framework comprises the following components: (i) entity and (ii) query language models, (iii) entity prior, (iv) the probability of an entity for a given category, and (v) the probability of an entity given another entity. We explore various ways of estimating these components, and report on our results. We find that improving the estimation of these components has very positive effects on performance, yet, there is room for further improvements.

2008

1. The University of Amsterdam at TREC 2008: Blog, Enterprise, and Relevance Feedback

   K. Balog, E. Meij, W. Weerkamp, and 2 more authors

   In TREC 2008 Working Notes, Nov 2008

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   We describe the participation of the University of Amsterdam’s ILPS group in the blog, enterprise and relevance feedback track at TREC 2008. Our main preliminary conclusions are that estimating mixture weights for external expansion in blog post retrieval is non-trivial and we need more analysis to find out why it works better for blog distillation than for blog post retrieval. For the relevance feedback track we observe two things: (i) in terms of statMAP, a larger number of judged non-relevant documents improves retrieval effectiveness and (ii) on the TREC Terabyte topics, we can effectively replace the estimates on the judged non-relevant documents with estimations on the document collection. Finally, since the enterprise track did not have any results yet, we only described our participation and do not draw any conclusions.

2. The University of Amsterdam at the CLEF 2008 Domain Specific Track - Parsimonious Relevance and Concept Models

   Edgar Meij, and Maarten Rijke

   In Working Notes for the CLEF 2008 Workshop, Nov 2008

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3. Enabling Data Transport between Web Services through alternative protocols and Streaming

   S. Koulouzis, E. Meij, M.S. Marshall, and 1 more author

   In 4th IEEE International Conference on e-Science, Nov 2008

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4. Biological applications of AIDA knowledge management components

   Marco Roos, M. Scott Marshall, Piter T. Boer, and 5 more authors

   In ISMB ’08, Nov 2008

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5. Bootstrapping Language Associated with Biomedical Entities

   E. Meij, and S. Katrenko

   In The Sixteenth Text REtrieval Conference, Nov 2008

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6. Parsimonious concept modeling

   Edgar Meij, Dolf Trieschnigg, Maarten Rijke, and 1 more author

   In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, Nov 2008

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7. Parsimonious relevance models

   Edgar Meij, Wouter Weerkamp, Krisztian Balog, and 1 more author

   In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, Nov 2008

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8. Towards a combined model for search and navigation of annotated documents

   Edgar Meij

   In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, Nov 2008

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   Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

9. Measuring concept relatedness using language models

   Dolf Trieschnigg, Edgar Meij, Maarten Rijke, and 1 more author

   In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, Nov 2008

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10. The University of Amsterdam (ILPS) at INEX 2008

    W. Weerkamp, J. He, K. Balog, and 1 more author

    In INEX 2008 Workshop Pre-Proceedings, Nov 2008

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    We describe our participation in the INEX 2008 Entity Ranking and Link-the-Wiki tracks. We provide a detailed account of the ideas underlying our approaches to these tasks. For the Link-the-Wiki track, we also report on the results and findings so far.

2007

1. The University of Amsterdam at the TREC 2006 Enterprise Track

   K. Balog, E. Meij, and M. Rijke

   In The Fifteenth Text REtrieval Conference, Nov 2007

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2. Integrating Conceptual Knowledge into Relevance Models: A Model and Estimation Method

   E. Meij, and M. Rijke

   In Proceedings of the 1st International Conference on Theory of Information Retrieval, Nov 2007

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3. Thesaurus-Based Feedback to Support Mixed Search and Browsing Environments

   Edgar Meij, and Maarten Rijke

   In Research and Advanced Technology for Digital Libraries, 11th European Conference, ECDL 2007, Nov 2007

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4. Using Prior Information Derived from Citations in Literature Search

   E. Meij, and M. Rijke

   In RIAO 2007, Nov 2007

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5. Expanding Queries Using Multiple Resources (The AID Group at TREC 2006: Genomics Track)

   E. Meij, M. Jansen, and M. Rijke

   In The Fifteenth Text REtrieval Conference, Nov 2007

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6. A new unsupervised method for document clustering by using WordNet lexical and conceptual relations

   Diego R. Recupero

   Information Retrieval, Nov 2007

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7. My first BioAID: heuristic support for hypothesis construction

   M. Roos, S. Katrenko, W.R. Hage, and 3 more authors

   In ISMB-ECCB’07, Nov 2007

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2006

1. Deploying Lucene on the Grid

   E. Meij, and M. Rijke

   In Proceedings SIGIR 2006 workshop on Open Source Information Retrieval (OSIR2006), Nov 2006

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2. Combining Thesauri-based Methods for Biomedical Retrieval

   E. Meij, L.H.L. IJzereef, L.A. Azzopardi, and 4 more authors

   In The Fourteenth Text REtrieval Conference, Nov 2006

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2005

1. Van Case-Based Reasoning tot Information Retrieval; Case retrieval voor de helpdesk van een webhosting bedrijf.

   Edgar Meij

   University of Amsterdam, Nov 2005

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   The helpdesk department of Hostnet, a web hosting company, daily receives 35 up to 50 questions from its customers. Within the domain in which Hostnet operates, only few off-the-shelf manuals exist and this is particularly noticeable on the helpdesk. Currently, only a few possibilities for knowledge management and/or elicitation exist within the organization. Questions are answered and problems are solved mostly by relying on the expertise of the staff. They therefore need to have up-to-date knowledge of a variety of possible questions, problem situations and solutions. They also need to be creative and flexible when handling novel questions. Hostnet uses a ticketing system to handle questions from their customers. One of many advantages of using such a system is that all questions are stored, along with their corresponding answers. Hostnet uses the system for some time now and it has thus collected a large amount of domain and organization specific knowledge. This kind of information is exactly the type on which the research area of case-based reasoning focuses. Case-based reasoning uses previously solved problems (cases) as a knowledge source to aid solving similar cases in the future. One of the main components, in any case-based reasoning system, is the retrieval module. This module searches for alike cases, given a new case and a similarity measure. Techniques from the area of Information Retrieval may be used to assist in finding these alike questions, for example by implementing vector-space based, statistical methods. This research focuses on analyzing to what extent previously solved cases can serve as a basis for a statistical information retrieval module of a case-based reasoning system within Hostnet by measuring the effects of different information retrieval techniques on the results. The evaluated techniques are stemming, term weighting and combinations thereof. The above described organizational setting is not unique to Hostnet. Every service-providing company with direct customer contacts is probably familiar with the described situation and could benefit from the presented results. The suggested approach yields adequate results by which, at best, 60% of new questions can be answered, based on the first 10 retrieved stored questions. The mean reciprocal rank of the first matching question provided room for improvement however, with a value of 7 out of 10. The most important conclusion is that the best results are achieved when applying none of the before mentioned information retrieval techniques. The suggested approach needs to be improved for a successful integration within a case-based reasoning system, but it does seem viable.