Almost all of today’s knowledge is stored in databases and thus can only be accessed with the help of domain specific query languages, strongly limiting the number of people which can access the data. In this work, we demonstrate an end-to-end trainable question answering (QA) system that allows a user to query an external NoSQL database by using natural language. A major challenge of such a system is the non-differentiability of database operations which we overcome by applying policy-based reinforcement learning. We evaluate our approach on Facebook’s bAbI Movie Dialog dataset and achieve a competitive score of 84.2% compared to several benchmark models. We conclude that our approach excels with regard to real-world scenarios where knowledge resides in external databases and intermediate labels are too costly to gather for non-end-to-end trainable QA systems.

Information on the Web is heterogeneous and available in constantly increasing quantities. Consequently, there are numerous, partly redundant data analytics services, each optimized for data with certain characteristics. Often, analytics tasks require multiple services to be pipelined to find a solution, where combinations of exchangeable services for single steps might outperform one-service-predictions. This work proposes a Multi-Agent System (MAS) perception of prior setting, where decentralized agents are considered to manage services, having to coordinate their decisions to find a consensus. We, first, propose a supervised method for service accuracy estimation and, therefore, exploit locality-sensitive features of training data. Given a committee of services managed by agents, we develop coordination strategies to handle conflicting confidences and reduce erroneous predictions due to service correlation. We evaluate our approach with Named Entity Recognition (NER)- and Named Entity Disambiguation (NED) services on text corpora with heterogeneous characteristics (i.e. news articles and tweets). Our empirical results improve the out-of-the-box performance of the original services.

Capturing knowledge via learned latent vector representations of words, images and knowledge graph (KG) entities has shown state-of-the-art performance in computer vision, computational linguistics and KG tasks. Recent results demonstrate that the learning of such representations across modalities can be beneficial, since each modality captures complementary information. However, those approaches are limited to concepts with cross-modal alignments in the training data which are only available for just a few concepts. Especially for visual objects exist far fewer embeddings than for words or KG entities. We investigate whether a word embedding (e.g., for "apple") can still capture information from other modalities even if there is no matching concept within the other modalities (i.e., no images or KG entities of apples but of oranges as pictured in the title analogy). The empirical results of our knowledge transfer approach demonstrate that word embeddings do benefit from extrapolating information across modalities even for concepts that are not represented in the other modalities. Interestingly, this applies most to concrete concepts (e.g., dragonfly) while abstract concepts (e.g., animal) benefit most if aligned concepts are available in the other modalities.

In recent years, the amount of entities in large knowledge bases available on the Web has been increasing rapidly, making it possible to propose new ways of intelligent information access. Within the context of globalization, there is a clear need for techniques and systems that can enable multilingual and cross-lingual information access. In this paper, we present XKnowSearch!, a novel entity-based system for multilingual and cross-lingual information retrieval, which supports keyword search and also allows users to influence the search process according to their search intents. By leveraging the multilingual knowledge base on the Web, keyword queries and documents can be represented in their semantic forms, which can facilitate query disambiguation and expansion, and can also overcome the language barrier between queries and documents in different languages.

In many languages, sparse availability of resources causes numerous challenges for textual analysis tasks.Text classification is one of such standard tasks that is hindered due to limited availability of label information in lowresource languages.Transferring knowledge (i.e.label information) from high-resource to low-resource languages might improve text classification as compared to the other approaches like machine translation.We introduce BRAVE (Bilingual paRAgraph VEctors), a model to learn bilingual distributed representations (i.e.embeddings) of words without word alignments either from sentencealigned parallel or label-aligned non-parallel document corpora to support cross-language text classification.Empirical analysis shows that classification models trained with our bilingual embeddings outperforms other stateof-the-art systems on three different crosslanguage text classification tasks.

In recent years, the amount of entities in large knowledge bases has been increasing rapidly. Such entities can help to bridge unstructured text with structured knowledge and thus be beneficial for many entity-centric applications. The key issue is to link entity mentions in text with entities in knowledge bases, where the main challenge lies in mention ambiguity. Many methods have been proposed to tackle this problem. However, most of the methods assume certain characteristics of the input mentions and documents, e.g., only named entities are considered. In this paper, we propose a context-aware approach to collective entity disambiguation of the input mentions in text with different characteristics in a consistent manner. We extensively evaluate the performance of our approach over 9 datasets and compare it with 14 state-of-the-art methods. Experimental results show that our approach outperforms the existing methods in most cases.

Xavier Carreras, Lluís Padró, Lei Zhang, Achim Rettinger, Zhixing Li, Esteban García-Cuesta, Željko Agić, Božo Bekavac, Blaz Fortuna, Tadej Štajner. Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics. 2014.

Within the context of globalization, multilinguality and cross-linguality for information access have emerged as issues of major interest. In order to achieve the goal that users from all countries have access to the same information, there is an impending need for systems that can help in overcoming language barriers by facilitating multilingual and cross-lingual access to data. In this paper, we demonstrate such a toolkit, which supports both service-oriented and user-oriented interfaces for semantically annotating, analyzing and comparing multilingual texts across the boundaries of languages. We conducted an extensive user study that shows that our toolkit allows users to solve cross-lingual entity tracking and article matching tasks more efficiently and with higher accuracy compared to the baseline approach. 1

The ever-increasing quantities of structured knowledge on the Web and the impending need of multilinguality and cross-linguality for information access pose new challenges but at the same time open up new opportunities for knowledge extraction research. In this regard, cross-lingual semantic annotation has emerged as a topic of major interest and it is essential to build tools that can link words and phrases in unstructured text in one language to resources in structured knowledge bases in any other language. In this paper, we demonstrate X-LiSA, an infrastructure for cross-lingual semantic annotation, which supports both service-oriented and user-oriented interfaces for annotating text documents and web pages in different languages using resources from Wikipedia and Linked Open Data (LOD).

In this paper, we address the task of inferring user preference relationships about various objects in order to generate relevant recommendations. The majority of the traditional approaches to the problem assume a flat representation of the data, and focus on a single dyadic relationship between the objects. We present a richer theoretical model for making recommendations that allows us to reason about many different relations at the same time. The model is based on Markov logic, which is a simple and powerful language that combines first-order logic and probabilistic graphical models. We apply a hybrid, content-collaborative merging scheme through feature combination. We experimentally verify the efficacy of our theoretical model, and show that our method outperforms state-of-the-art recommendation approaches.

The problem of rewriting keyword search queries on graph data has been studied recently, where the main goal is to clean user queries by rewriting keywords as valid tokens appearing in the data and grouping them into meaningful segments. The main solution to this problem employs heuristics for ranking query rewrites and a dynamic programming algorithm for computing them. Based on a broader set of queries defined by an existing benchmark, we show that the use of these heuristics does not yield good results. We propose a novel probabilistic framework, which enables the optimality of a query rewrite to be estimated in a more principled way. We show that our approach outperforms existing work in terms of effectiveness and efficiency of query rewriting. More importantly, we provide the first results indicating query rewriting can indeed improve overall keyword search runtime performance and result quality.

The task of recommending items, like movies, to users is a core feature of many social networks. Standard approaches either use item or user similarity to suggest the next items users might be interested in. Recently, multivariate models like matrix factorization have become popular to combine the advantages of both perspectives. In addition, extensions have been proposed to capture the dynamics of user interests over time, like trends or recurrent user needs. While offering good predictive performance, so far those models do not exploit possibly available rich semantic context. Typically, only one implicit feature, like user ratings, is tracked to give personalized recommendations. However, with semantic data sources, like linked data, wealthy background knowledge becomes available that could be leveraged to improve predictive performance. We argue, that a more flexible framework is needed to model and learn a greater class of recommendation scenarios where rich context is available. Thus, we propose a generic approach which generalizes state-of-the-art methods based on pair wise interaction tensor factorization by leveraging arbitrary background knowledge related to the recommendation situation. Our experiments on streamed semantic data from a social network show that by adding varying sets of context - like user information, sequential information or time information - the ranking of potential items can be personalized and the predictive performance can be improved.

Collaborative tagging systems with user generated content have become a fundamental element of websites such as Delicious, Flickr or CiteULike. By sharing common knowledge, massively linked semantic data sets are generated that provide new challenges for data mining. In this paper, we reduce the data complexity in these systems by finding meaningful topics that serve to group similar users and serve to recommend tags or resources to users. We propose a well-founded probabilistic approach that can model every aspect of a collaborative tagging system. By integrating both user information and tag information into the well-known Latent Dirichlet Allocation framework, the developed models can be used to solve a number of important information extraction and retrieval tasks.