We asked human observers to adjust the color of natural fruit objects until they appeared achromatic. The objects were generally perceived to be gray when their color was shifted away from the observers' gray point in a direction opposite to the typical color of the fruit. These results show that color sensations are not determined by the incoming sensory data alone, but are significantly modulated by high-level visual memory.

Abstract. Semantic Question Answering (SQA) removes two major access requirements to the Semantic Web: the mastery of a formal query language like SPARQL and knowledge of a specific vocabulary. Because of the complexity of natural language, SQA presents difficult challenges and many research opportunities. Instead of a shared effort, however, many essential components are redeveloped, which is an inefficient use of researcher's time and resources. This survey analyzes 62 different SQA systems, which are systematically and manually selected using predefined inclusion and exclusion criteria, leading to 72 selected publications out of 1960 candidates. We identify common challenges, structure solutions, and provide recommendations for future systems. This work is based on publications from the end of 2010 to July 2015 and is also compared to older but similar surveys.

Color constancy is the ability to assign a constant color to an object independent of changes in illumination. Color constancy is achieved by taking context information into account. Previous approaches that have used matching paradigms to quantify color constancy found degrees of constancy between 20% and 80%. Here, we studied color constancy in a color-naming task under different conditions of surround illumination and patch size. Observers categorized more than 400 patches for each illumination condition. This allows one to overcome inherent limitations in color naming and to study the changes in color categories under illumination changes. When small central test patches with a full context illumination were categorized, observers followed the illumination shift almost completely, showing a high degree of constancy (99%). Reducing the available context information or increasing the patch size decreased the degree of constancy to about 50%. Moderate degrees of constancy (66%) occurred even when the test patches were never viewed simultaneously but only in temporal alternation with the illumination. Boundaries between color categories were largely stable within and across observers under neutral illumination. Under changing illumination, there were small but systematic variations in the color category boundaries. Color category boundaries tended to rotate away from the illumination color. This variation was largest under full context conditions where highest degrees of color constancy were obtained.

Abstract. The third instalment of the open challenge on Question Answering over Linked Data (QALD-3) has been conducted as a half-day lab at CLEF 2013. Differently from previous editions of the challenge, QALD-3 put a strong emphasis on multilinguality, offering two tasks: one on multilingual question answering and one on ontology lexicalization. While no submissions were received for the latter, the former attracted six teams who submitted their systems' results on the provided datasets. This paper provides an overview of QALD-3, discussing the approaches experimented by the participating systems as well as the obtained results.

Abstract. We present a question answering system architecture which processes natural language questions in a pipeline consisting of five steps: i) question parsing and query template generation, ii) lookup in an inverted index, iii) string similarity computation, iv) lookup in a lexical database in order to find synonyms, and v) semantic similarity computation. These steps are ordered with respect to their computational effort, following the idea of layered processing: questions are passed on along the pipeline only if they cannot be answered on the basis of earlier processing steps, thereby invoking computationally expensive operations only for complex queries that require them. In this paper we present an evaluation of the system on the dataset provided by the 2nd Open Challenge on Question Answering over Linked Data (QALD-2). The main, novel contribution is a systematic empirical investigation of the impact of the single processing components on the overall performance of question answering over linked data.

Abstract. While there are many large knowledge bases (e.g. Freebase, Yago, DBpedia) as well as linked data sets available on the web, they typically lack lexical information stating how the properties and classes are realized lexically. If at all, typically only one label is attached to these properties, thus lacking any deeper syntactic information, e.g. about syntactic arguments and how these map to the semantic arguments of the property as well as about possible lexical variants or paraphrases. While there are lexicon models such as lemon allowing to define a lexicon for a given ontology, the cost involved in creating and maintaining such lexica is substantial, requiring a high manual effort. Towards lowering this effort, in this paper we present a semi-automatic approach that exploits a corpus to find occurrences in which a given property is expressed, and generalizing over these occurrences by extracting dependency paths that can be used as a basis to create lemon lexicon entries. We evaluate the resulting automatically generated lexica with respect to DBpedia as dataset and Wikipedia as corresponding corpus, both in an automatic mode, by comparing to a manually created lexicon, and in a semi-automatic mode in which a lexicon engineer inspected the results of the corpus-based approach, adding them to the existing lexicon if appropriate.

Abstract. Question answering over linked data has emerged in the past years as an important topic of research in order to provide natural language access to a growing body of linked open data on the Web. In this paper we focus on analyzing the lexical gap that arises as a challenge for any such question answering system. The lexical gap refers to the mismatch between the vocabulary used in a user question and the vocabulary used in the relevant dataset. We implement a semantic parsing approach and evaluate it on the QALD-4 benchmark, showing that the performance of such an approach suffers from training data sparseness. Its performance can, however, be substantially improved if the right lexical knowledge is available. To show this, we model a set of lexical entries by hand to quantify the number of entries that would be needed. Further, we analyze if a state-of-the-art tool for inducing ontology lexica from corpora can derive these lexical entries automatically. We conclude that further research and investments are needed to derive such lexical knowledge automatically or semi-automatically.

There is a range of large knowledge bases, such as Freebase and DBpedia, as well as linked data sets available on the web, but they typically lack lexical information stating how the properties and classes they comprise are realized lexically. Often only one label is attached, if at all, thus lacking rich linguistic information, e.g. about morphological forms, syntactic arguments or possible lexical variants and paraphrases. While ontology lexicon models like lemon allow for defining such linguistic information with respect to a given ontology, the cost involved in creating and maintaining such lexica is substantial, requiring a high manual effort. Towards lowering this effort we present ATOLL, a framework for the automatic induction of ontology lexica, based both on existing labels and dependency paths extracted from a text corpus. We instantiate ATOLL with respect to DBpedia as dataset and Wikipedia as corresponding corpus, and evaluate it by comparing the automatically generated lexicon with a manually constructed one. Our results clearly corroborate that our approach shows a high potential to be applied in a semi-automatic fashion in which a lexicon engineer can validate, reject or refine the automatically generated lexical entries, thus having a clear potential to contributing to the reduction the overall cost of creating ontology lexica.