User interest tracing is a common practice in many Web use-cases including, but not limited to, search, recommendation or intelligent assistants. The overall aim is to provide the user a personalized “Web experience” by aggregating and exploiting a plenitude of user data derived from collected logs, accessed contents, and/or mined community context. As such, fairly basic features such as terms and graph structures can be utilized in order to model a user’s interest. While there are clearly positive aspects in the before mentioned application scenarios, the user’s privacy is highly at risk. In order to highlight inherent privacy risks, this paper studies Semantic User Interest Tracing (SUIT in short) by investigating a user’s publishing/editing behavior of Web contents. In contrast to existing approaches, SUIT solely exploits the (semantic) concepts [categories] inherent in documents derived via entity-level analytics. By doing so, we raise Web contents to the entity-level. Thus, we are able to abstract the user interest from plain text strings to “things”. In particular, we utilize the inherited structural relationships present among the concepts derived from a knowledge graph in order to identify the user associated with a specific Web content. Our extensive experiments on Wikipedia show that our approach outperforms state of the art approaches in tracing and predicting user behavior in a single language. In addition, we also demonstrate the viability of our semantic (language-agnostic) approach in multi-lingual experiments. As such, SUIT is capable of revealing the user’s identity, which demonstrates the fine line between personalization and surveillance, raising questions regarding ethical considerations at the same time.

Digital curation requires substantial human expertise in order to achieve and maintain document collections of high quality. This necessitates usually expert knowledge of a librarian or curator in order to interpret the content and categorize it accordingly. This process is at the same time expensive and time-consuming. With the advent of knowledge bases and the plenitude of information contained within them new opportunities emerge at the horizon. In particular, entity-level analytics allows to semantically enrich contents via linked open data (LOD). To this end, we assess in this paper the approach of concise content annotation as a means of supporting the process of digital curation. In particular, we compare various entity-level annotation methods and highlight the importance of concise semantic tagging based on qualitative as well as quantitative evaluations.

Capturing and exploiting a content’s semantic is a key success factor for Web search. To this end, it is crucial to - ideally automatically - extract the core semantics of the data being processed and link this information with some formal representation, such as an ontology. By intertwining both, search becomes semantic by simultaneously allowing end-users a structured access to the data via the underlying ontology. Connecting both, we introduce the SEMANNOREX framework in order to provide semantically enriched access to a news corpus from Websites and Wikinews.

Digital libraries build on classifying contents by capturing their semantics and (optionally) aligning the description with an underlying categorization scheme. This process is usually based on human intervention, either by the content creator or a curator. As such, this procedure is highly time-consuming and - thus - expensive. In order to support the human in data curation, we introduce an annotation tagging system called “AnnoTag”. AnnoTag aims at providing concise content annotations by employing entity-level analytics in order to derive semantic descriptions in the form of tags. In particular, we are generating “Semantic LOD Tags” (linked open data) that allow an interlinking of the derived tags with the LOD cloud. Based on a qualitative evaluation on Web news articles we prove the viability of our approach and the high-quality of the automatically extracted information.

With the abundance of data and wide access to the internet, a user can be overwhelmed with information. For an average Web user, it is very difficult to identify which information is relevant or irrelevant. Hence, in the era of continuously enhancing Web, organization and interpretation of Web contents are very important in order to easily access the relevant information. Many recent advancements in the area of Web content management such as classification of Web contents, information diffusion, credibility of information, etc. have been explored based on text and semantic of the document. In this paper, we propose a purely semantic contextualization of Web contents. We hypothesize that named entities and their types present in a Web document convey substantial semantic information. By extraction of this information, we aim to study the reasoning and explanation behind the Web contents or patterns. Furthermore, we also plan to exploit LOD (Linked Open Data) to get a deeper insight of Web contents.

Thirty years of the Web have led to a tremendous amount of contents. While contents of the early years have been predominantly “simple” HTML documents, more recent ones have become more and more “machine-interpretable”. Named entities - ideally explicitly and intentionally annotated - pave the way toward a semantic exploration and exploitation of the data. While this appears to be the golden sky toward a more human-centric Web, it not necessarily is. The key-point is simple: “the more the merrier” is not necessarily the case along all dimensions. For instance, each and every named entity provides via the Web of data a plenitude of information potentially overwhelming the end-user. In particular, named entities are predominantly annotated with multiple types without any order of importance associated. In order to depict the most concise type information, we introduce an approach towards Pattern Utilization for Representative Entity type classification called PURE. To this end, PURE aims at exploiting solely structural patterns derived from knowledge graphs in order to “purify” the most representative type(s) associated with a named entity. Our experiments with named entities in Wikipedia demonstrate the viability of our approach and improvement over competing strategies.

Web users these days are confronted with an abundance of information. While this is clearly beneficial in general, there is a risk of “information overload”. To this end, there is an increasing need of filtering, classifying and/or summarizing Web contents automatically. In order to help consumers in efficiently deriving the semantics from Web contents, we have developed the CALVADOS (Content AnaLytics ViA Digestion Of Semantics) system. To this end, CALVADOS raises contents to the entity-level and digests its inherent semantics. In this demo, we present how entity-level analytics can be employed to automatically classify the main topic of a Web content and reveal the semantic building blocks associated with the corresponding document.

In this paper, we propose a system for aspect-based sentiment analysis (ABSA) by incorporating the concepts of multi-objective optimization (MOO), distributional thesaurus (DT) and unsupervised lexical induction. The task can be thought of as a sequence of processes such as aspect term extraction, opinion target expression identification and sentiment classification. We use MOO for selecting the most relevant features, and demonstrate that classification with the resulting feature set can improve classification accuracy on many datasets. As base learning algorithms we make use of Support Vector Machines (SVM) for sentiment classification and Conditional Random Fields (CRF) for aspect term and opinion target expression extraction tasks. Distributional thesaurus and unsupervised DT prove to be effective with enhanced performance. Experiments on benchmark setups of SemEval-2014 and SemEval-2016 shared tasks show that we achieve the state of the art on aspect-based sentiment analysis for several languages.

This paper reports the IIT-TUDA participation in the SemEval 2016 shared Task 5 of Aspect Based Sentiment Analysis (ABSA) for subtask 1.We describe our system incorporating domain dependency graph features, distributional thesaurus and unsupervised lexical induction using an unlabeled external corpus for aspect based sentiment analysis.Overall, we submitted 29 runs, covering 7 languages and 4 different domains.Our system is placed first in sentiment polarity classification for the English laptop domain, Spanish and Turkish restaurant reviews, and opinion target expression for Dutch and French in restaurant domain, and scores in medium ranks for aspect category identification and opinion target extraction.