Link prediction on knowledge graphs is useful in numerous application areas such as semantic search, question answering, entity disambiguation, enterprise decision support, recommender systems and so on. While many of these applications require a reasonably quick response and may operate on data that is constantly changing, existing methods often lack speed and adaptability to cope with these requirements. This is aggravated by the fact that knowledge graphs are often extremely large and may easily contain millions of entities rendering many of these methods impractical. In this paper, we address the weaknesses of current methods by proposing Random Semantic Tensor Ensemble (RSTE), a scalable ensemble-enabled framework based on tensor factorization. Our proposed approach samples a knowledge graph tensor in its graph representation and performs link prediction via ensembles of tensor factorization. Our experiments on both publicly available datasets and real world enterprise/sales knowledge bases have shown that our approach is not only highly scalable, parallelizable and memory efficient, but also able to increase the prediction accuracy significantly across all datasets.

Question Answering (Q&A) from structured data is a technique that may revolutionize enterprise search. A very promising use-case for such technology is Business Intelligence (BI). In order to make BI more accessible to end-users, some efforts have been made in the field of search for existing reports. However, the problem of converting an end-user's natural language input to a valid structured query in an ad-hoc fashion hasn't been sufficiently solved yet. In this paper we present a framework for Q&A systems that operate on structured data. The main innovation is that the framework allows defining a mapping between recognized semantics of a user's questions to a structured query model that can be executed on arbitrary data sources. It bases on popular standards like RDF and SparQL and is therefore very easy to adapt to other domains or use-cases. We will describe the application of this framework at hand of a BI question answering use-case, which also includes the personalization of generated queries, demonstrating the realworld applicability of our approach. In our experiments, we demonstrate that with our approach one can easily achieve a similar answering quality as one of the most popular Q&A systems on the Web.

Regular expressions are the dominant technique to extract business relevant entities (e.g., invoice numbers or product names) from text data (e.g., invoices), since these entity types often follow a strict underlying syntactical pattern. However, the manual construction of regular expressions that guarantee a high recall and precision is a tedious manual task and requires expert knowledge. In this paper, we propose an approach that automatically infers regular expressions from a set of (positive) sample entities, which in turn can be derived either from enterprise databases (e.g., a product catalog) or annotated documents (e.g., historical invoices). The main innovation of our approach is that it learns effective regular expressions that can be easily interpreted and modified by a user. The effectiveness is obtained by a novel method that weights dependent entity features of different granularity (i.e. on character and token level) against each other and selects the most suitable ones to form a regular expression.

Enterprise Search (ES) is different from traditional IR due to a number of reasons, among which the high level of ambiguity of terms in queries and documents and existence of graph-structured enterprise data (ontologies) that describe the concepts of interest and their relationships to each other, are the most important ones. Our method identifies concepts from the enterprise ontology in the query and corpus. We propose a ranking scheme for ontology sub-graphs on top of approximately matched token q-grams. The ranking leverages the graph-structure of the ontology to incorporate not explicitly mentioned concepts. It improves previous solutions by using a fine-grained ranking function that is specifically designed to cope with high levels of ambiguity. This method is able to capture much more of the semantics of queries and documents than previous techniques. We prove this claim by an evaluation of our method in three real-life scenarios from two different domains, and found it to consistently be superior both in terms of precision and recall.

Current forum search technologies lack the ability to identify threads with near-duplicate content and to group these threads in the search results. As a result, forum users are overloaded with duplicated search results and prefer to create new threads without trying to find existing ones. In this paper we therefore identify common reasons leading to near-duplicates and develop a new near-duplicate detection algorithm for forum threads. The algorithm is implemented using a large case study of a real-world forum serving more than one million users. We compare this work with current algorithms, similar to [4, 5], for detecting near-duplicates on machine generated web pages. Our preliminary results show, that we significantly outperform these algorithms and that we are able to group forum threads with a precision of 74%.

Developer communities built around software products, like the SAP Community Network, provide a knowledge base for reocurring problems and their solutions. Due to the large amount of content maintained in such communities, e.g., in forums, finding relevant solutions is a major challenge beyond the scope of common keyword-based search engines. In fact, it is measured that around 50% of the forum questions of our particular scenario have already been answered at the time they are posted. We target this challenge by an entity aware search, which exploits structured knowledge, such as domain-specific ontologies, for both query interpretation and creation of document indexes. The system takes a natural language query as input, interprets it as an entity graph, matches this graph with pre-processed content and supports the user in refining his query based on the top-k relevant entities. Results are presented in a user interface that supports faceted search based on entities. Additionally, the user interface is structured according to possible search intentions of users. The evaluation of our system on the SCN scenario yields that the top 5 entities in user queries are recognized with a precision of 83% compared to 61% of state of the art algorithms.