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.
Abstract-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 usecases. 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.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.