QAnswer: A Question Answering prototype bridging the gap between a considerable part of the LOD cloud and end-users

Abstract

We present QAnswer, a Question Answering system which queries at the same time 3 core datasets of the Semantic Web, that are relevant for end-users. These datasets are Wikidata with Lexemes, LinkedGeodata and Musicbrainz. Additionally, it is possible to query these datasets in English, German, French, Italian, Spanish, Pourtuguese, Arabic and Chinese. Moreover, QAnswer includes a fallback option to the search engine Qwant when the answer to a question cannot be found in the datasets mentioned above. These features make QAnswer as the first prototype of a Question Answering System over a considerable part of the LOD cloud.

Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion

“…Convex enables stand-alone systems. The state-of-the-art QAnswer [7] scores only about 0.011 − 0.064 (since it produces sets and not ranked lists, all metric values are identical) on its own on the incomplete utterances, which it is clearly not capable of addressing. When Convex is applied, its performance jumps significantly to 0.172 − 0.264 ) (MRR) across the domains.…”

“…Our proposed approach, Convex (CONVersational KG-QA with context EXpansion) overcomes these limitations, based on the following key ideas. The initial question is used to identify a small subgraph of the KG for retrieving answers, similar to what prior methods for unsupervised KG-QA use [7]. For incomplete and ungrammatical follow-up questions, we capture context in the form of a subgraph as well, and we dynamically maintain it as the conversation proceeds.…”

“…Specifically, Convex works as follows. Answers to the first question are obtained by any standard KG-QA system (we use the stateof-the-art system QAnswer [7] and other variants in our experiments over Wikidata). Entities in the initial question q 0 , the answer a 0 , and their connections initialize a context subgraph X 1 (X t for turn t) for the conversation in the KG.…”

“…Starting with early approaches in 2012-'13 [5,36,38], based on parsing questions via handcoded templates and grammars, KG-QA already has a rich body of literature. While templates continued to be a strong line of work due to its focus on interpretability and generalizability [1,2,4,7,35], a parallel thread has focused on neural methods driven by performance gains [15,20,31]. Newer trends include shifts towards more complex questions [19,21,34], and fusion of knowledge graphs and text [31,33].…”

IntKB: A Verifiable Interactive Framework for Knowledge Base Completion

“…Prominent examples are, for instance, Wikidata (Vrandečić and Krötzsch, 2014), Freebase (Bollacker et al, 2008), NELL (Carlson et al, 2010), DBpedia (Auer et al, 2007), or Yago (Suchanek et al, 2008). Owed to their structured information, KBs are widely used in various downstream tasks of NLP such as, e. g., entity linking (Mendes et al, 2011), query expansion (Dalton et al, 2014), co-reference resolution (Rahman and Ng, 2011), and question answering (Diefenbach et al, 2019). Yet the performance of downstream NLP tasks is often impeded due to the issue that KBs are incomplete.…”