Introduction to neural network based approaches for question answering over knowledge graphs. Finally, an indexing process is applied to the detected phrases to efficiently extract the corresponding entities from DBpedia [29]. In: Proceedings of the 2018 World Wide Web Conference 2018. Afterwards, natural language questions are composed by manually correcting the generated NNQTs [34]. Journal of Big Data
Google Scholar.
For the LC-QuAD dataset we achieve the highest \(F_1\)-score for list queries, followed by Boolean and count queries. While existing systems simplify user access, there is still room for improvement in the accuracy of these systems. 2022 BioMed Central Ltd unless otherwise stated. We follow the approach proposed in [9]. Your
The basic idea of generating all possible triple patterns is taken from previous research [9]. For instance, the property http://dbpedia.org/ontology/postalCode. In the third step, the candidate queries are ranked based on five features including the number of variables and triples in the query, the number of the words in the question which are covered by the query, the sum of the relevance of the resources and the edit distance between the resource and the word. MathSciNet Therefore, the questions in LC-QuAD contain much fewer noisy patterns compared to other collected natural language questions.
You can get your own at API Key by filling out a few forms. Here is the beginning of the Turtle version of the search result for the person Charles Schwab: The first instance in the data is an item list.
One example question could be Who is the wife of Obama?.
Springer. EARL uses two strategies.
The ranking model takes into account both the syntactical structure of the question and the tree representation of the candidate queries to select the most plausible SPARQL query which represents the correct intention behind the question. In particular, we focus our discussions on systems that are most relevant for understanding the contributions of our proposed QA system.
For instance, the generated SPARQL queries contain at most two triple patterns. 
Affolter K, Stockinger K, Bernstein A. When you feed RDF to riot, it can usually guess the serialization from the end of the input filename, but when piping data to it from stdout like I do above, you need the --syntax parameter to tell it what flavor of RDF you are feeding it.
Google Scholar. The Semantic Web-ISWC 2017.
In reality, many more types of questions are commonly used. For instance, dbr:French_Polynesia is a mapped resource and dbo:capital is a mapped property in the example question Who is the mayor of the capital of French Polynesia?. Algorithm 1 summarizes the process of constructing set S of all possible triples and set Q of all possible queries, where \(E'\) is the set of all mapped resources, \(P'\) is the set of all mapped properties and K is the underlying knowledge graph. Afterwards, the type of the question is identified and phrases in the question are mapped to corresponding resources and properties in the underlying RDF knowledge graph.
languages. Kullback S. Information Theory and Statistics. At each step, a relevant task is solved independently by one individual software component. Afterwards, a QA optimization algorithm is implemented in two steps to automatically build the final QA pipeline by selecting the best performing QA components from the 29 reusable QA components based on the questions. Over the past decade knowledge graphs have been increasingly adopted to structure and describe data in various fields like education, biology [1] or social media [2]. In addition, we use a Tree-LSTM to compute the similarity between NL questions and SPARQL queries as the ranking score instead of the five simple features selected by the authors of [22]. The Random Forest Classifier was trained on the training dataset. https://www.w3.org/TR/2014/REC-rdf11-concepts-20140225/#dfn-literal. 2001;45(1):532. The architecture of the proposed QA system includes five components for five different tasks.
Because the request sends a query to Google, just like a search entered at www.google.com, the server actually returns a list of search results.
For instance, in order to identify Resources in a natural language question, we use DBpedia Spotlight [29], TagMe [30], EARL [31] and Falcon [32].
In: Plant Bioinformatics, pp. https://doi.org/10.1108/00330331211221828. We build on the modular design of the Frankenstein framework [10] and the SPARQL Query Generator (SQG) [9]. represents the postal code of a place. Proceedings of the ACM SIGMOD International Conference on Management of Data.
We use Tree-LSTM to map the input question and the candidate queries to latent space (i.e.
Formal query generation for question answering over knowledge bases.
Springer, Honnibal M, Johnson M. An improved non-monotonic transition system for dependency parsing. Bollacker K, Evans C, Paritosh P, Sturge T, Taylor J. Freebase: A collaboratively created graph database for structuring human knowledge. 2018.
The Parlotones? SIGMOD 08, pp. recommend using data dumps from, Sign up for the Google Developers newsletter.
You can pick apart the URL with the Taylor Swift query and then reassemble it with new pieces using the Google Knowledge Graph API Reference. Fig.3 shows the mapped resources, properties and classes for the example question: Who is the mayor of the capital of French Polynesia?, The phrase mapping result for the example question: Who is the mayor of the capital of French Polynesia?. In: European Semantic Web Conference, 2018;714728.
Frankenstein [10] decomposes the problem into several QA component tasks and builds the whole QA pipeline by integrating 29 state-of-the-art QA components.
Consequently, a SPARQL query will be constructed based on the derived question type. Finally, we would like to compare with the results from SQG. Building natural language interfaces to databases has been a long-standing research challenge for a few decades [13,14,15]. The reduction in performance is mainly due to the different qualities of the datasets.
represents the city Zurich or the string literal CH-ZH that denotes the Zurich region code in DBpedia.
TagMe shows good performance especially when annotating texts that are short and poorly composed.
https://www.w3.org/TR/2014/REC-rdf11-concepts-20140225/#dfn-iri.
Predictively completing entities in a search box. Boutet E, Lieberherr D, Tognolli M, Schneider M, Bairoch A. Uniprotkb/swiss-prot.
They are identified by IRIs and may be described with properties. Google Scholar. CoRR abs/1801.03825 2018;. Hoffart J, Suchanek FM, Berberich K, Weikum G. Yago2: a spatially and temporally enhanced knowledge base from wikipedia.
For instance, a question Who is the mayor of the capital of French Polynesia? can be converted to the lemma representation as Who be the mayor of the capital of French Polynesia?.
Note that sometimes the expected answer to a Count question could be directly extracted as the value of the property in the underlying knowledge graph instead of being calculated by the COUNT SPARQL set function.
Translating from natural language to SPARQL is a hard problem due to the ambiguity of the natural language.
ACM, New York, 2010. https://doi.org/10.1145/1871437.1871689. The reference page also includes a form you can fill out with sample API call parameters to learn about them more interactively than you would by revising a curl command over and over.
These kind of questions usually start with a particular word such as how. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Classes are also resources.
Adding a new operator in the generated queries would require significant work in designing the transformation rules.
To provide the correct result, a QA system needs to understand the users intention. https://doi.org/10.1016/j.websem.2013.05.0062. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. (3) Phrase mapping, i.e.
These high accuracy values are due to the generation mechanism of the LC-QuAD dataset. Vrandei D, Krtzsch M. Wikidata: a free collaborative knowledge base 2014. Semantic Web Challenges.
As a result, this question is treated as of the type List instead of Count. is it a yes/no questions or a count question? Afterwards, the QA pipeline is generated based on the selected components. Finally, the WHERE clause of the SPARQL query is constructed. Finally, by considering the aforementioned, we were able to compare our system with the following state-of-art SPARQL-based QA systems: WDAqua-core1 [22], ganswer2 [35], WDAqua [21] and Frankenstein [10].
Details are discussed in "Methods" section. DBpedia Spotlight also allows users to tune the values of important parameters such as the confidence level and support range to get the trade-off between the coverage and accuracy of the detected resources.
The basic idea is to break up the task of translation from natural language to SPARQL in the following components: (1) Question analysis, i.e. Some examples of how you can use the Knowledge Graph Search API include: For detailed information about the API methods and parameters, see the Hence, the final selected query is more likely to express the true intention of the question and extract the right answer. throughout the whole research project. Their first example sends a search for Taylor Swift; below I have used that example with curl and piped the output through the Jena riot command line utility (not to be confused with DJ Jenna Riot, who I just learned about in a web search) so that I could get Turtle triples of the result. Trivedi P, Maheshwari G, Dubey M, Lehmann J. Lc-quad: A corpus for complex question answering over knowledge graphs. Therefore, the coverage of identified intentions is improved enormously. et al. Artifici Intell. Then we use term frequency-inverse document frequency (TF-IDF) to convert the resulting questions into a numeric feature vector [26].
(Because types is plural you can also specify a comma-delimited list as that parameters value.).
We adopt the basic assumption that the syntactic similarity between between the queries and the input question can be used for ranking. For instance, the IRI http://dbpedia.org/resource/Zurich. Bojanowski P, Grave E, Joulin A, Mikolov T. Enriching word vectors with subword information. Finally, the Boolean question type must contain the keyword ASK in the corresponding SPARQL query. In the first step, the performance of each component is predicted based on the question features and then the best performing QA components are selected based on the predicted performance. Resources are concrete or abstract entities denoted with any Internationalized Resource Identifier (IRI)Footnote 1 or literalFootnote 2. Our in-depth analysis of the failed questions shows that no SPARQL query was generated for 968 questions in LC-QuAD datset and 80 questions in QALD-7 dataset.
For example, a query that uses parameters of query=charles+schwab&type=Corporation returns information about the company with that name, but query=charles+schwab&type=Person returns information about its founder. Machine learn.
Since our paper proposes a solution for querying knowledge graphs, we will now review the major work on QA systems over knowledge graphs such as [10, 20,21,22]. ETH Swiss Federal Institute of Technology, Rmistrasse 101, 8092, Zurich, Switzerland, Zurich University of Applied Sciences, Obere Kirchgasse 2, 8400, Winterthur, Switzerland, Kurt Stockinger,Maria Anisimova&Manuel Gil, SIB Swiss Institute of Bioinformatics, Quartier Sorge-Btiment Amphiple, 1015, Lausanne, Switzerland, Tarcisio Mendes de Farias,Maria Anisimova&Manuel Gil, Department of Ecology and Evolution, University of Lausanne, Quartier Sorge-Btiment Biophore, 1015, Lausanne, Switzerland, You can also search for this author in This search result points to information about the steel magnate, which identifies him with his Freebase ID, and it also has a search result score.
Trivedi P, Maheshwari G, Dubey M, Lehmann J. Lc-quad: A corpus for complex question answering over knowledge graphs.
Note that the deep learning model Tree-LSTM does not outperform simple classical machine learning models such as SVM and Random Forest for this specific classification task. What is more, each component in our QA system is reusable and can be integrated with other components to construct a new QA system that further improves the performance. First, the input question is processed by the question analysis component, based solely on syntactic features. Below we discuss these systems in more detail.
In the question type classification component, the LC-QuAD dataset was split into 80% / 20% for the training dataset and test dataset, respectively.
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1905.08205. J Big Data 8, 3 (2021).
but as you can see decided to go with the Knowledge Graph anglenot because this term is a popular way to talk about graph databases in general, but because were pulling data from the graph that Google itself is calling a Knowledge Graph. Atom feed (summarized entries) To automatically derive the question type, we first convert each word of the original question into its lemma representation. The Google Knowledge Base API doesnt return a large amount of data for each entity, but when you have the Freebase ID, you can use it to retrieve additional data about that entity from Wikidata. . ganswer2 [20] answers natural language questions through a graph data-driven solution composed of offline and online phases. The first is the List question type, to which belong most common questions, according to our analysis of the available datasets (see "Results" section for details). The first one is based on reducing the problem to an instance of the Generalized Travelling Salesman problem and the second one uses machine learning in order to exploit the connection density between nodes in the knowledge graph [31]. More specifically, Tree-LSTM incorporates information not only from an input vector but also from the hidden states of arbitrarily many child units. EARL is a tool for resource and property mapping as a joint task. Through a careful design, including the choice of components, our system outperforms the state-of-the art, while requiring minimal training data.
We thank the Swiss National Science foundation for funding (NRP 75, grant 407540_167149). Ferragina P, Scaiella U. Tagme: On-the-fly annotation of short text fragments (by wikipedia entities).
Recall that a SPARQL query is comprised of graph patterns in the form of
Among these 215 questions, 7 questions contain a COUNT aggregator, 28 questions are Boolean questions and the remaining 180 questions belong to the type of List questions, i.e. You'll also need to insert your own API key.). This component determines the SELECT clause in the SPARQL query. We tested our QA system on 2430 questions in the LC-QuAD dataset which are still applicable to the latest SPARQL endpoint version (2019-06). Xu X, Liu C, Song D. Sqlnet: Generating structured queries from natural language without reinforcement learning. Cham: Springer; 2017. p. 5969. Our proposed QA system first identifies the type of each question by training a Random Forest model. This API is not suitable for use as a production-critical service. VLDB J. As discussed in "Question type classification" section, the component Question Type Generation is responsible for determining if a question falls into the category List, Count or Boolean.
The expected answer is of a Boolean value - either True or False. We tested various machine learning methods including Support-Vector Machine (SVM), Random Forest and Tree-LSTM to classify the questions of the two datasets. The NER-component recognizes Zurich as an entity which could have the three meanings as mentioned above. Privacy
We choose to examine only the subgraph containing the mapped resources and properties instead of traversing the whole underlying knowledge graph.
Dependency parsing is the process of analyzing the syntactic structure of a sentence to establish semantic relationships between its components. Here we describe the details of our proposed QA system.
The QALD dataset is not one single benchmark but a series of evaluation challenges for Question Answering systems over linked data. (4) Query generation, i.e. WDAqua-core1 [22] constructs queries in four consecutive steps: question expansion, query construction, query ranking and answer decision. It is shown to have the best overall performance on the LC-QuAD dataset, although its overall macro performance is poor. In addition, advanced query ranking mechanisms which could better capture the intention behind questions could also be useful in improving the recall on Boolean questions.
In order to make this system fully independent of the underlying knowledge graph, and for it to be easily transferable to a new domain, the models used in this component could be changed to more general models.
One important design question might concern our choice of a modular architecture, rather than an end-to-end system. Like this, our QA system can be easily applied to newly unseen data domains.
Both strategies are shown to produce good results for entity and relationship mapping tasks.
\({\texttt {
These knowledge graphs are often composed of millions or billions of nodes and edges, and are published in the Resource Description Framework (RDF).
2019;2019: baz106. Modern Information Retrieval, vol.
We extended the query generation algorithm in [9] to include more complex queries. The values of hyperparameters used in the query ranking step are summarized in Table1.
Zafar H, Napolitano G, Lehmann J. ACM, New York 2008. https://doi.org/10.1145/1376616.1376746. Experimental results show that our QA system outperforms the state-of-art systems by 15% on the QALD-7 dataset and by 48% on the LC-QuAD dataset, respectively. All other components are independent of the underlying knowledge graph and therefore can be applied to another knowledge domain without being modified. Since the desired query should capture the intention of the input question, the candidate queries that are syntactically most similar to the input question should rank highest. Knowledge graphs are a powerful concept for querying large amounts of data. The first component of our QA system analyzes natural language questions based solely on syntactic features.
Then, an ensemble approach comprised of various entity recognition and property mapping tools is used in the phrase mapping task. Daiber J, Jakob M, Hokamp C, Mendes PN. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. When these pieces are all assembled together like this, they make it easier to incorporate Google Knowledge Graph data into the wide range of RDF-based tools that are out there. Androutsopoulos I, Ritchie GD, Thanisch P. Natural language interfaces to databases-an introduction. The Beach Boys ranked at 111, well below many groups Ive never heard of that, like the Trammps, didnt even have bea anywhere in their name: Vansire? The second type is the Count question type, where the keyword COUNT exists in the corresponding SPARQL query. : Courier Corporation; 1997. Performance of each question type on LC-QuAD dataset, Performance of each question type on QALD-7 dataset. Wang B, Shin R, Liu X, Polozov O, Richardson M. Rat-sql: Relation-aware schema encoding and linking for text-to-sql parsers.
We used a gradient-based Adagrad Optimizer [37] with a mini batch size of 25 examples. rather than graphs of interconnected entities.