Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Artificial Intelligence
Title: MSBGA: A Multi-Document Summarization System Based on Genetic Algorithm
Abstract: The multi-document summarizer using genetic algorithm-based sentence extraction (MSBGA) regards summarization process as an optimization problem where the optimal summary is chosen among a set of summaries formed by the conjunction of the original articles sentences. To solve the NP hard optimization problem, MSBGA adopts genetic algorithm, which can choose the optimal summary on global aspect. The evaluation function employs four features according to the criteria of a good summary: satisfied length, high coverage, high informativeness and low redundancy. To improve the accuracy of term frequency, MSBGA employs a novel method TFS, which takes word sense into account while calculating term frequency. The experiments on DUC04 data show that our strategy is effective and the ROUGE-1 score is only 0.55% lower than the best participant in DUC04
Extracted statement — implementation: MSBGA
Research field: Artificial Intelligence
Title: Transforming XML documents to OWL ontologies: A survey
Abstract: The aims of XML data conversion to ontologies are the indexing, integration and enrichment of existing ontologies with knowledge acquired from these sources. The contribution of this paper consists in providing a classification of the approaches used for the conversion of XML documents into OWL ontologies. This classification underlines the usage profile of each conversion method, providing a clear description of the advantages and drawbacks belonging to each method. Hence, this paper focuses on two main processes, which are ontology enrichment and ontology population using XML data. Ontology enrichment is related to the schema of the ontology (TBox), and ontology population is related to an individual (Abox). In addition, the ontologies described in these methods are based on formal languages of the Semantic Web such as OWL (Ontology Web Language) or RDF (Resource Description Framework). These languages are formal because the semantics are formally defined and take advantage of the Description Logics. In contrast, XML data sources are without formal semantics. The XML language is used to store, export and share data between processes able to process the specific data structure. However, even if the semantics is not explicitly expressed, data structure contains the universe of discourse by using a qualified vocabulary regarding a consensual agreement. In order to formalize this semantics, the OWL language provides rich logical constraints. Therefore, these logical constraints are evolved in the transformation of XML documents into OWL documents, allowing the enrichment and the population of the target ontology. To design such a transformation, the current research field establishes connections between OWL constructs (classes, predicates, simple or complex data types, etc.) and XML constructs (elements, attributes, element lists, etc.). Two different approaches for the transformation process are exposed. The instance approaches are based on XML documents without any schema associated. The validation approaches are based on the XML schema and document validated by the associated schema. The second approaches benefit from the schema definition to provide automated transformations with logic constraints. Both approaches are discussed in the text.
Extracted statement — Output format: OWL
Research field: Artificial Intelligence
Title: A GRAPH-BASED TOOL FOR THE TRANSLATION OF XML DATA TO OWL-DL ONTOLOGIES: 
Abstract: Today most of the data exchanged between information systems is done with the help of the XML syntax. Unfortunately when these data have to be integrated, the integration becomes difficult because of the semantics' heterogeneity. Consequently, leading researches in the domain of database systems are moving to semantic model in order to store data and its semantics definition. To benefit from these new systems and technologies, and to integrate different data sources, a flexible method consists in populating an existing OWL ontology from XML data. In paper we present such a method based on the definition of a graph which represents rules that drive the populating process. The graph of rules facilitates the mapping definition that consists in mapping elements from an XSD schema to the elements of the OWL schema.
Extracted statement — Output format: OWL
Research field: Artificial Intelligence
Title: DTD2OWL: automatic transforming XML documents into OWL ontology
Abstract: DTD and its instance have been considered the standard for data representation and information exchange format on the current web. However, when coming to the next generation of web, the Semantic Web, the drawbacks of XML and its schema are appeared. They mainly focus on the structure level and lack support for data representation. Meanwhile, some Semantic Web applications such as intelligent information services and semantic search engines require not only the syntactic format of the data, but also the semantic content. These requirements are supported by the Web Ontology Language (OWL), which is one of the recent W3C recommendation. But nowadays the amount of data presented in OWL is small in compare with XML data. Therefore, finding a way to utilize the available XML documents for the Semantic Web is a current challenge research. In this work we present an effective solution for transforming XML document into OWL domain knowledge. While keeping the original structure, our work also adds more semantics for the XML document. Moreover, whole of the transformation processes are done automatically without any outside intervention. Further, unlike previous approaches which focus on the schema level, we also extend our methodology for the data level by transforming specific XML instances into OWL individuals. The results in existing OWL syntaxes help them to be loaded immediately by the Semantic Web applications.
Extracted statement — Output format: OWL
Research field: Artificial Intelligence
Title: Mapping XML to OWL Ontologies
Abstract: By now, XML has reached a wide acceptance as data exchange format in E-Business. An efficient collaboration between different participants in E-Business thus, is only possible, when business partners agree on a common syntax and have a common understanding of the basic concepts in the domain. XML covers the syntactic level, but lacks support for efficient sharing of conceptualizations. The Web Ontology Language (OWL [Bec04]) in turn supports the representation of domain knowledge using classes, properties and instances for the use in a distributed environment as the WorldWideWeb. We present in this paper a mapping between the data model elements of XML and OWL. We give account about its implementation within a ready-to-use XSLT framework, as well as its evaluation for common use cases.
Extracted statement — Output format: OWL
Research field: Artificial Intelligence
Title: An Approach For Transforming of Relational Databases to OWL Ontology
Abstract: Rapid growth of documents, web pages, and other types of text content is a huge challenge for the modern content management systems. One of the problems in the areas of information storage and retrieval is the lacking of semantic data. Ontologies can present knowledge in sharable and repeatedly usable manner and provide an effective way to reduce the data volume overhead by encoding the structure of a particular domain. Metadata in relational databases can be used to extract ontology from database in a special domain. According to solve the problem of sharing and reusing of data, approaches based on transforming relational database to ontology are proposed. In this paper we propose a method for automatic ontology construction based on relational database. Mining and obtaining further components from relational database leads to obtain knowledge with high semantic power and more expressiveness. Triggers are one of the database components which could be transformed to the ontology model and increase the amount of power and expressiveness of knowledge by presenting part of the knowledge dynamically
Extracted statement — Output format: OWL
Research field: Artificial Intelligence
Title: Automatic Constructing OWL Ontology from Relational Database Schema: 
Abstract: In this paper we present a new tool, called DB_DOOWL, for creating domain ontology from relational database schema (RDBS). In contrast with existing transformation approaches, we propose a generic solution based on automatic instantiation of a specified meta-ontology. This later is an owl ontology which describes any database structure. A prototype of our proposed tool is implemented based on Jena in Java in order to demonstrate its feasibility.
Extracted statement — Output format: OWL
Research field: Artificial Intelligence
Title: Learning ontology from relational database
Abstract: Ontology provides a shared and reusable piece of knowledge about a specific domain, and has been applied in many fields, such as semantic Web, e-commerce and information retrieval, etc. However, building ontology by hand is a very hard and error-prone task. Learning ontology from existing resources is a good solution. Because relational database is widely used for storing data and OWL is the latest standard recommended by W3C, this paper proposes an approach of learning OWL ontology from data in relational database. Compared with existing methods, the approach can acquire ontology from relational database automatically by using a group of learning rules instead of using a middle model. In addition, it can obtain OWL ontology, including the classes, properties, properties characteristics, cardinality and instances, while none of existing methods can acquire all of them. The proposed learning rules have been proven to be correct by practice.
Extracted statement — Output format: OWL
Research field: Artificial Intelligence
Title: Description of the POMELO System for the Task 2 of QALD-2014
Abstract: In this paper, we present the POMELO system developed for participating in the task 2 of the QALD-4 challenge. For translating natural language questions in SPARQL queries we exploit Natural Language Processing methods, semantic resources and RDF triples description. We designed a four-step method which pre-processes the question, performs an abstraction of the question, then builds a representation of the SPARQL query and finally generates the query. The system was ranked second out of three participating systems. It achieves good performance with 0.85 F-measure on the set of 25 test questions.
Extracted statement — implementation: POMELO
Research field: Artificial Intelligence
Title: Transforming XML documents to OWL ontologies: A survey
Abstract: The aims of XML data conversion to ontologies are the indexing, integration and enrichment of existing ontologies with knowledge acquired from these sources. The contribution of this paper consists in providing a classification of the approaches used for the conversion of XML documents into OWL ontologies. This classification underlines the usage profile of each conversion method, providing a clear description of the advantages and drawbacks belonging to each method. Hence, this paper focuses on two main processes, which are ontology enrichment and ontology population using XML data. Ontology enrichment is related to the schema of the ontology (TBox), and ontology population is related to an individual (Abox). In addition, the ontologies described in these methods are based on formal languages of the Semantic Web such as OWL (Ontology Web Language) or RDF (Resource Description Framework). These languages are formal because the semantics are formally defined and take advantage of the Description Logics. In contrast, XML data sources are without formal semantics. The XML language is used to store, export and share data between processes able to process the specific data structure. However, even if the semantics is not explicitly expressed, data structure contains the universe of discourse by using a qualified vocabulary regarding a consensual agreement. In order to formalize this semantics, the OWL language provides rich logical constraints. Therefore, these logical constraints are evolved in the transformation of XML documents into OWL documents, allowing the enrichment and the population of the target ontology. To design such a transformation, the current research field establishes connections between OWL constructs (classes, predicates, simple or complex data types, etc.) and XML constructs (elements, attributes, element lists, etc.). Two different approaches for the transformation process are exposed. The instance approaches are based on XML documents without any schema associated. The validation approaches are based on the XML schema and document validated by the associated schema. The second approaches benefit from the schema definition to provide automated transformations with logic constraints. Both approaches are discussed in the text.
Extracted statement — Output format: RDF
Research field: Artificial Intelligence
Title: Improving sequential recommendation with knowledge-enhanced memory networks
Abstract: With the revival of neural networks, many studies try to adapt powerful sequential neural models, ıe Recurrent Neural Networks (RNN), to sequential recommendation. RNN-based networks encode historical interaction records into a hidden state vector. Although the state vector is able to encode sequential dependency, it still has limited representation power in capturing complicated user preference. It is difficult to capture fine-grained user preference from the interaction sequence. Furthermore, the latent vector representation is usually hard to understand and explain. To address these issues, in this paper, we propose a novel knowledge enhanced sequential recommender. Our model integrates the RNN-based networks with Key-Value Memory Network (KV-MN). We further incorporate knowledge base (KB) information to enhance the semantic representation of KV-MN. RNN-based models are good at capturing sequential user preference, while knowledge-enhanced KV-MNs are good at capturing attribute-level user preference. By using a hybrid of RNNs and KV-MNs, it is expected to be endowed with both benefits from these two components. The sequential preference representation together with the attribute-level preference representation are combined as the final representation of user preference. With the incorporation of KB information, our model is also highly interpretable. To our knowledge, it is the first time that sequential recommender is integrated with external memories by leveraging large-scale KB information.
Extracted statement — Machine Learning Method: RNN
Research field: Artificial Intelligence
Title: A HMM-based approach to question answering against linked data
Abstract: In this paper, we present a QA system enabling NL questions against Linked Data, designed and adopted by the Tor Vergata University AI group in the QALD-3 evaluation. The system integrates lexical semantic modeling and statistical inference within a complex architecture that decomposes the NL interpretation task into a cascade of three different stages: (1) The selection of key ontological information from the question (i.e. predicate, arguments and properties), (2) the location of such salient information in the ontology through the joint disambiguation of the different candidates and (3) the compilation of the final SPARQL query. This architecture characterizes a novel approach for the task and exploits a graphical model (i.e. an Hidden Markov Model) to select the proper ontological triples according to the graph nature of RDF. In particular, for each query an HMM model is produced whose Viterbi solution is the comprehensive joint disambiguation across the sentence elements. The combination of these approaches achieved interesting results in the QALD competition. The RTV is in fact within the group of participants performing slightly below the best system, but with smaller requirements and on significantly poorer input information.
Extracted statement — implementation: RTV
Research field: Artificial Intelligence
Title: Automated text summarization and the SUMMARIST system
Abstract: This paper consists of three parts: a preliminary typology of summaries in general; a description of the current and planned modules and performance of the SUMMARIST automated multilingual text summarization system being built sat ISI, and a discussion of three methods to evaluate summaries.
Extracted statement — implementation: SUMMARIST
Research field: Artificial Intelligence
Title: Generating Natural Language Summaries from Multiple On-Line Sources
Abstract: We present a methodology for summarization of news about current events in the form of briefings that include appropriate background (historical) information. The system that we developed, SUMMONS, uses the output of systems developed for the DARPA Message Understanding Conferences to generate summaries of multiple documents on the same or related events, presenting similarities and differences, contradictions, and generalizations among sources of information. We describe the various components of the system, showing how information from multiple articles is combined, organized into a paragraph, and finally, realized as English sentences. A feature of our work is the extraction of descriptions of entities such as people and places for reuse to enhance a briefing.
Extracted statement — implementation: SUMMONS
Research field: Artificial Intelligence
Title: TIARA: A Visual Exploratory Text Analytic System
Abstract: In this paper, we present a novel exploratory visual analytic system called TIARA (Text Insight via Automated Responsive Analytics), which combines text analytics and interactive visualization to help users explore and analyze large collections of text. Given a collection of documents, TIARA first uses topic analysis techniques to summarize the documents into a set of topics, each of which is represented by a set of keywords. In addition to extracting topics, TIARA derives time-sensitive keywords to depict the content evolution of each topic over time. To help users understand the topic-based summarization results, TIARA employs several interactive text visualization techniques to explain the summarization results and seamlessly link such results to the original text. We have applied TIARA to several real-world applications, including email summarization and patient record analysis. To measure the effectiveness of TIARA, we have conducted several experiments. Our experimental results and initial user feedback suggest that TIARA is effective in aiding users in their exploratory text analytic tasks.
Extracted statement — implementation: TIARA
Research field: Artificial Intelligence
Title: UWN: A Large Multilingual Lexical Knowledge Base
Abstract: We present UWN, a large multilingual lexical knowledge base that describes the meanings and relationships of words in over 200 languages. This paper explains how link prediction, information integration and taxonomy induction methods have been used to build UWN based on WordNet and extend it with millions of named entities from Wikipedia. We additionally introduce extensions to cover lexical relationships, frame-semantic knowledge, and language data. An online interface provides human access to the data, while a software API enables applications to look up over 16 million words and names.
Extracted statement — implementation: UWN
Research field: Artificial Intelligence
Title: A GRAPH-BASED TOOL FOR THE TRANSLATION OF XML DATA TO OWL-DL ONTOLOGIES: 
Abstract: Today most of the data exchanged between information systems is done with the help of the XML syntax. Unfortunately when these data have to be integrated, the integration becomes difficult because of the semantics' heterogeneity. Consequently, leading researches in the domain of database systems are moving to semantic model in order to store data and its semantics definition. To benefit from these new systems and technologies, and to integrate different data sources, a flexible method consists in populating an existing OWL ontology from XML data. In paper we present such a method based on the definition of a graph which represents rules that drive the populating process. The graph of rules facilitates the mapping definition that consists in mapping elements from an XSD schema to the elements of the OWL schema.
Extracted statement — Input format: XSD
Research field: Artificial Intelligence
Title: Building ontologies from XML data sources
Abstract: In this paper, we present a tool called X2OWL that aims at building an OWL ontology from an XML datasource. This method is based on XML schema to automatically generate the ontology structure, as well as, a set of mapping bridges. The presented method also includes a refinement step that allows to clean the mapping bridges and possibly to restructure the generated ontology.
Extracted statement — Approaches: X2OWL
Research field: Artificial Intelligence
Title: Automatic Constructing OWL Ontology from Relational Database Schema: 
Abstract: In this paper we present a new tool, called DB_DOOWL, for creating domain ontology from relational database schema (RDBS). In contrast with existing transformation approaches, we propose a generic solution based on automatic instantiation of a specified meta-ontology. This later is an owl ontology which describes any database structure. A prototype of our proposed tool is implemented based on Jena in Java in order to demonstrate its feasibility.
Extracted statement — Learning tool: DB_DOOWL
Research field: Artificial Intelligence
Title: FEMsum at DUC 2007
Abstract: This paper describes and analyzes how the FEMsum system deals with DUC 2007 tasks of providing summary-length answers to complex questions, both background and just-the-news summaries. We participated in producing background summaries for the main task with the FEMsum approach that obtained better results in our last year participation. The FEMsum semantic based approach was adapted to deal with the update pilot task with the aim of producing just-the-news summaries.
Extracted statement — implementation: FEMsum
Research field: Artificial Intelligence
Title: GOFAIsum: a symbolic summarizer for DUC
Abstract: This article presents GOFAISUM, a topicanswering and summarizing system developed for the main task of DUC 2007 by the Université de Montréal and the Université de Genève. We chose to use an all-symbolic approach, the only source of linguistic knowledge being FIPS, a multilingual syntactic parser. We further attempted to innovate by using XML and XSLT to both represent FIPS’s analysis trees and to manipulate them to create summaries. We relied on tf·idf -like scores to ensure relevance to the topic and on syntactic pruning to achieve conciseness and grammaticality. NIST evaluation metrics show that our system performs well with respect to summary responsiveness and linguistic quality, but could be improved to reduce redundancy.
Extracted statement — implementation: GOFAIsum
Research field: Artificial Intelligence
Title: Answering natural language questions with Intui3
Abstract: Intui3 is one of the participating systems at the fourth evaluation campaign on multilingual question answering over linked data, QALD4. The system accepts as input a question formulated in natural language (in English), and uses syntactic and semantic information to construct its interpretation with respect to a given database of RDF triples (in this case DBpedia 3.9). The interpretation is mapped to the corresponding SPARQL query, which is then run against a SPARQL endpoint to retrieve the answers to the initial question. Intui3 competed in the challenge called Task 1: Multilingual question answering over linked data, which offered 200 training questions and 50 test questions in 7 different languages. It obtained an F-measure of 0.24 by providing a correct answer to 10 of the test questions and a partial answer to 4 of them.
Extracted statement — implementation: Intui3