Pasquale Lops

University of Bari Aldo Moro

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Papers

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i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Giovanni Semeraro

University of Bari Aldo Moro

238

Marco de Gemmis

University of Bari Aldo Moro

203

Cataldo Musto

University of Bari Aldo Moro

108

Pierpaolo Basile

University of Bari Aldo Moro

Fedelucio Narducci

Polytechnic University of Bari

Marco Polignano

University of Bari Aldo Moro

Alexander Felfernig

Graz University of Technology

Martijn C. Willemsen

Eindhoven University of Technology

Peter Brusilovsky

University of Pittsburgh

Lucia Siciliani

University of Bari Aldo Moro

Cooperative Institutions

University of Bari Aldo Moro

University of Turin

Polytechnic University of Bari

University of Cagliari

Fraunhofer Institute for Secure Information Technology

National Research Council

University of Calabria

Instituto Politécnico Nacional

University of Groningen

Center for Advanced Studies Research and Development in Sardinia

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A Personalized Information Search Process Based on Dialoguing Agents and User Profiling

Lecture notes in computer science (2003)

Giovanni Semeraro Marco de Gemmis Pasquale Lops Ulrich Thiel Marcello L’Abbate

Profiling (computer programming)

User profile

10.1007/3-540-36618-0_49

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Citations (2)

Generating post hoc review-based natural language justifications for recommender systems

User Modeling and User-Adapted Interaction (2020)

Cataldo Musto Marco de Gemmis Pasquale Lops Giovanni Semeraro

Implementation

Natural language understanding

Natural Language Generation

10.1007/s11257-020-09270-8

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Citations (37)

Personalization in Digital Libraries: An Intelligent Service based on Semantic User Profiles.

Italian Research Conference on Digital Library Management Systems (2007)

Giovanni Semeraro Pasquale Lops Marco de Gemmis Pierpaolo Basile Anna Lisa Gentile

Suppose you registered to a large scientific congress and you got from the Web site the conference program containing a long list of papers which will be presented. Which presentations do you choose to attend? Usually either you try to guess the most interesting talks from their titles and authors or you are forced to have a quick look at the conference proceedings. A recommender system able to learn your research interests from the latest papers you wrote or read, and use them to provide suggestions, might be of valuable help for you in this scenario. Content-based recommenders analyze documents previously rated by a target user, and build a profile exploited to recommend new interesting documents. One of the main limitations of traditional keyword-based approaches is that they are unable to capture the semantics of the user interests, due to the natural language ambiguity. We developed a semantic recommender system, called ITem Recommender, able to disambiguate documents before using them to learn the user profile. The Conference Participant Advisor service relies on the profiles learned by ITem Recommender to build a personalized conference program, in which relevant talks are highlighted according to the participant interests.

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Proceedings of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, IntRS 2017, Como, Italy, August 27, 2017

CEUR Workshop Proceedings (2017)

Peter Brusilovsky Marco de Gemmis Alexander Felfernig Pasquale Lops John O’Donovan

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OIE4PA: Open Information Extraction for the Public Administration

Zenodo (CERN European Organization for Nuclear Research) (2023)

Lucia Siciliani Eleonora Ghizzota Pierpaolo Basile Pasquale Lops

Tenders are powerful means of investment of public funds and represent a strategic development resource.
Despite the efforts made so far by governments at national and international levels to digitalise documents related to the Public Administration sector, most of the information is still available in an unstructured format only.
With the aim of bridging this gap, we present OIE4PA, our latest study on extracting and classifying relations from tenders of the Public Administration.
Our work focuses on the Italian language, where the availability of linguistic resources to perform Natural Language Processing tasks is considerably limited.
For evaluation purposes, we built a dataset composed of 2,000 triples extracted from Italian tenders, which have been manually annotated by two human experts. The dataset, compressed in a single zip file, is composed of: The corpus of 6,262 texts extracted from Italian public tenders (corpus_tenders) The training set of 1,600 annotated triples (training_set) The test set of 400 annotated triples (test_set) The set U of 14,096 triples used for the self-training (u_triples_dd) a compressed archive that contains both the extracted triples and the index for each supervised approach (extraction)

10.5281/zenodo.8331106

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Third Workshop on New Trends in Content-based Recommender Systems (CBRecSys 2016)

Toine Bogers Marijn Koolen Cataldo Musto Pasquale Lops Giovanni Semeraro

While content-based recommendation has been applied successfully in many different domains, it has not seen the same level of attention as collaborative filtering techniques have. However, there are many recommendation domains and applications where content and metadata play a key role, either in addition to or instead of ratings and implicit usage data. For some domains, such as movies, the relationship between content and usage data has seen thorough investigation already, but for many other domains, such as books, news, scientific articles, and Web pages we still do not know if and how these data sources should be combined to provided the best recommendation performance. The CBRecSys 2016 workshop provides a dedicated venue for papers dedicated to all aspects of content-based recommendation.

Content (measure theory)

Web content

10.1145/2959100.2959200

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AI-based decision support system for public procurement

Information Systems (2023)

Lucia Siciliani Vincenzo Taccardi Pierpaolo Basile Marco Di Ciano Pasquale Lops

Tenders are powerful means of investment of public funds and represent a strategic development resource. Thus, improving the efficiency of procuring entities and developing evaluation models turn out to be essential to facilitate e-procurement procedures. With this contribution, we introduce our research to create a supporting system for the decision-making and monitoring process during the entire course of investments and contracts. This system employs artificial intelligence techniques based on natural language processing, focused on providing instruments for extracting useful information from both structured and unstructured (i.e., text) data. Therefore, we developed a framework based on a web app that provides integrated tools such as a semantic search engine, a summarizer, an open information extraction engine in the form of triples (subject–predicate–object) for tender documents, and dashboards for analysing tender data.

Call for bids

10.1016/j.is.2023.102284

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Citations (15)

MQALD

Zenodo (CERN European Organization for Nuclear Research) (2020)

Lucia Siciliani Pierpaolo Basile Pasquale Lops Giovanni Semeraro

Question Answering (QA) over Knowledge Graphs (KG) has the aim of developing a system that is capable of answering users' questions using the information coming from one or multiple Knowledge Graphs, like DBpedia, Wikidata and so on.
This kind of system needs to translate the question of the user, written using natural language, into a query formulated through a data query language that is compliant with the underlying KG.
The translation process is already non-trivial to solve even when trying to answer simple questions that involve a single triple pattern but becomes troublesome when trying to cope with questions that require the presence of modifiers in the final query, i.e. aggregate functions, query forms, and so on.
The attention over this aspect is growing but has never been thoroughly addressed by the existing literature.
Starting from the latest advances in this field, we want to make a further step towards this direction by giving a comprehensive description of this topic and the main issues revolving around it and making publicly available a dataset designed to evaluate the performance of a QA system in translating such articulated questions into a specific data query language.
This dataset has also been used to evaluate the best QA systems available at the state of the art.

10.5281/zenodo.4050353

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Emotion Detection Techniques for the Evaluation of Serendipitous Recommendations

Human-computer interaction series (2016)

Marco de Gemmis Pasquale Lops Giovanni Semeraro

Serendipity

Curiosity

10.1007/978-3-319-31413-6_17

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Citations (3)

Motivating Serendipitous Encounters in Museum Recommendations

Studies in computational intelligence (2011)

Leo Iaquinta Marco de Gemmis Pasquale Lops Giovanni Semeraro Piero Molino

10.1007/978-3-642-21384-7_11

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Citations (0)