In this paper, we introduce EgoFine, a new approach for fine-tuning Large Language Models (LLMs) based on ego networks extracted from Knowledge Graphs (KGs). EgoFine first identifies the most informative nodes in the KG using degree centrality. It then extracts their ego networks and generates structured training data through random paths within them, thus enabling LLMs to learn domain-specific knowledge. It further constructs negative samples to explicitly model the absence of relationships between entities. We present an experimental campaign involving three KGs (PrimeKG, WN18RR, and YAGO3) and four LLMs (Minerva-350M, Llama3.2-1B, Qwen2-1.5B, and Ministral-3B). This campaign demonstrates that EgoFine outperforms traditional embedding-based methods (e.g., AutoSF, BoxE, NodePiece, PairRE, and TransE), two state-of-the-art approaches integrating KGs and LLMs (e.g., GNN-RAG and KG-Adapter), as well as a baseline approach operating on the same principle as EgoFine but without exploiting the contribution of ego networks. Compared with this last approach, EgoFine improves Hit@1 values up to 47.37%, Mean Reciprocal Rank (MRR) values by up to 46.87%, F1-Score values by up to 36.59%, and Accuracy values by up to 30.91%. The paper also presents an ablation study devoted to evaluating several design choices underlying EgoFine, as well as an analysis of the EgoFine’s behavior when applied on dynamic or noisy KGs. This way of proceeding makes EgoFine particularly beneficial for a variety of real-world applications, including understanding complex biological mechanisms, reasoning about the relationships between legislative sources and court cases, interpreting and explaining complex industrial maintenance and production processes, and understanding the connections between attacks, exploits, and countermeasures in the context of cybersecurity.

In this paper, we propose Automatic Intent Detector (AID), a framework for automatic intent detection to facilitate the creation of a conversational agent. AID follows an eight-step process incorporating best practices from the current literature and introducing innovative approaches in certain steps. The most notable innovation within AID is the automatic labeling of clusters, which is based on detailed and sophisticated rules derived from linguistics. These rules focus on morphosyntactic analysis, while also taking into account an aspect of semantic role theory. Furthermore, as for the overall validation of the results obtained, it provides an approach based on the concepts of semantic coherence, variability, and label appropriateness. After describing AID at the technical level, we illustrate the experiments we conducted both on a dataset widely used as benchmark in the literature and on a real corporate dataset. Finally, we present a critical discussion on the results obtained.

Social platforms are the preferred medium for many people to express their opinions on many topics. This has led many professionals from various fields (marketing, politics, research and development, etc.) to demand increasingly advanced approaches capable of analyzing the evolution of user or community sentiments on particular topics. In this paper, we want to make a contribution to addressing this issue. Specifically, we propose a model and a framework to analyze the dynamics of user and community sentiments in a social platform. In particular, our framework currently focuses on three activities, namely: (i) finding users capable of creating and maintaining a community that reflects their sentiment on a topic; (ii) studying how a user or community sentiment on a topic evolves over time; and (iii) investigating the cross-contamination between a user community and its neighborhood. We tested our framework by means of an extensive experimental campaign that we describe in the paper. Our framework is extremely scalable, and further activities can be easily implemented in it in the near future.

The Internet of Things (IoT) is currently considered the new frontier of the Internet. One of the most effective ways to investigate and implement IoT is based on the use of the social network paradigm. In the last years, social network researchers have introduced new models capable of capturing the growing complexity of this scenario. One of the most known of them is the Social Internetworking System, which models a scenario comprising several related social networks. In this paper, we investigate the possibility of applying the ideas characterizing the Social Internetworking System to IoT and we propose a new paradigm capable of modelling this scenario and of favoring the cooperation of objects belonging to different IoTs. Furthermore, in order to give an idea of both the potentialities and the complexity of this new paradigm, we illustrate in more detail one of the most interesting issues regarding it, namely the redefinition of the betweenness centrality measure.

Time is a useful dimension to explore in text databases especially when historical and factual information is concerned. As documents generally refer to different events and time periods, understanding the focus time of key sentences, defined as the time the content refers to, is a crucial task to temporally annotate a document. In this paper, we leverage a bag of linked entities representation of sentences and temporal information from Wikipedia and DBpedia to implement a novel approach to focus time estimation. We evaluate our approach on sample datasets and compare it with a state of the art method, measuring improvements in MRR.

In new generation social networks, we expect that the paradigm of Social Internetworking Systems (SISs, for short) will be more and more important. In this new scenario, the role of Social Network Analysis is of course still crucial but the preliminary step to do is designing a good way to crawl the underlying graph. While this aspect has been deeply investigated in the field of social networks, it is an open issue when moving towards SISs. Indeed, we cannot expect that a crawling strategy which is good for social networks, is still valid in a Social Internetworking Scenario, due to its specific topological features. In this paper, we first confirm the above claim and, then, define a new crawling strategy specifically conceived for SISs. Finally, we show that it fully overcomes the drawbacks of the state-of-the-art crawling strategies.

Social (or folksonomic) tagging has become a very popular way to describe content within Web 2.0 websites. However, as tags are informally defined, continually changing, and ungoverned, it has often been criticised for lowering, rather than increasing, the efficiency of searching. To address this issue, a variety of approaches have been proposed that recommend users what tags to use, both when labeling and when looking for resources. These techniques work well in dense folksonomies, but they fail to do so when tag usage exhibits a power law distribution, as it often happens in real-life folksonomies. To tackle this issue, we propose an approach that induces the creation of a dense folksonomy, in a fully automatic and transparent way: when users label resources, an innovative tag similarity metric is deployed, so to enrich the chosen tag set with related tags already present in the folksonomy. The proposed metric, which represents the core of our approach, is based on the mutual reinforcement principle. Our experimental evaluation proves that the accuracy and coverage of searches guaranteed by our metric are higher than those achieved by applying classical metrics.

In this paper, we present a multiagent system to support patients in search of healthcare services in an e-health scenario. The proposed system is HL7-aware in that it represents both patient and service information according to the directives of HL7, the information management standard adopted in medical context. Our system builds a profile for each patient and uses it to detect Healthcare Service Providers delivering e-health services potentially capable of satisfying his needs. In order to handle this search it can exploit three different algorithms: the first, called PPB, uses only information stored in the patient profile; the second, called DS-PPB, considers both information stored in the patient profile and similarities among the e-health services delivered by the involved providers; the third, called AB, relies on {\rm A}{\bf^*}, a popular search algorithm in Artificial Intelligence. Our system builds also a social network of patients; once a patient submits a query and retrieves a set of services relevant to him, our system applies a spreading activation technique on this social network to find other patients who may benefit from these services.

Social internetworking systems are a significantly emerging new reality; they group together a set of social networks and allow their users to share resources, to acquire opinions and, more in general, to interact, even if these users belong to different social networks and, therefore, did not previously know each other. In this context the notions of trust and reputation play a very relevant role. These notions have been widely studied in the past in several contexts whereas they have been largely neglected in the social internetworking research; however, since this application field presents several peculiarities, the results found in other application contexts are not automatically valid here. This paper introduces a model to represent and handle trust and reputation in a social internetworking system and proposes an approach that exploits these parameters to provide users with suggestions about the most reliable persons they can contact or social networks they can register to.

In this article, we propose an agent-based approach for managing e-commerce activities. In our approach, an agent is present in each e-commerce site, managing the information stored there. In addition, another agent is associated with each customer, handling his/her profile. The proposed approach is based on the use of a particular conceptual model called the Behaviour-Semantic Distance and Relevance (B-SDR) network, which is capable of uniformly representing and handling information stored in e-commerce sites and customer profiles. The capabilities of the B-SDR network model are exploited to let customer and site agents cooperate in such a way in order to support a customer in identifying, whenever he/she accesses an e-commerce site, those products and services present in the site itself and for better matching his/her interests. The approach has been implemented in a prototype in which its functionalities are discussed here also. © 2004 Wiley Periodicals, Inc.

The availability of automatic tools for inferring semantics of database schemes is useful to solve several database design problems such as that of obtaining cooperative information systems or data warehouses from large sets of data sources. In this context, a main problem is to single out similarities or dissimilarities among scheme objects (interscheme properties). This paper presents graph-based techniques for a uniform derivation of interscheme properties including synonymies, homonymies, type conflicts, and subscheme similarities. These techniques are characterized by a common core: the computation of maximum weight matchings on some bipartite weighted graphs derived using a suitable metrics to measure semantic closeness of objects. The techniques have been implemented in a system prototype. Several experiments conducted with it, and (in part) accounted for in the paper, confirmed the effectiveness of our approach.

In this paper we propose a multi-agent model for handling e-commerce activities. In our model, an agent is present in each e-commerce site, managing the information stored therein. In addition, another agent is associated with each customer handling her/his profile. The proposed model is based on the exploitation of a particular conceptual model, called the B-SDR network, capable of representing and handling both information stored in e-commerce sites and customer profiles. The capabilities of the B-SDR network model are exploited to let customer and site agents to cooperate in such a way to support a customer to detect, whenever she/he accesses an e-commerce site, those products and services present in the site itself and better matching her/his interests.

XML is rapidly becoming a standard for information exchange over the Web. Web providers and applications using XML for representing and exchanging their data make their information available in such a way that interoperability can be easily reached. However in order to guarantee both the exchange of XML documents and the interoperability between information providers, it is often needed to single out semantic similarity properties relating concepts of different XML documents. This paper gives a contribution to this framework by proposing a technique for extracting synonymies and homonymies. The derivation technique is based on a rich conceptual model (called SDR-Network) which is used to represent concepts expressed in XML documents as well as the semantic relationships holding among them.

Studies the interaction between the extraction of heterogeneous database inter-scheme properties, such as synonymies, homonymies and type conflicts (which are exploited for defining the semantics of the involved schemes for integration purposes), and the resolution of type conflicts. As a matter of fact, the transformations required by type conflict resolution could invalidate some of the detected inter-scheme properties. We propose a semi-automatic approach to face this problem, based on an iterative computation: each iteration of the computation derives inter-scheme properties, verifies if at least one type conflict exists and, in the affirmative case, modifies schemes for solving the derived type conflicts. We prove that the number of iterations required by the computation to terminate is polynomial in the number of objects belonging to the involved schemes.

Article Free Access Share on An automatic technique for detecting type conflicts in database schemes Authors: Luigi Palopoli Dipartimento di Elettronica, Informatica e Sistemistica, Università della Calabra, 87036 Rende (CS), Italy Dipartimento di Elettronica, Informatica e Sistemistica, Università della Calabra, 87036 Rende (CS), ItalyView Profile , Domenico Saccá View Profile , Domenico Ursino Dipartimento di Elettronica, Informatica e Sistemistica, Università della Calabra, 87036 Rende (CS), Italy Dipartimento di Elettronica, Informatica e Sistemistica, Università della Calabra, 87036 Rende (CS), ItalyView Profile Authors Info & Claims CIKM '98: Proceedings of the seventh international conference on Information and knowledge managementNovember 1998 Pages 306–313https://doi.org/10.1145/288627.288671Online:01 November 1998Publication History 22citation346DownloadsMetricsTotal Citations22Total Downloads346Last 12 Months9Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

An important tool for the integration of large federated database systems is a global dictionary describing all the involved schemes into an unified framework. The first step in the construction of such a dictionary is the discovery of the properties holding among objects in different schemes. This paper presents novel algorithms to discover possible synonyms, homonyms and inclusions. In addition, the paper also deals with another crucial step in the construction of a dictionary: schema integration. The approach proposed for this step exploits inter-schema properties discovered in the previous step to achieve schema integration. This approach is also concerned with producing suitable abstractions in order to structure the description of the global dictionary into a hierarchy of concepts in order to yield a more flexible, uniform view of the attached databases. The above two steps are interleaved with other steps (mainly devoted to interfacing with database administrators and to validating the discovered properties) and have been experimented with for the construction of a global dictionary for a large number of public administration database systems.