Personal knowledge assistance tools are designed to support knowledge work by delivering contextually relevant information and recommendations, thereby enhancing productivity and decision-making. Entity recommendation is a form of knowledge assistance that suggests relevant entities commonly sourced from public knowledge bases, like DBpedia, based on user context to improve productivity in daily digital tasks. In this study, we explore which similarity metrics within RDF2Vec knowledge graph embedding are most efective at capturing users' personal interpretations of entity similarities within their specific contexts. Accordingly, we propose a semantic-based recommendation method that includes an adaptive relevance prediction module to dynamically evaluate entity relevance by incorporating user feedback. Our approach is benchmarked on RLKWiC, a publicly available dataset of real-life knowledge work in context, and demonstrated a twenty percent improvement over the established baseline for entity recommendation, highlighting its potential to enhance knowledge work support.
Recommender systems (RS) have become a major technology in a wide range of applications, from
e-commerce and social media to digital entertainment [
KaRS extend beyond the conventional data-driven approaches by incorporating rich semantic
information from various knowledge sources, such as ontologies, Knowledge Graphs (KGs), and other
structured databases. These systems leverage this knowledge to provide more contextually relevant
recommendations, allowing them to address some of the limitations of traditional recommenders, such
as cold start problems and lack of explainability. By using knowledge bases and KGs, KaRS can infer new
relationships between items and users, capture deeper insights about user preferences, and understand
the semantics behind user interactions [
The integration of knowledge sources into RS represents a shift towards a more comprehensive
approach, where the goal is not only to predict what a user might like but also to provide
recommendations that are contextually appropriate and semantically meaningful. As such,
knowledge-aware and conversational RS are at the forefront of advancing the field by leveraging
the semantic richness of knowledge bases and the interactive nature of conversational AI, ultimately
enhancing user satisfaction and engagement [
Over the past two decades, a limited number of studies have investigated the integration of KaRS
with Personal Knowledge Assistance (PKA). This emerging application seeks to develop RS that can
continuously provide Knowledge Workers (KW) with the most relevant and useful information based on
their specific context, thereby enhancing productivity in their daily tasks [
In a relevant study, KG-based RS were identified as one of three promising types of RS capable
of addressing the complexities of this research goal [
While some studies have concentrated on exploring the personal and corporate information spaces of KW for recommendation, often framing it as proactive information delivery [9] or information re-finding [ 10], other research has focused on leveraging public knowledge bases for recommendations. This latter approach is commonly referred to as Entity Recommendation (ER), where entities typically refer to resources within public KGs, such as DBpedia1 [11, 12, 13].
The goal of our study is to explore how incorporating user feedback into ER systems can enhance their accuracy in suggesting relevant entities to KW, based on the users’ context. Specifically, we aim to investigate whether a semantic approach, which adapts recommendations by measuring the distance between entities in embedding space, can improve the relevance of these suggestions. This raises an additional research question: Are the representations of entities from public KGs, generated through common embedding methods, aligned with users’ personal interpretations of entity relevance within their self-defined contexts? Our study seeks to provide insights into these questions, assessing the potential of adaptive, feedback-driven systems in refining entity recommendations.
In the subsequent section, we discuss the evolution of ER towards PKA followed by an introduction to a publicly available benchmark designed for evaluating ER in PKA. We then present our proposed approach, which uses semantic similarities to dynamically predict the relevance of entities, thereby enhancing ER performance on the established benchmark. The paper is then concluded along with a brief overview of potential future research directions.
There has been a significant evolution of ER in the web search domain [ 14, 15] with extensive benchmarking using popular datasets [16] such as Movielens [17]. While PKA has not gained the same level of popularity as these domains, it has nonetheless seen notable contributions and advancements. This evolution reflects a shift from basic, application-specific models to sophisticated, context-aware systems that understand and anticipate user needs across various digital environments.
Initially, ER systems relied on limited user inputs and predefined logs, such as query histories or browsing data. For instance, the proactive information retrieval eforts focused on screen surveillance and the use of optical character recognition to analyze all content on a user’s screen, enhancing task detection accuracy and proactive retrieval through digital footprints [18, 19]. An earlier study explored ER application within email management, as demonstrated by the SmartOfice extension for Microsoft Outlook [20]. This tool integrated email processing with enterprise workflows, enhancing the eficiency of handling process-relevant emails and documents. Evaluation results highlighted SmartOfice’s potential to significantly improve workflow integration and user acceptance, showcasing the early promise of ER in supporting professional tasks through email.
As research progressed, there was a greater focus on user intentions, task goals, and the factors driving information search behaviors. Studies highlighted that search behaviors were often linked to creative processes triggered by prior digital activities, underscoring the need for contextual factors in RS design [21]. This led to the development of entity-based systems that deliver actionable recommendations across multiple applications by continuously monitoring digital activities and capturing context through screen frames. Such systems, exemplified by EntityBot, ofer recommendations without explicit queries, enhancing user productivity and satisfaction by reducing cognitive load [22, 12, 11].
Further advancements involved refining the methodologies for capturing and using contextual information. Techniques like Dirichlet–Hawkes processes were used to model context from digital traces, enhancing web search query augmentation based on comprehensive user activity data [23]. Additionally, integrating multimodal data from spoken conversations and digital activities improved task predictions and ER, thereby supporting users more efectively in digital environments [24].
Entity footprinting, which models contextual user states through digital monitoring and uses latent representations of entities to predict relevance, demonstrated improved accuracy in user state detection and entity prediction [25]. Most recently, research utilizing transformer architectures to model digital activity contexts has shown promise in predicting personalized information needs for various tasks, suggesting that a broader use of contextual data can enhance the efectiveness of RS [26].
The evolution of ER systems towards PKA has emphasized contextual awareness, multimodal data integration, and proactive support, reflecting a continuous efort to improve the relevance and utility of recommendations in meeting users’ information needs. However, despite the aforementioned valuable contributions, a significant barrier to advancing the use of RS for supporting KW has been the lack of a standardized framework for evaluating and benchmarking these methods [27]. Most of the conducted experiments rely on proprietary datasets that are, at best, partially available, leading to challenges in reproducing results and making fair comparisons across diferent approaches [ 28]. The RLKWiC Benchmarking Dataset for ER2, that is introduced in the following section, fills this gap by extending a publicly available dataset3 towards promoting transparency and comparability, enabling researchers to more efectively evaluate and contrast various methods [13].
RLKWiC is a publicly available dataset of Real-Life Knowledge Work in Context, gathered by monitoring computer interactions of 8 volunteers over 2 months. It aims to provide a standardized benchmark for evaluating PKA services by ofering multiple information dimensions, including detailed user contexts, documents, semantics, events, and sessions [28].
This dataset is extended to create a community benchmark by simulating an ER scenario where participants were given entities extracted from selected segments of their captured activities across various contexts [13]. In this setup, 1,850 entity recommendations were simulated across 56 diferent contexts within the dataset. After eliminating duplicates, these entities were grouped by context and presented to participants for explicit feedback.
To evaluate the relevance of recommendations in relation to their respective contexts, participants were instructed to rate the recommended entities on a 3-point scale: • Irrelevant (0): Indicates that there is no relevance between the recommended entity and the corresponding context. • Relevant (1): Suggests that there is a connection between the entity and the context, but it does not fully encapsulate or represent the context. • Representative (2): Denotes that the entity is closely aligned with the context, reflecting a high degree of relevance, such that the context can be inferred to be specifically about this entity.
It was noted that participants showed a preference for receiving information about entities labeled as representative in the corresponding context. For example, a participant who created the “GNN” context (a context about Graph Neural Networks), expressed interest in obtaining information about representative entities such as Message Passing4 and Graph Representation5 within this context. Conversely, participants generally found that explicit information about entities marked as relevant was less helpful in most scenarios. Nevertheless, these relevant entities can provide valuable, contextually rich information and enhance context representation learning for various information tasks. For example, in a job search context, the entity of Mannheim6, a city in Germany where a participant was looking for a job, was labeled as relevant. Although providing direct information about Mannheim may not seem necessary in this context, utilizing this data indirectly—such as filtering job recommendations to only include positions in Mannheim—could lead to more pertinent results.
Participants could also suggest additional entities they considered relevant but were not included in the original recommendations. Combining participant feedback with these additional suggestions resulted in a dataset of over a thousand entities, each labeled with explicit relevance scores, enhancing the RLKWiC dataset.
This ER benchmark dataset provides comprehensive details for each recommendation case, including timestamps and participant-assigned scores. To establish a baseline for future research, the performance of a simulated ER scenario in recommending relevant and representative entities is also reported. With this scored entity set for each context, the challenge is to develop a recommendation strategy that can be simulated on the RLKWiC dataset to maximize the number of relevant and representative entities while minimizing irrelevant ones [13].
In the following section, we demonstrate how incorporating an Adaptive Relevance Prediction (ARP) module into the simulated ER scenario has allowed us to outperform the established baseline.
In the previously simulated ER scenario on RLKWiC (illustrated in Figure 1), participants were provided with entities derived from selected portions of their captured activities across various contexts. 4http://dbpedia.org/resource/Message_passing 5http://dbpedia.org/resource/Graph_representation 6http://dbpedia.org/page/Mannheim
In this scenario, each recorded event was first classified as either context-informative or not by the ER system. Context-informative events, which have evidence of the user’s implicit confirmation of relevance include five types of activities: naming a newly defined context, making a search query, and adding a tag, ifle, or web page to the current context. Given the abundance of activities within each context, including considerable noise (e.g., distractions from irrelevant emails), only the context-informative events were considered as recommendation triggers. In the case of any of the mentioned triggering events, the corresponding content was extracted and pre-processed for the next step. This involves removing symbols and irrelevant strings (such as “https” in URLs) from the content. Finally, using the DBpedia Spotlight7 Named Entity Recognition (NER) tool, entities were extracted from the pre-processed text to be recommended to the user [13].
A sample recommendation process is shown by the red dashed rectangles in Figure 1. In this case, a recommendation was triggered when a web page was added to the GNN context. The URL and title of the web page were pre-processed and analyzed by the NER module, resulting in three entities being extracted for recommendation to the user [13].
In the original scenario, all entities recognized from the pre-processed text extracted from context-informative events were directly recommended to the user without further filtering (gray dashed arrow in Figure 1). In contrast, our proposed method introduces an ARP module (shown in green color in Figure 1) to refine the recommendation process by incorporating user feedback on previously recommended entities. The core idea behind ARP is to recommend only those entities that are semantically more closely aligned with the ones the user has already confirmed as relevant. By integrating user feedback into the ER process, we demonstrate a noticeable improvement in the system’s performance. The following subsections provide a detailed description of our proposed method: we ifrst explain how entities are semantically represented in our approach using RDF2Vec embeddings, then conduct a comparative analysis of diferent similarity metrics to determine the most efective one, followed by detailing our semantic similarity-based algorithm, which utilizes the selected metric to enhance ER on the RLKWiC dataset. Finally, we present the results to illustrate how our method outperforms the original recommendation strategy.
DBpedia, the foundational knowledge base used for representing entities in RLKWiC, is a prominent linked open data resource that structures its knowledge as a Resource Description Framework (RDF) graph. The graph-based nature of RDF makes it inherently complex and challenging to manage using traditional data mining and machine learning techniques. To address this challenge, RDF2Vec was introduced as a method for creating semantic representations of entities in RDF graphs by learning latent numerical vector embeddings [29]. RDF2Vec leverages language modeling techniques traditionally used for word embeddings to generate feature vectors from graph substructures, such as those obtained through Weisfeiler-Lehman Subtree RDF Graph Kernels [30, 31] and random graph walks. These embeddings efectively capture the semantics of entities in a way that is suitable for data mining and predictive modeling tasks [29].
In our study, we utilized the DBpedia RDF2Vec graph embeddings dataset8 [32] to semantically represent RLKWiC entities to enhance the ER accuracy by enabling the system to recommend entities based on their semantic proximity within the DBpedia KG.
Our method is based on the assumption that entities representing a user context, or at least those relevant to it, should be positioned closer to each other in the embedding space. Consequently, by ifltering out recognized entities that are not suficiently close to previously successful recommendations (entities scored as 1: relevant or 2: representative by the user), we can enhance recommendation precision. To evaluate this assumption, we analyzed the pairwise similarities between the embeddings of entities within each context. For an intuitive understanding, we began by visualizing the similarities.
While similarities based on certain metrics, such as Sigmoid, Linear, and Polynomial kernels, yielded poor results, others aligned well with our hypothesis.
Figure 2 illustrates the pairwise semantic similarity of entities within two sample contexts from RLKWiC using Euclidean distance. The heatmap on the left represents a context named “Vehicle Vibrations” which includes 17 evaluated entities: 10 representative (scored 2, shown in green font), 4 relevant (scored 1, in yellow), and 3 irrelevant (scored 0, in red). The heatmap demonstrates a high pairwise similarity among representative entities, with relevant entities being somewhat less close, and irrelevant ones further away.
An interesting case to note in this context is the entity “stroke”. Here, the entity stroke9 (medical condition) was incorrectly recognized and recommended within the “Vehicle Vibrations” context, whereas the correct entity to recommend was stroke in an engine10. The heatmap shows that the correct entity (stroke in engine) is closer to the appropriate entities compared to the incorrect one (stroke as a medical condition), highlighting the potential of semantic information for disambiguation and improving ER. The heatmap on the right depicts another context from a diferent participant named “Neuropsychology”, with more entities, supporting the same interpretation.
While these visualizations provided some intuitive confirmation of our assumption, they are not suficient for drawing definitive conclusions. Therefore, in the designed experiment presented in the next section, we assess our assumption more rigorously and identify which similarity metrics appear to be more efective in representing entity similarities in alignment with users’ assessments of relevance.
While there has been intriguing research on entity similarities within KG embedding spaces, such as conducting extensive experiments to evaluate the clustering capabilities of various KG embedding models to investigate how diferent models may capture distinct notions of similarity [ 33], the challenge of ensuring that these embeddings inherently reflect the user’s personal interpretation of entity similarity by positioning similar entities close to each other has not yet been explored. This research takes a step towards this goal by exploring entity similarity in the RDF2Vec DBpedia KG embedding space from the perspective of the user’s context. To achieve this, we utilized the scikit-learn library11, which provides functions for computing pairwise similarities and supports a variety of metrics including Euclidean, Cosine, and Manhattan distances, along with RBF, Laplacian, Sigmoid, Linear, and Polynomial kernels.
To evaluate our assumption and determine which similarity metrics are more efective in aligning with users’ assessments of entity relevance to their contexts, we applied a one-way ANOVA (Analysis of Variance) test. The one-way ANOVA is used to determine whether an independent variable has an efect on a dependent variable. In our case, the independent variable is the entity’s relevance to the context, which is categorized as 0 (irrelevant), 1 (relevant), or 2 (representative). The dependent variable is the average pairwise semantic similarity between all entities and representative entities within each context. The one-way ANOVA test produces an F-statistic, which is then used to calculate the p-value. The p-value helps us decide whether to reject or fail to reject the null hypothesis (0), which states that there is no statistically significant diference between the pairwise semantic similarity within the representative entities of a context compared to their similarity to other entities within the same context. Conversely, the alternative hypothesis (1) posits that representative entities are semantically closer to each other. We denote the average pairwise semantic similarities for each group by : = ⎨⎧ 1 ∑︁ ∑︁ sim(2, ) ⃒⃒⃒⃒ = 1, . . . , 56⎬⎫ , for = 0, 1, 2 ⎩ |2 | · | | 2∈2 ∈ ⃒ ⎭
Where ( ∈ {1, 2, 3}) denotes the set of average semantic similarities of entities scored with from representative entities (scored with 2) over the 56 existing contexts in RLKWiC. The notation 2 represents the set of entities scored with 2 in context ( = 1, . . . , 56), and represents the set of entities scored with (which can be 0, 1, or 2) in context . The function sim(2, ) denotes the semantic similarity between a representative entity 2 in 2 and an entity in , based on the similarity metric . The double summation ∑︀2∈2 ∑︀∈ sim(2, ) calculates the total sum of semantic similarities between all pairs of entities from sets 2 and , and dividing by |2 | · | | computes the average of these similarities over the number of pairwise comparisons.
Figure 3 presents the values of the three groups calculated using cosine similarity as an example, along with their respective average lines which aligns with our expectation that the similarities among representative entities will be the highest, followed by relevant entities, and then irrelevant ones.
Using the computed average similarity groups across contexts for each metric, we then calculated the F-statistic, which is the ratio of the mean square between groups to the mean square within groups (MST/MSE), to determine the p-values for testing our hypothesis. As shown in Table 1, most p-values are below 0.05, indicating a statistically significant diference between the pairwise semantic similarity among representative entities in a context and their similarity to other entities within the same context. aligning with users’ personal interpretations of entity similarities in relation to their contexts.
Based on the findings from our semantic analysis, we propose an algorithm aimed at enhancing entity recommendations on the RLKWiC dataset. In our approach, detailed in Algorithm 1, we categorize evaluated entities into relevant (scored 1 or 2) and irrelevant (scored 0) based on the explicit feedback provided in the RLKWiC benchmark dataset. For each newly recognized entity, we compute its semantic distance using the Laplacian kernel metric relative to both the relevant and irrelevant groups. The new entity is recommended only if it is closer to the relevant entities than to the irrelevant ones. We ordered the recognized entities within each context chronologically, simulating the availability of user feedback at each recommendation point, and then applied our recommendation method across the 56 contexts of the 8 participants in the RLKWiC dataset.
Algorithm 1: Adaptive relevance prediction via semantic similarity of RDF2Vec entity embeddings
Input: Participants , for each ∈ a set of contexts including a chronologically ordered set of entities , Participant’s explicit feedback on previous recommendations divided (RDF2Vec embeddings dataset consists of 200-dimensional vectors of DBpedia entities) into relevant entities and irrelevant entities , scaling parameter = 210.00 Output: Recommendation decision for all recognized entities in RLKWiC for each participant ∈ do for each context ∈ do for each entity ∈ do
Compute the average semantic similarity of with relevant entities in : Compute the average semantic similarity of with irrelevant entities in : sim ← sim ← else if sim > sim then
Recommend the entity Add to relevant entities Do not recommend the entity Add to irrelevant entities
1 ||
1 || ∑︀∈ exp − ‖RDF2Vec()− RDF2Vec()‖1 )︁ ︁(
∑︀∈ exp − ‖RDF2Vec()− RDF2Vec()‖1 )︁ ︁(
The step by step process of simulating the proposed ER method on RLKWiC is explained as follows: 4.3.1. Input Data: The algorithm works with data from multiple participants. For each participant, the algorithm processes a set of contexts, each of which includes a chronologically ordered set of entities. The participant’s explicit feedback is also provided, where entities are divided into two categories: • Relevant entities (scored 1 or 2), meaning the user found these entities useful.
• Irrelevant entities (scored 0), meaning the user did not find these entities useful.
The core data utilized for the similarity computation is a set of 200-dimensional RDF2Vec embeddings representing DBpedia entities. These embeddings capture semantic information about the entities. 4.3.2. Contextual Evaluation: The algorithm processes the entity data within each participant’s context. For each context, entities are evaluated one by one in chronological order, simulating a scenario where the system provides recommendations progressively, as more feedback becomes available. 4.3.3. Similarity Computation: For each entity, the algorithm calculates two separate semantic similarity scores: • Similarity to relevant entities: This measures how close the new entity is to entities previously marked as relevant within the current context. The similarity is computed using the Laplacian kernel, which measures the distance between the RDF2Vec embedding of the new entity and the embeddings of the relevant entities. The formula for the similarity is: sensitivity of the similarity function.
where ‖ · ‖ 1 denotes the L1 (Manhattan) distance, and is a scaling parameter that controls the • Similarity to irrelevant entities: This is similarly computed, but compares the new entity to those previously marked as irrelevant, using the same Laplacian kernel approach: 4.3.4. Recommendation Decision: Once both similarity scores are computed, the algorithm compares them: • If the similarity to relevant entities (sim ) is greater than the similarity to irrelevant entities (sim ), the new entity is recommended. This means the new entity is considered closer, in terms of semantic similarity, to the group of relevant entities.
• If the similarity to irrelevant entities is higher, the entity is not recommended. 4.3.5. Feedback Incorporation: After each decision (whether to recommend or not), the algorithm updates the relevant or irrelevant entity sets for that context by adding the newly evaluated entity to the corresponding set. This allows the recommendation process to evolve as more user feedback becomes available.
1 1 sim = || ∈
∑︁ exp − sim = || ∈ ∑︁ exp − ︂( ︂( ‖RDF2Vec() − RDF2Vec()‖1 ︂) ‖RDF2Vec() − RDF2Vec()‖1 ︂) 4.3.6. Repeat for All Participants and Contexts: The process is repeated for each participant and for each context within their data to generate a set of recommendation decisions for all recognized entities in the RLKWiC dataset. These recommendations are personalized for each participant and context, based on how semantically similar a new entity is to those previously marked as relevant or irrelevant.
In summary, this algorithm adapts to user preferences over time by learning from their explicit feedback and progressively improving recommendations based on the semantic similarities of entity embeddings. The use of the Laplacian kernel with RDF2Vec embeddings allows for the assessment of how closely related new entities are to those the user has previously interacted with, ultimately aiming to deliver more relevant recommendations.
In our evaluation, we focused exclusively on the set of entities recognized by the NER module within the scenario, excluding any additional entities manually added by users (Unlike the original evaluation [13]). Since the baseline method recommends all entities identified by the NER, it achieves the highest possible recall value (1.0). In contrast, our ARP-based method filters out entities that are not semantically related to the relevant ones, resulting in improved accuracy. The results presented in Table 2 provide a detailed comparison of the performance of the two methods. While ARP only provides a slight improvement in recommending relevant entities, it enhances the accuracy for representative entities by 20 percent.
Additionally, we trained several binary classification models, including Random Forest, Linear Regression, and Gaussian Naive Bayes, which are known to perform well with sparse datasets, to predict the relevance of recognized entities using their embeddings. However, these models did not demonstrate any significant improvements compared to our proposed ARP-based approach.
In this paper, we explored ER as a method to support knowledge work. We examined the evolution of ER tools towards PKA and introduced the RLKWiC benchmark, the only existing dataset that ensures full transparency and reproducibility for ER evaluation. Our approach outperformed the defined baseline by 20% in recommending representative entities by incorporating user feedback and simulating ER using an ARP module.
To predict the relevance of recognized entities in an adaptive manner, we measured the semantic distance between new entities and previously evaluated entities within each context. We utilized RDF2Vec embeddings to represent DBpedia entities in the RLKWiC dataset. Additionally, we tested our core hypothesis statistically, demonstrating the potential of semantic-based methods in enhancing ER. We found that among various similarity metrics, Laplacian kernel and Euclidean distance show more potential in maintaining the user’s context-based interpretation of entity closeness.
Furthermore, We highlighted that entities perceived as similar by a user may not align with their closeness in conventional knowledge representations. Consequently, methods that are efective in general knowledge tasks may not necessarily perform well in PKA, where a more personalized interpretation of world knowledge is required. Further exploration of novel approaches to computing semantic similarity of resources in DBpedia [34] could ofer valuable insights in this regard.
Despite the usefulness of the RLKWiC dataset for evaluating our approach, the study has some limitations. The small sample size, with only eight participants, restricts the generalizability of our ifndings, making it essential to explore the robustness of the results with a larger and more diverse user base in future work. Additionally, our study could benefit from a more comprehensive comparison with alternative ER approaches. Benchmarking our approach against state-of-the-art ER methods would provide deeper insights into its relative performance and areas for further improvement. Addressing these limitations in future research could strengthen the validity and applicability of our conclusions.
Further avenues could be pursued to further improve ER. One potential enhancement involves incorporating a disambiguation module into the system. This would allow the model to distinguish context-specific meanings of entities, such as recognizing that “stroke” in the context of “Vehicle Vibrations” refers to an engine component rather than a medical condition.
Our method focuses on enhancing the ER scenario following the NER phase. However, this enhancement could also be applied at an earlier stage in the ER process by leveraging novel methods, such as inflection-tolerant ontology-based NER for real-time applications [35], to further improve the efectiveness of ER.
Additionally, large language models are anticipated to excel in predicting entity relevance when given enough information about user contexts, as they have already demonstrated considerable success in various domains including RS[36].
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